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1. A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

Author

Camille Eché, Carole Iampietro, Clément Birbes, Andreea Dréau, Claire Kuchly, Arnaud Di Franco, Christophe Klopp, Thomas Faraut, Sarah Djebali, Adrien Castinel, Matthias Zytnicki, Erwan Denis, Mekki Boussaha, Cécile Grohs, Didier Boichard, Christine Gaspin, Denis Milan, and Cécile Donnadieu

Subjects
Science
Abstract

Abstract Inspired by the production of reference data sets in the Genome in a Bottle project, we sequenced one Charolais heifer with different technologies: Illumina paired-end, Oxford Nanopore, Pacific Biosciences (HiFi and CLR), 10X Genomics linked-reads, and Hi-C. In order to generate haplotypic assemblies, we also sequenced both parents with short reads. From these data, we built two haplotyped trio high quality reference genomes and a consensus assembly, using up-to-date software packages. The assemblies obtained using PacBio HiFi reaches a size of 3.2 Gb, which is significantly larger than the 2.7 Gb ARS-UCD1.2 reference. The BUSCO score of the consensus assembly reaches a completeness of 95.8%, among highly conserved mammal genes. We also identified 35,866 structural variants larger than 50 base pairs. This assembly is a contribution to the bovine pangenome for the “Charolais” breed. These datasets will prove to be useful resources enabling the community to gain additional insight on sequencing technologies for applications such as SNP, indel or structural variant calling, and de novo assembly.

Published
2023
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2. Enhancer/gene relationships: Need for more reliable genome-wide reference sets

Author

Tristan Hoellinger, Camille Mestre, Hugues Aschard, Wilfried Le Goff, Sylvain Foissac, Thomas Faraut, and Sarah Djebali

Subjects
gene expression regulation, identification of enhancer/gene relationships, method evaluation, chromatin structure, eQTL, functional genomic data, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Differences in cells’ functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.

Published
2023
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3. An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Author

Frédéric Jehl, Kévin Muret, Maria Bernard, Morgane Boutin, Laetitia Lagoutte, Colette Désert, Patrice Dehais, Diane Esquerré, Hervé Acloque, Elisabetta Giuffra, Sarah Djebali, Sylvain Foissac, Thomas Derrien, Frédérique Pitel, Tatiana Zerjal, Christophe Klopp, and Sandrine Lagarrigue

Subjects
Medicine, Science
Abstract

Abstract Long non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC ( http://www.fragencode.org/ ), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.

Published
2020
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4. Multi-species annotation of transcriptome and chromatin structure in domesticated animals

Author

Sylvain Foissac, Sarah Djebali, Kylie Munyard, Nathalie Vialaneix, Andrea Rau, Kevin Muret, Diane Esquerré, Matthias Zytnicki, Thomas Derrien, Philippe Bardou, Fany Blanc, Cédric Cabau, Elisa Crisci, Sophie Dhorne-Pollet, Françoise Drouet, Thomas Faraut, Ignacio Gonzalez, Adeline Goubil, Sonia Lacroix-Lamandé, Fabrice Laurent, Sylvain Marthey, Maria Marti-Marimon, Raphaelle Momal-Leisenring, Florence Mompart, Pascale Quéré, David Robelin, Magali San Cristobal, Gwenola Tosser-Klopp, Silvia Vincent-Naulleau, Stéphane Fabre, Marie-Hélène Pinard-Van der Laan, Christophe Klopp, Michèle Tixier-Boichard, Hervé Acloque, Sandrine Lagarrigue, and Elisabetta Giuffra

Subjects
Functional annotation, Livestock, RNA-seq, ATAC-seq, Hi-C, Biology (General), QH301-705.5
Abstract

Abstract Background Comparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells). Results RNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically associating domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species. Conclusions We report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.

Published
2019
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5. RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

Author

Frédéric Jehl, Fabien Degalez, Maria Bernard, Frédéric Lecerf, Laetitia Lagoutte, Colette Désert, Manon Coulée, Olivier Bouchez, Sophie Leroux, Behnam Abasht, Michèle Tixier-Boichard, Bertrand Bed’hom, Thierry Burlot, David Gourichon, Philippe Bardou, Hervé Acloque, Sylvain Foissac, Sarah Djebali, Elisabetta Giuffra, Tatiana Zerjal, Frédérique Pitel, Christophe Klopp, and Sandrine Lagarrigue

Subjects
RNA-seq, SNP calling, genotype calling, SNP annotation, allele-specific expression, livestock, Genetics, QH426-470
Abstract

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

Published
2021
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6. Author Correction: An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Author

Frédéric Jehl, Kévin Muret, Maria Bernard, Morgane Boutin, Laetitia Lagoutte, Colette Désert, Patrice Dehais, Diane Esquerré, Hervé Acloque, Elisabetta Giuffra, Sarah Djebali, Sylvain Foissac, Thomas Derrien, Frédérique Pitel, Tatiana Zerjal, Christophe Klopp, and Sandrine Lagarrigue

Subjects
Medicine, Science
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2021
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7. Evidence for transcript networks composed of chimeric RNAs in human cells.

Author

Sarah Djebali, Julien Lagarde, Philipp Kapranov, Vincent Lacroix, Christelle Borel, Jonathan M Mudge, Cédric Howald, Sylvain Foissac, Catherine Ucla, Jacqueline Chrast, Paolo Ribeca, David Martin, Ryan R Murray, Xinping Yang, Lila Ghamsari, Chenwei Lin, Ian Bell, Erica Dumais, Jorg Drenkow, Michael L Tress, Josep Lluís Gelpí, Modesto Orozco, Alfonso Valencia, Nynke L van Berkum, Bryan R Lajoie, Marc Vidal, John Stamatoyannopoulos, Philippe Batut, Alex Dobin, Jennifer Harrow, Tim Hubbard, Job Dekker, Adam Frankish, Kourosh Salehi-Ashtiani, Alexandre Reymond, Stylianos E Antonarakis, Roderic Guigó, and Thomas R Gingeras

Subjects
Medicine, Science
Abstract

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Published
2012
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8. Author Correction: An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Author

Morgane Boutin, Maria Bernard, Diane Esquerre, Sylvain Foissac, Kévin Muret, Thomas Derrien, Patrice Dehais, Sandrine Lagarrigue, Frédérique Pitel, Sarah Djebali, Laetitia Lagoutte, Hervé Acloque, Tatiana Zerjal, Elisabetta Giuffra, Christophe Klopp, Frédéric Jehl, and Colette Désert

Subjects
Multidisciplinary, Atlas (topology), Science, Medicine, Computational biology, Biology, Gene, Coding (social sciences)
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2021

9. Correlation Networks Provide New Insights into the Architecture of Testicular Steroid Pathways in Pigs

Author

Laurence Liaubet, Annie Robic, Thomas Faraut, Sarah Djebali, Catherine Larzul, Katia Feve, Armelle Prunier, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche en Santé Digestive (IRSD ), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and ANR-10-GENM-0015,UtOpIGe,Vers une Utilisation Optimale de l'Information Génomique dans les schémas pyramidaux(2010)

Subjects
Male, 0301 basic medicine, endocrine system, 17-Hydroxysteroid Dehydrogenases, lcsh:QH426-470, Swine, medicine.medical_treatment, androstenone, 030209 endocrinology & metabolism, Peptide hormone, Biology, HSD17B3, Article, Steroid, CYP11A1, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, AKR1C, Testis, [SDV.BA.ZV]Life Sciences [q-bio]/Animal biology/Vertebrate Zoology, Genetics, medicine, Animals, Gene Regulatory Networks, Cholesterol Side-Chain Cleavage Enzyme, Gene, steroids synthesis, Genetics (clinical), Testosterone, co-expression network, WGCNA, Cholesterol side-chain cleavage enzyme, [SDV.BA]Life Sciences [q-bio]/Animal biology, luteinizing hormone/choriogonadotropin receptor, porcine testis, Androstenone, Metabolism, Cell biology, lcsh:Genetics, 030104 developmental biology, chemistry, testosterone, Signal Transduction, estrogens
Abstract

Steroid metabolism is a fundamental process in the porcine testis to provide testosterone but also estrogens and androstenone, which are essential for the physiology of the boar. This study concerns boars at an early stage of puberty. Using a RT-qPCR approach, we showed that the transcriptional activities of several genes providing key enzymes involved in this metabolism (such as CYP11A1) are correlated. Surprisingly, HSD17B3, a key gene for testosterone production, was absent from this group. An additional weighted gene co-expression network analysis was performed on two large sets of mRNA-seq to identify co-expression modules. Of these modules, two containing either CYP11A1 or HSD17B3 were further analyzed. This comprehensive correlation meta-analysis identified a group of 85 genes with CYP11A1 as hub gene, but did not allow the characterization of a robust correlation network around HSD17B3. As the CYP11A1-group includes most of the genes involved in steroid synthesis pathways (including LHCGR encoding for the LH receptor), it may control the synthesis of most of the testicular steroids. The independent expression of HSD17B3 probably allows part of the production of testosterone to escape this control. This CYP11A1-group contained also INSL3 and AGT genes encoding a peptide hormone and an angiotensin peptide precursor, respectively.

Published
2021
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10. A limited set of transcriptional programs define major cell types

Author

Chris Zaleski, Ferran Reverter, Alexander Dobin, Roderic Guigó, Sarah Djebali, Cassidy Danyko, Michael Snyder, Valentin Wucher, Dmitri D. Pervouchine, Thomas R. Gingeras, Alexandra Scavelli, Diego Garrido-Martín, Manuel Muñoz-Aguirre, Jesse Gillis, Carrie A. Davis, Jorg Drenkow, Alessandra Breschi, Anna Vlasova, Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra [Barcelona] (UPF), Stanford University, Universitat Politècnica de Catalunya [Barcelona] (UPC), Cold Spring Harbor Laboratory (CSHL), Institut de Recherche en Santé Digestive (IRSD ), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Research Institute of Molecular Pathology (IMP), United States Department of Health & Human Services, National Institutes of Health (NIH) - USA (U54HG007004), NIH National Human Genome Research Institute (NHGRI) (U41HG007234), National Human Genome Research Institute of the National Institutes of Health (R01MH101814), Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013-2017 (SEV-20120208), Programa de Ayudas FPI del Ministerio de Economia y Competitividad(BES-2012-055848), Ministerio de Educacion, Cultura y Deporte, under the FPU programme (Formacion de Profesorado Universitario) (FPU15/03635), CERCA programme/Generalitat de Catalunya, 'la Caixa'-Severo Ochoa predoctoral fellowship (LCF/BQ/SO15/52260001), Common Fund of the Office of the Director of the National Institutes of Health, European Project: 294653,EC:FP7:ERC,ERC-2011-ADG_20110310,RNA-MAPS(2012), SEGUIN, Nathalie, Uncovering and understanding RNA through Massively Parallel Sequencing - RNA-MAPS - - EC:FP7:ERC2012-04-01 - 2017-03-31 - 294653 - VALID, Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Male, Resource, Cell type, Transcription, Genetic, Bioinformatics, Computational biology, Epithelial cells, Biology, [SDV.BBM.BM] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Cell Line, Transcriptome, Mesoderm, 03 medical and health sciences, Genètica mèdica, 0302 clinical medicine, Bioinformàtica, Neoplasms, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Gene expression, Genetics, Humans, Epigenetics, Primary cell, Gene, Genetics (clinical), 030304 developmental biology, 0303 health sciences, Cèl·lules epitelials, Sequence Analysis, RNA, Gene Expression Profiling, Mesenchymal stem cell, Medical genetics, RNA, Endothelial Cells, Histology, Epithelial Cells, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Phenotype, Cell biology, 3. Good health, Enginyeria química::Biotecnologia [Àrees temàtiques de la UPC], Cell culture, Organ Specificity, Gynecomastia, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, 030217 neurology & neurosurgery
Abstract

We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease. This project was supported by awards U54HG007004, U41HG007234, and R01MH101814 from the National Human Genome Research Institute of the National Institutes of Health, as well as from the Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013–2017, SEV-2012-0208, Programa de Ayudas FPI del Ministerio de Economía y Competitividad BES-2012-055848 to A.B., and Ministerio de Educación, Cultura y Deporte, under the FPU programme (Formación de Profesorado Universitario) with predoctoral fellowship FPU15/03635 to M.M.A., as well as the support of the CERCA programme/Generalitat de Catalunya. D.G.M. is supported by a “la Caixa”-Severo Ochoa predoctoral fellowship LCF/BQ/SO15/52260001. We also acknowledge support from the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 294653.

Published
2020

11. Multi-level conservation of chromosome conformation across livestock species reveals evolutionary links between genome structure and function

Author

Sylvain Foissac, Sarah Djebali Quelen, Nathalie Vialaneix, Matthias Zytnicki, Andrea Rau, Sandrine Lagarrigue, Hervé Acloque, Elisabetta Giuffra, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Génome et Transcriptome - Plateforme Génomique (GeT-PlaGe), Institut National de la Recherche Agronomique (INRA)-Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Projet GA FrAgencode, Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], [SDV.GEN] Life Sciences [q-bio]/Genetics, [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, [MATH] Mathematics [math], [INFO] Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; The spatial conformation of the chromatin in the nucleus of animal cells is highly organized at several levels. Little is known, however, about its evolution, mainly because of the limited number of species in which chromosome conformation experiments have been performed.In the context of the FAANG pilot project FR-AgENCODE, we realized Hi-C experiments on tissue samples from three livestock species (pig, goat and chicken livers) and integrated the resulting data with those from other assays on the same samples (RNA-seq and ATAC-seq). Within each species, general features of the genome organization -like Topologycally Associating Domains (TADs) and genome A/B compartments- could be identified. They display functional properties in line with previous reports on other model organisms like human and mouse. By comparing these structural features across species we further investigated the connections between genome conformation and function. In particular, we found a significant conservation of the gene distribution among active and inactive compartments of the chromatin throughout evolution. In addition, comparing 3D interaction profiles of orthologous regions showed that conserved TAD boundaries have stronger insulation properties than recent ones, supporting a specific selective pressure on structural components of the genome. Accordingly, various blocks of synteny contain conserved elements from several levels of organization that contribute to the gene regulatory network: sites of chromatin accessibility, orthologous genes, TADs and A/B compartments. Altogether, these results shed new light on the conservation of genome conformation from birds to mammals and illustrate the various degrees of connection between genome structure and function.

Published
2019

12. 32 Functional annotation of livestock genomes: chromatin structure and regulation of gene expression

Author

Sarah Djebali, Andrea Rau, Elisabetta Giuffra, Kylie Munyard, Sandrine Lagarrigue, Sylvain Foissac, Nathalie Vialaneix, and Hervé Acloque

Subjects
2. Zero hunger, Regulation of gene expression, 0303 health sciences, business.industry, 0402 animal and dairy science, 04 agricultural and veterinary sciences, General Medicine, Computational biology, Biology, 040201 dairy & animal science, Genome, Chromatin, 03 medical and health sciences, Functional annotation, Genetics, Oral Presentations, Animal Science and Zoology, Livestock, business, 030304 developmental biology, Food Science
Abstract

Improving the functional annotation of animal genomes is a key challenge in bridging the gap between genotype and phenotype, thus enabling predictive biology. Regarding livestock production, major outcomes are expected from a better understanding of the genetic architecture underlying quantitative traits. As part of the Functional Annotation of ANimal Genomes action (FAANG: www.faang.org), the FR-AgENCODE project generated omics data to improve the reference annotation of the cattle, pig, goat and chicken genome. High-throughput molecular assays have been performed on tissues/cells relevant to immune and metabolic traits. From two males and two females per species (pig, cattle, goat, chicken), strand-oriented RNA-seq gene expression and ATAC-seq chromatin accessibility assays were performed on liver and two PBMC-sorted T-cell types (CD4+ and CD8+). Chromosome Conformation Capture (in situ Hi-C) was also carried out on liver samples. About 4,000 samples have been collected at the INRA biorepository and registered at the EBI BioSamples registry. More than 80% of the planned experiments could be completed, generating ~11.5 billions of sequencing reads over the 3 assays. While most (50–80%) RNA-seq reads mapped to annotated exons, thousands of novel transcripts were found, with ~60K mRNAs and ~22K lncRNAs in cattle. Differentially expressed genes between cell types were enriched for immunity- or metabolism-related terms, and differentially accessible chromatin regions were identified as potential regulatory sites. Interestingly, correlations between gene expression and promoter accessibility across samples were skewed towards both positive and negative values, suggesting distinct regulatory mechanisms of gene expression. These patterns have been further investigated using human data from the Epigenome Roadmap Mapping Consortium. Altogether, this study illustrates the interest of a coordinated effort to tackle the genome-to-phenome challenge and provides a useful resource to the community. Availability: www.fragencode.org.

Published
2019

13. An atlas of chicken long non-coding RNAs gathering multiple sources : gene models and expression across more than twenty tissues

Author

Frédéric Jehl, Kévin Muret, Maria Bernard, Diane Esquerré, Hervé Acloque, Elisabetta Giuffra, Sarah Djebali, Sylvain Foissac, Thomas Derrien, Tatiana Zerjal, Christophe Klopp, Sandrine Lagarrigue, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), European Project: 633531,H2020,H2020-SFS-2014-2,Feed-a-Gene(2015), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Autre (Sciences du Vivant)
Abstract

International audience; Long non-coding RNAs (lncRNAs) appear as key regulators of gene expression through numerous mechanisms. Yet, particularly in domesticated species like chicken, their annotation is incomplete and their expression in different tissues poorly characterized. Here, we used data from 4 public databases (Ensembl, NONCODE, NCBI, ALDB), a recent lncRNA catalogue, and genes models from 364 RNA-seq samples from our lab to generate an extensive annotation of the chicken genome aggregating most of the currently known lncRNAs, plus robust models of newly discovered lncRNA from our dataset. Our extended lncRNAs annotation comprises 30084 lncRNA loci versus 4640 in the Ensembl reference. These lncRNAs follow the same trends as those in the reference for their genomic localization, structure and expression. To further characterize these lncRNAs, we studied their expression in over 20 chicken tissues with minimum 6 biological replicates. This allowed us to explore expression variability of both lncRNAs and protein-coding genes (mRNAs) across tissues, and across replicates in each tissue. Across these chicken tissues, we found interesting differences in the tissue-specificity patterns between lncRNAs and mRNAs. We characterized the expressions of both types of genes in relation with (i) their tissue-specificity and/or (ii) their genomic localization relative to each other (convergent, divergent, antisense, distant or proximal) in order to highlight some lncRNA–mRNA couples with a role in the functions or the maintenance of a given organ in chicken. Finally, we started a conservation study of interesting lncRNA–mRNA couples across species. Project funded by ANR-13-ADAP and H2020-Feed-a-Gene projects.

Published
2019

14. FR-Agencode: An Update

Author

Giuffra, E., Sarah Djebali, Andrea Rau, Sandrine Lagarrigue, Acloque, H., Sylvain Foissac, Giuffra, Elisabetta, Génétique Animale et Biologie Intégrative (GABI), Université Paris-Saclay-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

15. Chromatin accessibility conservation across four livestock species

Author

Sarah Djebali Quelen, Sylvain Foissac, Nathalie Vialaneix, Kylie Munyard, Andrea Rau, Thomas Faraut, Sandrine Lagarrigue, Hervé Acloque, Elisabetta Giuffra, ProdInra, Migration, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Curtin University [Perth], Planning and Transport Research Centre (PATREC), Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Projet GA FrAgencode

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, Bioinformatics, [SDV]Life Sciences [q-bio], Comparative genomics, ATAC-seq, [SDV.GEN] Life Sciences [q-bio]/Genetics, [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, [MATH] Mathematics [math], [INFO] Computer Science [cs], [SDV] Life Sciences [q-bio], [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, FAANG, Vertebrate livestock species, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; Within the FAANG consortium, the FR-AgENCODE pilot project has generated transcriptome (RNA-seq) and chromatin accessibility (ATAC-seq) data in the liver and immune cells of 2 males and 2 females of 4 vertebrate species (cattle, goat, chicken and pig), in addition to Hi-C data (see abstract #79896 by S. Foissac et al.). The first single-assay and integrative analyses performed on these data were mostly done on each species separately. Although these analyses provided several interesting insights (https://www.biorxiv.org/content/10.1101/316091v1), they did not take full advantage of our data richness, namely the availability of the same kind of functional data on species belonging to different parts of the phylogenetic tree. Here we have investigated the relationship between chromatin accessibility conservation across vertebrates and functionality. We first defined orthologous accessible regions by projecting accessible regions from our four species to the human genome. Two accessible regions are called orthologous if their projection on the human genome overlap. In doing so we identified 19,982, 7,877 and 1,083 regions shared between 2, 3 and 4 species, respectively. Our main results are: The level of accessibility of a region, measured by ATAC-seq, increases with the number of shared accessibility-orthologues Regions with more shared accessibility-orthologues are more likely to be close to gene promoters The functional conservation of an accessible region, measured by the number of species exhibiting a tissue-differential accessibility, is correlated to sequence conservation, measured by phastCons. This confirms and reinforces previous findings in human and mouse A hierarchical clustering of ATAC-seq samples based on the correlation between the accessibility of the 1083 orthologous regions shared between 4 species shows that clustering occurs first by species and then by tissue. This is different from what we found for RNA-seq (i.e. all liver samples clustering together). Based on these findings, we are currently working to define more precisely the functional regulatory relationships in liver and immune cells in light of their conservation during evolution. We anticipate that the comparison of these relationships to known QTLs should provide interesting results.

Published
2019

16. Analyse transcriptomique cellule-unique pour définir les populations cellulaires pluripotentes et extra-embryonnaires et leurs interactions dans le blastocyste porcin

Author

Hervé Acloque, Yoann Bailly, Sarah Djebali, Stéphane Ferchaud, Doryan Grivault, Patrick Manceau, claire kuchly, Frédéric Martins, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Génétique, Expérimentation et Système Innovants (GenESI), Institut National de la Recherche Agronomique (INRA), Génome et Transcriptome - Plateforme Génomique (GeT-PlaGe), Institut National de la Recherche Agronomique (INRA)-Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3)

Subjects
Animal biology, population cellulaire, blastocyste, Vertebrate Zoology, [SDV.BA]Life Sciences [q-bio]/Animal biology, Biologie animale, [SDV.BA.ZV]Life Sciences [q-bio]/Animal biology/Vertebrate Zoology, cellule unique, cellule pluripotente, Zoologie des vertébrés, ComputingMilieux_MISCELLANEOUS, étude transcriptomique, porc
Abstract

National audience

Published
2018

17. Transcriptome and chromatin structure annotation of liver, CD4+ and CD8+ T cells from four livestock species

Author

Maria Marti-Marimon, der Laan Mp, Elisa Crisci, Matthias Zytnicki, Hervé Acloque, Kylie Munyard, Florence Mompart, Thomas Faraut, Kévin Muret, Thomas Derrien, Nathalie Vialaneix, Magali San Cristobal, Gwenola Tosser-Klopp, Stéphane Fabre, Cédric Cabau, Raphaelle Momal-Leisenring, Sylvain Marthey, David Robelin, Michèle Tixier-Boichard, Sylvain Foissac, Sonia Lacroix-Lamandé, Ignacio González, Elisabetta Giuffra, Sarah Djebali, Françoise Drouet, Fabrice Laurent, Pascale Quéré, Fany Blanc, Philippe Bardou, Christophe Klopp, Sandrine Lagarrigue, Sophie Dhorne-Pollet, Andrea Rau, Silvia Vincent-Naulleau, Diane Esquerre, and Adeline Goubil

Subjects
2. Zero hunger, 0303 health sciences, Computational biology, Genome project, Biology, Genome, Chromatin, DNA binding site, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Synteny, Genomic organization
Abstract

BackgroundFunctional annotation of livestock genomes is a critical step to decipher the genotype-to-phenotype relationship underlying complex traits. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project (http://www.fragencode.org) aimed to profile the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in four livestock species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).ResultsRNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically Associating Domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.ConclusionsWe report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.

Published
2018
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18. Comparative analysis of neutrophil and monocyte epigenomes

Author

Rubio M, van Bommel A, Nilofar Sharifi, Alessandra Breschi, David G. Pisano, David J. Richardson, Paul Bertone, Marie-Laure Yaspo, Stephen B. Watt, Emanuele Raineri, de la Torre, Hinri Kerstens, Angelika Merkel, Pancaldi, Gelpí Jl, Emilio Palumbo, Martin Vingron, van der Ent Ma, Joost H.A. Martens, Royo R, Daniel R. Zerbino, Myrto Kostadima, Ian Dunham, Augusto Rendon, Laura Clarke, Sadia Saeed, Gut I, S. Farrow, Dirk S. Paul, Ali Aghajanirefah, Enrique Carrillo-de-Santa-Pau, Valencia A, Love Mi, Kuijpers T, Marc Sultan, Frances Burden, Anna Esteve-Codina, Nicola Foad, Jessica Quintin, Steven P. Wilder, Iotchkova, Remco Loos, Daniela C. Ifrim, Stephan Beck, Simon Heath, Henk Stunnenberg, Daniel Rico, Lu Chen, Roderic Guigó, Torrents D, Kim Berentsen, Gut M, Fernandez Jm, Abhishek Singh, Paul Flicek, Willem H. Ouwehand, Mihai G. Netea, Hans Lehrach, Thomas Lengauer, Kate Downes, Erber W, Christoph Bock, Stamatina Fragkogianni, Franz-Josef Müller, Sarah Djebali, Colin Logie, Anita Kaan, Eva M. Janssen-Megens, Mattia Frontini, Nicole Soranzo, and Antony P. Attwood

Subjects
0303 health sciences, Innate immune system, Cellular differentiation, Monocyte, Epigenome, Biology, 3. Good health, Cell biology, Chromatin, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, 030220 oncology & carcinogenesis, DNA methylation, medicine, Epigenetics, 030304 developmental biology, Epigenomics
Abstract

Neutrophils and monocytes provide a first line of defense against infections as part of the innate immune system. Here we report the integrated analysis of transcriptomic and epigenetic landscapes for circulating monocytes and neutrophils with the aim to enable downstream interpretation and functional validation of key regulatory elements in health and disease. We collected RNA-seq data, ChIP-seq of six histone modifications and of DNA methylation by bisulfite sequencing at base pair resolution from up to 6 individuals per cell type. Chromatin segmentation analyses suggested that monocytes have a higher number of cell-specific enhancer regions (4-fold) compared to neutrophils. This highly plastic epigenome is likely indicative of the greater differentiation potential of monocytes into macrophages, dendritic cells and osteoclasts. In contrast, most of the neutrophil-specific features tend to be characterized by repressed chromatin, reflective of their status as terminally differentiated cells. Enhancers were the regions where most of differences in DNA methylation between cells were observed, with monocyte-specific enhancers being generally hypomethylated. Monocytes show a substantially higher gene expression levels than neutrophils, in line with epigenomic analysis revealing that gene more active elements in monocytes. Our analyses suggest that the overexpression of c-Myc in monocytes and its binding to monocyte-specific enhancers could be an important contributor to these differences. Altogether, our study provides a comprehensive epigenetic chart of chromatin states in primary human neutrophils and monocytes, thus providing a valuable resource for studying the regulation of the human innate immune system.

Published
2017
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19. Long noncoding RNA repertoire in chicken liver and adipose tissue

Author

Sylvain Foissac, Morgane Boutin, Valentin Wucher, Frédéric Jehl, Colette Désert, Elisabetta Giuffra, Diane Esquerre, Frédéric Lecerf, Sandrine Lagarrigue, Christophe Klopp, Kévin Muret, Thomas Derrien, Sarah Djebali, Fabrice Legeai, Hervé Acloque, BMC, BMC, Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems - Feed-a-Gene - - H20202015-03-01 - 2020-02-29 - 633531 - VALID, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Système d'Information des GENomes des Animaux d'Elevage (SIGENAE), Institut National de la Recherche Agronomique (INRA), Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT], Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, IN-1275570, INRA, European Project: 633531,H2020,H2020-SFS-2014-2,Feed-a-Gene(2015), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), and AgroParisTech-Institut National de la Recherche Agronomique (INRA)

Subjects
0301 basic medicine, Genotype, [SDV]Life Sciences [q-bio], Quantitative Trait Loci, poulet, génomique fonctionnelle, tissu adipeux, Adipose tissue, Biology, Genome, Evolution, Molecular, Transcriptome, Open Reading Frames, analyse de génome, 03 medical and health sciences, Genetics, Animals, Humans, RNA, Antisense, Genetics(clinical), RNA, Messenger, Gene, Conserved Sequence, Ecology, Evolution, Behavior and Systematics, Synteny, 030102 biochemistry & molecular biology, Gene Expression Profiling, General Medicine, Lipid Metabolism, foie, Phenotype, Long non-coding RNA, [SDV] Life Sciences [q-bio], Gene expression profiling, 030104 developmental biology, Adipose Tissue, Gene Expression Regulation, Liver, Organ Specificity, RNA, Long Noncoding, Animal Science and Zoology, Chickens, annotation fonctionnelle, Research Article
Abstract

Background Improving functional annotation of the chicken genome is a key challenge in bridging the gap between genotype and phenotype. Among all transcribed regions, long noncoding RNAs (lncRNAs) are a major component of the transcriptome and its regulation, and whole-transcriptome sequencing (RNA-Seq) has greatly improved their identification and characterization. We performed an extensive profiling of the lncRNA transcriptome in the chicken liver and adipose tissue by RNA-Seq. We focused on these two tissues because of their importance in various economical traits for which energy storage and mobilization play key roles and also because of their high cell homogeneity. To predict lncRNAs, we used a recently developed tool called FEELnc, which also classifies them with respect to their distance and strand orientation to the closest protein-coding genes. Moreover, to confidently identify the genes/transcripts expressed in each tissue (a complex task for weakly expressed molecules such as lncRNAs), we probed a particularly large number of biological replicates (16 per tissue) compared to common multi-tissue studies with a larger set of tissues but less sampling. Results We predicted 2193 lncRNA genes, among which 1670 were robustly expressed across replicates in the liver and/or adipose tissue and which were classified into 1493 intergenic and 177 intragenic lncRNAs located between and within protein-coding genes, respectively. We observed similar structural features between chickens and mammals, with strong synteny conservation but without sequence conservation. As previously reported, we confirm that lncRNAs have a lower and more tissue-specific expression than mRNAs. Finally, we showed that adjacent lncRNA-mRNA genes in divergent orientation have a higher co-expression level when separated by less than 1 kb compared to more distant divergent pairs. Among these, we highlighted for the first time a novel lncRNA candidate involved in lipid metabolism, lnc_DHCR24, which is highly correlated with the DHCR24 gene that encodes a key enzyme of cholesterol biosynthesis. Conclusions We provide a comprehensive lncRNA repertoire in the chicken liver and adipose tissue, which shows interesting patterns of co-expression between mRNAs and lncRNAs. It contributes to improving the structural and functional annotation of the chicken genome and provides a basis for further studies on energy storage and mobilization traits in the chicken. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0275-0) contains supplementary material, which is available to authorized users.

Published
2017

20. Profiling the landscape of transcription and chromatin conformation reveals regulatory mechanisms involved in immune and metabolic functions

Author

Sylvain Foissac, Sarah Djebali Quelen, Hervé Acloque, Marie-Helene Pinard - van der Laan, Sandrine Lagarrigue, Elisabetta Giuffra, ProdInra, Archive Ouverte, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), FAANG pilot project 'FR-AgENCODE', AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]

Subjects
pig, [SDV]Life Sciences [q-bio], chicken, génome, goat, animal de rente, T cells, functional annotation, liver, cash cattle, [SDV] Life Sciences [q-bio], cattle, chromatin, annotation fonctionnelle, chromatine
Abstract

Functional annotation of livestock genomes is a critical and obvious next step to derive maximum benefit for agriculture, animal science, animal welfare and human health. The aim of the Fr-AgENCODE project is to generate multi-species functional genome annotations by applying high-throughput molecular assays on three target tissues/cells relevant to the study of immune and metabolic traits. Here we present on-going results on gene expression and chromatin conformation in hepatocytes and CD3+CD4+ (“CD4”) and CD3+CD8+ (“CD8”) T cells with a focus on the pig species (two male and two female Large White adult pigs). These samples were processed by strand-oriented RNA-seq and ATAC-seq experiments. Principal Component Analyses on log-transformed TMM-normalized read counts in genes (from RNA-seq) and regions of chromatin accessibility (from ATAC-seq) consistently highlighted the variability between liver and the T cells, and to a lesser extent within T cells (CD4 vs. CD8), as well as between the male and female samples. Comparative analyses identified differentially expressed genes between cell types as well as potential regulatory sites from differentially accessible chromatin regions. As expected, ontology annotations of differentially expressed genes were enriched for either immunity- or metabolism-related terms. Interestingly, correlations between gene expression and promoter accessibility across samples were enriched for both extreme positive and negative values, which suggests that ATAC-seq can efficiently capture distinct regulatory mechanisms of gene expression. Candidate enhancers and repressors were identified by comparing ATAC-seq regions with predicted binding sites of 500+ transcription factors. By integrating these results with those from Hi-C chromosome conformation capture on the liver samples, we investigated differences between “active” and “repressed” topological domains in terms of functional features, including gene density and general chromatin accessibility. Altogether, these results lead to a better understanding of the molecular mechanisms involved in pig immune and metabolic functions, and illustrate a useful contribution to the functional annotation effort of the FAANG initiative.

Published
2017

21. Bioinformatics Pipeline for Transcriptome Sequencing Analysis

Author

Thomas Derrien, Sylvain Foissac, Sarah Djebali, Valentin Wucher, Christophe Hitte, Erwan Corre, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut de Génétique et Développement de Rennes (IGDR), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT), and Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )

Subjects
0301 basic medicine, reconstruction, [SDV]Life Sciences [q-bio], Population, RNA-Seq, Biology, transcriptome sequencing, Bioinformatics, DNA sequencing, Transcriptome, 03 medical and health sciences, expression, read, [INFO]Computer Science [cs], protocol, education, Illumina dye sequencing, gencode, ComputingMilieux_MISCELLANEOUS, education.field_of_study, [SDV.GEN]Life Sciences [q-bio]/Genetics, rna-seq data, Gene expression profiling, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, 030104 developmental biology, rna-seq, Reference genome, De facto standard, bioinformatic workflow
Abstract

International audience; The development of High Throughput Sequencing (HTS) for RNA profiling (RNA-seq) has shed light on the diversity of transcriptomes. While RNA-seq is becoming a de facto standard for monitoring the population of expressed transcripts in a given condition at a specific time, processing the huge amount of data it generates requires dedicated bioinformatics programs. Here, we describe a standard bioinformatics protocol using state-of-the-art tools, the STAR mapper to align reads onto a reference genome, Cufflinks to reconstruct the transcriptome, and RSEM to quantify expression levels of genes and transcripts. We present the workflow using human transcriptome sequencing data from two biological replicates of the K562 cell line produced as part of the ENCODE3 project.

Published
2017

22. An atlas of human long non-coding RNAs with accurate 5′ ends

Author

Yukio Nakamura, Mickal Mendez, Julian Gough, Jessica Severin, Owen J. L. Rackham, Dave Tang, A. Maxwell Burroughs, Masayoshi Itoh, Timo Lassmann, Carsten O. Daub, Leonard Lipovich, Erik Arner, Yoshihide Hayashizaki, Sarah Djebali, Soichi Kojima, Magda Babina, Shohei Noma, Piero Carninci, Hideya Kawaji, Sebastian Schmeier, Peter Heutink, Yulia A. Medvedeva, Tanvir Alam, Alison C. Testa, Elena Denisenko, Marina Lizio, Christine A. Wells, Chi Wai Yip, Akira Hasegawa, Jordan A. Ramilowski, Alistair R. R. Forrest, Michiel J. L. de Hoon, Harukazu Suzuki, Takeya Kasukawa, Nicolas Bertin, Thomas M. Poulsen, Imad Abugessaisa, Jayson Harshbarger, Chung-Chau Hon, Ctr Life Sci Technol, Div Gen Technol, Tsurumi Ku, RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Omics Sci Ctr, Tsurumi Ku, Ctr Translat Med, Canc Sci Inst Singapore, National University of Singapore (NUS), Human Longev Singapore Pte Ltd, Dept Comp Sci, Life Sci, University of Bristol [Bristol], Cardiovasc & Metab Disorders, Duke-NUS Medical School [Singapore], Inst Nat & Math Sci, Biotechnol Res Inst Drug Discovery BRD, Natl Inst Adv Ind Sci & Technol, Prevent Med & Diag Innovat Program, Natl Lib Med, Natl Ctr Biotechnol Informat, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Ctr Gen Regulat CRG, Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra [Barcelona] (UPF), Computat Biosci Res Ctr, Comp Elect & Math Sci & Engn Div, King Abdullah University of Science and Technology (KAUST), Biotechnol Res Ctr, Inst Bioengn, the Russian Academy of Sciences [Moscow, Russia] (RAS), Vavilov Inst Gen Genet, Russian Academy of Sciences [Moscow] (RAS), QEII Med Ctr, Harry Perkins Inst Med Res, The University of Western Australia (UWA), Med Res Ctr, Ctr Mol Med & Genet, Wayne State University [Detroit], Sch Med, Dept Neurol, Dept Comp Sci, North Dakota State University (NDSU), Telethon Kids Inst, German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Dept Dermatol & Allergy, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Australian Inst Bioengn & Nanotechno, University of Queensland [Brisbane], Fac Med, Dept Anat & Neurosci, University of Melbourne, CLST Div Biofunct Dynam Imaging, BioResource Ctr, Cell Engn Div, Université de Tsukuba = University of Tsukuba, Dept Biosci & Nutr, Karolinska Institute, QEII Med Ctr, Harry Perkins Inst Med Re, Duke NUS Medical School, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT], Universitat Pompeu Fabra [Barcelona], Russian Academy of Sciences, Wayne State University, Charité - Universitätsmedizin Berlin / Charite - University Medicine Berlin, and University of Tsukuba

Subjects
0301 basic medicine, RNA Stability, [SDV]Life Sciences [q-bio], genetics [Transcriptome], Datasets as Topic, genetics [RNA, Long Noncoding], Genome, Epigenesis, Genetic, 0302 clinical medicine, Databases, Genetic, Promoter Regions, Genetic, Cells, Cultured, Conserved Sequence, Genetics, Regulation of gene expression, Multidisciplinary, genetics [Organ Specificity], Genomics, Enhancer Elements, Genetic, annotation, Organ Specificity, 030220 oncology & carcinogenesis, RNA, Long Noncoding, ddc:500, pr interval, transcription, architecture, Quantitative Trait Loci, Biology, Polymorphism, Single Nucleotide, Article, genetics [RNA, Messenger], 03 medical and health sciences, seq, evolution, genetics [Conserved Sequence], Humans, genetics [Genome, Human], RNA, Messenger, Gene, Internet, genetics [Quantitative Trait Loci], Genome, Human, Gene Expression Profiling, genetics [Enhancer Elements, Genetic], Molecular Sequence Annotation, landscape, gene-expression, Cap analysis gene expression, promoter usage, Gene expression profiling, chemistry [RNA, Long Noncoding], 030104 developmental biology, Gene Expression Regulation, genetics [Promoter Regions, Genetic], Expression quantitative trait loci, genome-wide association, Human genome, Transcriptome, Genome-Wide Association Study
Abstract

Long non-coding RNAs (lncRNAs) are largely heterogeneous and functionally uncharacterized. Here, using FANTOM5 cap analysis of gene expression (CAGE) data, we integrate multiple transcript collections to generate a comprehensive atlas of 27,919 human lncRNA genes with high-confidence 5' ends and expression profiles across 1,829 samples from the major human primary cell types and tissues. Genomic and epigenomic classification of these lncRNAs reveals that most intergenic lncRNAs originate from enhancers rather than from promoters. Incorporating genetic and expression data, we show that lncRNAs overlapping trait-associated single nucleotide polymorphisms are specifically expressed in cell types relevant to the traits, implicating these lncRNAs in multiple diseases. We further demonstrate that lncRNAs overlapping expression quantitative trait loci (eQTL)-associated single nucleotide polymorphisms of messenger RNAs are co-expressed with the corresponding messenger RNAs, suggesting their potential roles in transcriptional regulation. Combining these findings with conservation data, we identify 19,175 potentially functional lncRNAs in the human genome.

Published
2017

23. Comparative Analysis of the Transcriptome across Distant Species

Author

Gang Fang, LaDeana W. Hillier, Brenton R. Graveley, Ali Mortazavi, Norbert Perrimon, Nathan Boley, Jingyi Jessica Li, William C. Spencer, James B. Brown, Chau Huynh, Roger A. Hoskins, Mark Gerstein, Ann S. Hammonds, Sarah Djebali, Sonali Jha, Kenneth H. Wan, Cédric Howald, Raymond K. Auerbach, Chenghai Xue, Haiyan Huang, Jorg Drenkow, Elise A. Feingold, Julien Lagarde, Daifeng Wang, Dmitri D. Pervouchine, Thomas R. Gingeras, Guilin Wang, Peter Cherbas, Brent Ewing, Chao Di, Gary Saunders, Benjamin W. Booth, Joel Rozowsky, Yan Zhang, Anastasia Samsonova, Dionna M. Kasper, Cristina Sisu, Marcus H. Stoiber, Jiayu Wen, Michael O. Duff, Felix Schlesinger, Gennifer E. Merrihew, Sara Olson, Susan E. Celniker, Burak H. Alver, Chao Cheng, Gemma E. May, Alexandre Reymond, Carrie A. Davis, Alexander Dobin, Max E. Boeck, Roger P. Alexander, Michael J. Pazin, Peter J. Park, Adam Frankish, Lucy Cherbas, Zhi Lu, Kevin Y. Yip, Henry Zheng, Owen Thompson, Jing Leng, Kathie L. Watkins, Andrea Tanzer, Valerie Reinke, Rebecca McWhirter, Eric C. Lai, Steven E. Brenner, Robert H. Waterston, Koon-Kiu Yan, Masaomi Kato, Roderic Guigó, Huaien Wang, Kimberly Bell, Pnina Strasbourger, Baikang Pei, Jen Harrow, Long Hu, Chris Zaleski, Rabi Murad, Thomas C. Kaufman, Erik Ladewig, Robert R. Kitchen, Anurag Sethi, Kejia Wen, Guanjun Gao, Arif Harmanci, Megan Fastuca, Brian Oliver, Frank J. Slack, David M. Miller, Tim Hubbard, Garrett Robinson, Peter J. Good, Peter J. Bickel, Michael J. MacCoss, and Li Yang

Subjects
RNA, Untranslated, Caenorhabditis elegans -- Embriologia, Genome, Transcriptome, Histones, 0302 clinical medicine, Models, Cluster Analysis, Developmental, Drosòfila -- Genètica, Promoter Regions, Genetic, Genetics, 0303 health sciences, Multidisciplinary, biology, Pupa, Untranslated, Gene Expression Regulation, Developmental, Chromatin, Molecular Sequence Annotation, Drosophila melanogaster, Larva, Sequence Analysis, Biotechnology, animal structures, General Science & Technology, Computational biology, ENCODE, Article, Promoter Regions, 03 medical and health sciences, Genetic, Regulació genètica, Animals, Humans, Transcriptomics, Caenorhabditis elegans, 030304 developmental biology, Comparative genomics, Models, Genetic, Phylum, Sequence Analysis, RNA, Gene Expression Profiling, fungi, Human Genome, biology.organism_classification, Gene expression profiling, Gene Expression Regulation, RNA, Generic health relevance, 030217 neurology & neurosurgery
Abstract

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features1, 2, 3, 4, 5, 6. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters. In particular, this work was funded by a contract from the National Human Genome Research Institute modENCODE Project, contract U01 HG004271 and U54 HG006944, to S.E.C. (principal investigator) and P.C., T.R.G., R.A.H. and B.R.G. (co-principal investigators) with additional support from R01 GM076655 (S.E.C.) both under Department of Energy contract no. DE-AC02-05CH11231, and U54 HG007005 to B.R.G. J.B.B.’s work was supported by NHGRI K99 HG006698 and DOE DE-AC02-05CH11231. Work in P.J.B.’s group was supported by the modENCODE DAC sub award 5710003102, 1U01HG007031-01 and the ENCODE DAC 5U01HG004695-04. Work in M.B.G.’s group was supported by NIH grants HG007000 and HG007355. Work in Bloomington was supported in part by the Indiana METACyt Initiative of Indiana University, funded by an award from the Lilly Endowment, Inc. Work in E.C.L.’s group was supported by U01-HG004261 and RC2-HG005639. P.J.P. acknowledges support from the National Institutes of Health (grant no. U01HG004258). We thank the HAVANA team for providing annotation of the human reference genome, whose work is supported by National Institutes of Health (grant no. 5U54HG004555), the Wellcome Trust (grant no. WT098051). R.G. acknowledges support from the Spanish Ministry of Education (grant BIO2011-26205). We also acknowledge use of the Yale University Biomedical High Performance Computing Center. R.W.'s lab was supported by grant no. U01 HG 004263.

Published
2014

26. Bioinformatics Pipeline for Transcriptome Sequencing Analysis

Author

Sarah, Djebali, Valentin, Wucher, Sylvain, Foissac, Christophe, Hitte, Erwan, Corre, and Thomas, Derrien

Subjects
Sequence Analysis, RNA, Gene Expression Profiling, Computational Biology, High-Throughput Nucleotide Sequencing, Humans, K562 Cells, Workflow
Abstract

The development of High Throughput Sequencing (HTS) for RNA profiling (RNA-seq) has shed light on the diversity of transcriptomes. While RNA-seq is becoming a de facto standard for monitoring the population of expressed transcripts in a given condition at a specific time, processing the huge amount of data it generates requires dedicated bioinformatics programs. Here, we describe a standard bioinformatics protocol using state-of-the-art tools, the STAR mapper to align reads onto a reference genome, Cufflinks to reconstruct the transcriptome, and RSEM to quantify expression levels of genes and transcripts. We present the workflow using human transcriptome sequencing data from two biological replicates of the K562 cell line produced as part of the ENCODE3 project.

Published
2016

27. Erratum to: A benchmark for RNA-seq quantification pipelines

Author

Dmitri D. Pervouchine, Sarah Djebali, Mingxiang Teng, Lijun Zhan, Alexander Dobin, Xintao Wei, Carrie A. Davis, Sheng Li, Cricket A. Sloan, Sara Olson, Brenton R. Graveley, Rafael A. Irizarry, Michael I. Love, and Christopher E. Mason

Subjects
0301 basic medicine, Method, RNA-Seq, Computational biology, Biology, Bioinformatics, Genome, Column (database), Pipeline transport, 03 medical and health sciences, 030104 developmental biology, Benchmark (computing), Applied mathematics, Expressió gènica -- Mètodes, Erratum, Typographical error
Abstract

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users.

Published
2016

28. A benchmark for RNA-seq quantification pipelines

Author

Brenton R. Graveley, Alexander Dobin, Sarah Djebali, Cricket A. Sloan, Mingxiang Teng, Rafael A. Irizarry, Xintao Wei, Sara Olson, Dmitri D. Pervouchine, Sheng Li, Michael I. Love, Carrie A. Davis, Christopher E. Mason, and Lijun Zhan

Subjects
0301 basic medicine, Complex data type, Sequence Analysis, RNA, RNA-Seq, Biology, Bioinformatics, computer.software_genre, Sensitivity and Specificity, Bioconductor, Pipeline transport, 03 medical and health sciences, 030104 developmental biology, Reference Values, Reference values, Computer software, Benchmark (computing), Animals, Humans, Data mining, Sensitivity (control systems), Expressió gènica -- Mètodes, Erratum, computer, Algorithms
Abstract

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( http://bioconductor.org/packages/rnaseqcomp ). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.

Published
2016

29. Gene-specific patterns of expression variation across organs and species

Author

Thomas R. Gingeras, Dmitri D. Pervouchine, Roderic Guigó, Alexander Dobin, Alessandra Breschi, Sarah Djebali, Jesse Gillis, Carrie A. Davis, Centro de Regulación Genómica (CRG), Universitat Pompeu Fabra [Barcelona] (UPF), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Cold Spring Harbor Laboratory (CSHL)

Subjects
0301 basic medicine, [SDV]Life Sciences [q-bio], RNA-Seq, Biology, specie, Clustering, Transcriptome, 03 medical and health sciences, Comparative transcriptomics, rna-seq, comparative transcriptomic, gene expression, clustering, vertebrate, organ, biology.animal, [INFO]Computer Science [cs], Gene, Organism, Genetics, Species, [SDV.GEN]Life Sciences [q-bio]/Genetics, Vertebrate, Human genetics, Gene expression profiling, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, 030104 developmental biology, Variation (linguistics), Organ, Gene expression, RNA-seq
Abstract

Background: A comparison of transcriptional profiles derived from different tissues in a given species or among different species assumes that commonalities reflect evolutionarily conserved programs and that differences reflect species or tissue responses to environmental conditions or developmental program staging. Apparently conflicting results have been published regarding whether organ-specific transcriptional patterns dominate over species-specific patterns, or vice versa, making it unclear to what extent the biology of a given organism can be extrapolated to another. These studies have in common that they treat the transcriptomes monolithically, implicitly ignoring that each gene is likely to have a specific pattern of transcriptional variation across organs and species. Results: We use linear models to quantify this pattern. We find a continuum in the spectrum of expression variation: the expression of some genes varies considerably across species and little across organs, and simply reflects evolutionary distance. At the other extreme are genes whose expression varies considerably across organs and little across species; these genes are much more likely to be associated with diseases than are genes whose expression varies predominantly across species. Conclusions: Whether transcriptomes, when considered globally, cluster preferentially according to one component or the other may not be a property of the transcriptomes, but rather a consequence of the dominant behavior of a subset of genes. Therefore, the values of the components of the variance of expression for each gene could become a useful resource when planning, interpreting, and extrapolating experimental data from mouse to humans. This project was supported by awards U54HG007004 and U41HG007234 from the National Human Genome Research Institute of the National Institutes of Health, as well as from the Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013–2017, SEV-2012-0208, and Programa de Ayudas FPI del Ministerio de Economia y Competitividad, BES-2012-055848. We would also like to acknowledge support from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 294653.

Published
2016

30. The human transcriptome across tissues and individuals

Author

Dmitri D. Pervouchine, Michael Sammeth, Pedro G. Ferreira, Ferran Reverter, Gad Getz, Timothy J. Sullivan, Miquel Calvo, Jakob M. Goldmann, Ayellet V. Segrè, Rory Johnson, Kristin G. Ardlie, Tuuli Lappalainen, Fred A. Wright, Emmanouil T. Dermitzakis, Anastasia Niarchou, Sarah Djebali, David S. DeLuca, Jean Monlong, Marta Melé, Roderic Guigó, and Taylor Young

Subjects
Gene expression profiling, Transcriptome, Regulation of gene expression, Genetics, Multidisciplinary, Alternative splicing, RNA splicing, Transcriptional regulation, Human genome, Biology, Gene, Article
Abstract

Expression, genetic variation, and tissues Human genomes show extensive genetic variation across individuals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within individuals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem samples covering 54 body sites from 175 individuals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science , this issue p. 648 , p. 660 , p. 666 ; see also p. 640

Published
2015

31. Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression

Author

Lei Hoon See, Jean Monlong, Huaien Wang, Dmitri D. Pervouchine, Alessandra Breschi, Alexander Dobin, Julien Lagarde, Cedric Notredame, Michael A. Beer, Suganthi Balasubramanian, Thomas R. Gingeras, Arif Harmanci, Meagan Fastuca, Chris Zaleski, Baikang Pei, Roderic Guigó, Pablo Prieto Barja, Mark Gerstein, Jorg Drenkow, Andrea Tanzer, Sarah Djebali, Giovanni Bussotti, and Carrie A. Davis

Subjects
Evolution, General Physics and Astronomy, Computational biology, Biology, Ratolins -- Genètica, Genome, Article, General Biochemistry, Genetics and Molecular Biology, Cell Line, Epigenesis, Genetic, Evolution, Molecular, Histones, Transcriptome, Mice, 03 medical and health sciences, 0302 clinical medicine, Regulació genètica, Gene expression, Genetics, Animals, Humans, Epigenetics, Gene, Gene Library, 030304 developmental biology, Regulation of gene expression, 0303 health sciences, Multidisciplinary, Models, Genetic, Sequence Analysis, RNA, Gene Expression Profiling, Alternative splicing, General Chemistry, Oligonucleotides, Antisense, Biological Evolution, Mice, Inbred C57BL, Gene expression profiling, Alternative Splicing, Biological sciences, Phenotype, Gene Expression Regulation, 030217 neurology & neurosurgery
Abstract

Mice have been a long-standing model for human biology and disease. Here we characterize, by RNA sequencing, the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles in human cell lines reveals substantial conservation of transcriptional programmes, and uncovers a distinct class of genes with levels of expression that have been constrained early in vertebrate evolution. This core set of genes captures a substantial fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with conserved epigenetic marking, as well as with characteristic post-transcriptional regulatory programme, in which sub-cellular localization and alternative splicing play comparatively large roles., The analysis of mammalian transcriptomes could provide new insights into human biology. Here the authors carry out RNA sequencing in a large collection of mouse tissues and compare these data to human transcriptome profiles, identifying a set of constrained genes that carry out basic cellular functions with remarkably constant expression levels across tissues and species.

Published
2015

33. Enhanced Transcriptome Maps from Multiple Mouse Tissues Reveal Evolutionary Constraint in Gene Expression for Thousands of Genes

Author

Lei Hoon See, Cedric Notredame, Arif Harmanci, Dmitri D. Pervouchine, Pablo Prieto Barja, Jean Monlong, Michael A. Beer, Alexander Dobin, Mark Gerstein, Alessandra Breschi, Huaien Wang, Andrea Tanzer, Baikang Pei, Meagan Fastuca, Jorg Drenkow, Suganthi Balasubramanian, Chris Zaleski, Thomas R. Gingeras, Julien Lagarde, Roderic Guigó, Sarah Djebali, Giovanni Bussotti, and Carrie A. Davis

Subjects
Transcriptome, Genetics, Cell type, Alternative splicing, Gene expression, Computational biology, Epigenetics, Biology, Gene, Embryonic stem cell, Phenotype
Abstract

We characterized by RNA-seq the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles obtained in human cell lines reveals substantial conservation of transcriptional programs, and uncovers a distinct class of genes with levels of expression across cell types and species, that have been constrained early in vertebrate evolution. This core set of genes capture a substantial and constant fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but it is associated with strong and conserved epigenetic marking, as well as to a characteristic post-transcriptional regulatory program in which sub-cellular localization and alternative splicing play comparatively large roles.

Published
2014

34. A comparative encyclopedia of DNA elements in the mouse genome

Author

Ting Wang, Matthew D. Rasmussen, Dana N. Levasseur, Thomas A. Reh, Fidencio Neri, Tanya Kutyavin, Peter J. Good, Katrina Learned, Kate R. Rosenbloom, Lei Hoon See, Cedric Notredame, Ross C. Hardison, Chris Zaleski, Gerd A. Blobel, Dmitri D. Pervouchine, Shane Neph, Ali Mortazavi, Tamer Kahveci, Arthur I. Skoultchi, Peter J. Sabo, Jorg Drenkow, Alex Reynolds, Rebecca F. Lowdon, Marella F. T. R. de Bruijn, John A. Stamatoyannopoulos, Yin Shen, Richard Humbert, Anthony Kirilusha, Sherman M. Weissman, Roderic Guigó, James Taylor, Paul Flicek, Kristen Lee, Shinny Vong, Xiaoyi Cao, Douglas Dunn, Alessandra Breschi, Elise A. Feingold, Michael A. Beer, Piper M. Treuting, Igor Antoshechkin, Thomas R. Gingeras, Georgi K. Marinov, Tyrone Ryba, Jin Lian, Christine M. Disteche, Javier Herrero, Eric Rynes, Sheng Zhong, Weisheng Wu, Richard Sandstrom, Gilberto DeSalvo, Michael Snyder, Meagan Fastuca, Miguel Pignatelli, Mark Groudine, Sarah Djebali, Nergiz Dogan, Rajinder Kaul, Giovanni Bussotti, Henry Amrhein, Daniel Bates, Matthew S. Wilken, Alexander Dobin, Manolis Kellis, Licia Selleri, Diane Trout, Morgan Diegel, David M. Gilbert, Zhihai Ma, Jeff Vierstra, Xiao Qiao Zhou, Theresa K. Canfield, Venkat S. Malladi, Zhen Ye, Anthony Shafer, Tejaswini Mishra, Yiing Lin, Samantha Kuan, Anshul Kundaje, Thalia Papayannopoulou, Pablo Prieto, Robert E. Thurman, Long Pham, Cricket A. Sloan, Olgert Denas, Kathryn Beal, Erika Giste, Andrea Tanzer, Katherine I. Fisher-Aylor, Shin Lin, M. A. Bender, Stuart H. Orkin, Carrie A. Davis, Benjamin D. Pope, Rachel Byron, Srikanta Gosh, Beatriz Lacerda de Sousa, R. Scott Hansen, Brian A. Williams, Yong Cheng, Kanwei Li, Dongwon Lee, Yanli Wang, Evan E. Eichler, Camden Jansen, Feng Yue, Deepti Jain, Miaohua Zhang, David McCleary, W. James Kent, Melissa S. Cline, Kai Hsin Chang, Leslie B. Adams, Robert S. Harris, Ghia Euskirchen, Christapher S. Morrissey, Alexander Y. Rudensky, Philip Cayting, Drew T. Erickson, Eric Haugen, Michael J. Pazin, Vanessa M. Kirkup, Yi-Chieh Wu, Trupti Kawli, Mukul S. Bansal, Cheryl A. Keller, Lee Edsall, Robert M. Samstein, Bing Ren, Miguel Santos, Steven Z. Josefowicz, Barbara J. Wold, Julien Lagarde, Alan P. Boyle, Mitchell J. Weiss, Audra K. Johnson, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Wu, Yi-Chieh, Rasmussen, Matthew D., Bansal, Mukul S., and Kellis, Manolis

Subjects
Epigenomics, DNA Replication, Biology, Regulatory Sequences, Nucleic Acid, ENCODE, Genome, Article, Mice, Species Specificity, Animals, Deoxyribonuclease I, Humans, Cell Lineage, Gene Regulatory Networks, Scaffold/matrix attachment region, Gene, ChIA-PET, Conserved Sequence, Genetics, Multidisciplinary, Mouse genome, Molecular Sequence Annotation, Genomics, Chromatin, Gene Expression Regulation, RNA, Human genome, Transcriptome, Genome-Wide Association Study, Transcription Factors
Abstract

The laboratory mouse shares the majority of its protein-coding genes with humans, making it the premier model organism in biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases. This work is funded by grants R01HG003991 (B.R.), 1U54HG007004 (T.R.G.), 3RC2HG005602 (M.P.S.), GM083337 and GM085354 (D.M.G.), F31CA165863 (B.D.P.), RC2HG005573 and R01DK065806 (R.C.H.) from the National Institutes of Health, and BIO2011-26205 from the Spanish Plan Nacional and ERC 294653 (to R.G.). J.V. is supported by a National Science Foundation Graduate Research Fellowship under grant no. DGE-071824. K.B., M.P., J.H. and P.F. acknowledge the Wellcome Trust (grant number 095908), the NHGRI (grant number U01HG004695) and the European Molecular Biology Laboratory. We thank G. Hon for helping the analysis of high-throughput enhancer validation. L.S. is supported by R01HD043997-09. S.L. was supported by grants F32HL110473 and K99HL119617.

Published
2014

35. Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Author

Modesto Orozco, Montserrat Soler-López, David Torrents, Sarah Djebali, Elisa Durán, Roderic Guigó, Santi González, Oscar Flores, and Josep M. Mercader

Subjects
Transcription, Genetic, Response element, Mammalian promoter database, Computational biology, Biology, Gene Regulation, Chromatin and Epigenetics, Genoma humà, Genome, Epigenesis, Genetic, Histones, Epigenetics of physical exercise, Genetics, Animals, Humans, Promoter Regions, Genetic, Gene, Conserved Sequence, Genome, Human, Computational Biology, Promoter, Sequence Analysis, DNA, Chromatin, Genetic Code, Nucleic Acid Conformation, Human genome, Software, Genètica
Abstract

Although protein recognition of DNA motifs in promoter regions has been traditionally considered as a critical regulatory element in transcription, the location of promoters, and in particular transcription start sites (TSSs), still remains a challenge. Here we perform a comprehensive analysis of putative core promoter sequences relative to non-annotated predicted TSSs along the human genome, which were defined by distinct DNA physical properties implemented in our ProStar computational algorithm. A representative sampling of predicted regions was subjected to extensive experimental validation and analyses. Interestingly, the vast majority proved to be transcriptionally active despite the lack of specific sequence motifs, indicating that physical signaling is indeed able to detect promoter activity beyond conventional TSS prediction methods. Furthermore, highly active regions displayed typical chromatin features associated to promoters of housekeeping genes. Our results enable to redefine the promoter signatures and analyze the diversity, evolutionary conservation and dynamic regulation of human core promoters at large-scale. Moreover, the present study strongly supports the hypothesis of an ancient regulatory mechanism encoded by the intrinsic physical properties of the DNA that may contribute to the complexity of transcription regulation in the human genome. This work was supported by the Spanish Ministry of Science and Innovation [BIO2012-32868 and Consolider E-Science Project]; Instituto de Salud Carlos III (Instituto Nacional de Bioinformática);/nEuropean Research Council (ERC) Advanced Grant; Fundación Marcelino Botín. M.O. is an Institució Catalana de Recerca i Estudis Avançats (ICREA) Academia Researcher. Funding for open access charge: Fundación Marcelino Botín

Published
2013

36. Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia

Author

Elias Campo, Anaïs Gouin, Rory Johnson, Cristina Royo, Ivo Gut, David G. Knowles, Pedro Jares, Magda Pinyol, Alejandra Martínez-Trillos, Nuria Lopez-Bigas, Julie Blanc, Xose S. Puente, Marta Aymerich, Guillem Clot, Carlos López-Otín, Daniel Rico, David Tamborero, Maite Cazorla, Gonzalo Gómez-López, David G. Pisano, Pedro G. Ferreira, José I. Martín-Subero, Alfonso Valencia, Víctor Quesada, Roderic Guigó, Dolors Colomer, Armando López-Guillermo, María Rozman, Abel Gonzalez-Perez, Marta Gut, Simone Ecker, Panagiotis Papasaikas, Jean Monlong, Marta Kulis, Neus Villamor, Sarah Djebali, Mónica López-Guerra, and Universitat de Barcelona

Subjects
Male, Chronic lymphocytic leukemia, Pseudogene, Cèl·lules B, Immunoglobulin Variable Region, Biology, Genètica molecular, Transcriptome, immune system diseases, hemic and lymphatic diseases, Genetics, medicine, Humans, Leucèmia limfocítica crònica, Molecular genetics, Gene, Genetics (clinical), Exome sequencing, Aged, Regulation of gene expression, B-Lymphocytes, B cells, Base Sequence, Research, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Middle Aged, medicine.disease, Leukemia, Lymphocytic, Chronic, B-Cell, Expressió gènica, Gene Expression Regulation, Neoplastic, Gene expression profiling, Genòmica, Mutation, Spliceosomes, Female, Gene expression, IGHV@, Genètica humana -- Variació, Ribosomes
Abstract

This work was funded by the Spanish Ministry of Economy and Competitivity (MINECO) through the Instituto de Salud Carlos III (ISCIII), Red Tematica de Investigacion del Cancer (RTICC) and Instituto Nacional de Bioinformatica (INB) from ISCIII, and Consolider CSD2007-00050. C.L.-O. is an investigator of the Botın Foundation and E.C. of the ICREA-Academia program. N.L.-B., D.T., and A.G.-P. acknowledge funding from the Spanish Ministry of Science and Technology (grant number SAF2009-06954). S.E. is supported by a fellowship from La Caixa., Ferreira, P.G., Jares, P., Rico, D., Gómez-López, G., Martínez-Trillos, A., Villamor, N., Ecker, S., González-Pérez, A., Knowles, D.G., Monlong, J., Johnson, R., Quesada, V., Djebali, S., Papasaikas, P., López-Guerra, M., Colomer, D., Royo, C., Cazorla, M., Pinyol, M., Clot, G., Aymerich, M., Rozman, M., Kulis, M., Tamborero, D., Gouin, A., Blanc, J., Gut, M., Gut, I., Puente, X.S., Pisano, D.G., Martin-Subero, J.I., López-Bigas, N., López-Guillermo, A., Valencia, A., López-Otín, C., Campo, E., Guigó, R.

Published
2013

37. S0103 Recurrent chimeric transcripts in human and mouse

Author

B. Rodríguez Martín, Alessandra Breschi, Sarah Djebali, Alberto Rastrojo, Dmitri D. Pervouchine, Graciela Alonso, Emilio Palumbo, Alexander Dobin, Roderic Guigó, Carrie A. Davis, Begoña Aguado, and Thomas R. Gingeras

Subjects
Genetics, Animal Science and Zoology, General Medicine, Food Science
Published
2016

38. An encyclopedia of mouse DNA elements (Mouse ENCODE)

Author

David M. Gilbert, A Sandra Stehling, Zhihai Ma, Barbara J. Wold, Sonali Jha, Theresa K. Canfield, Julien Lagarde, Vanessa M. Kirkup, W. James Kent, Olgert Denas, M. A. Bender, Erica Giste, Mark Groudine, Yong Cheng, Sarah Djebali, Tyrone Ryba, Marta Byrska-Bishop, Jin Lian, Zhen Ye, Philip Cayting, Michael Snyder, Cheryl A. Keller, Alexander Dobin, Anthony Kiralusha, Tejaswini Mishra, Gayathri Balasundaram, Wulan Deng, Gaurav Jain, Ross C. Hardison, Michael J. Pazin, Stephen G. Landt, Takayo Sasaki, Mia Zhang, Manoj Hariharan, Job Dekker, Peter J. Sabo, Diane Trout, Bing Ren, Mitchell J. Weiss, Audra K. Johnson, Sherman M. Weissman, Deepti Jain, Venkat S. Malladi, Huaien Wang, David McCleary, Melissa S. Cline, Robert E. Thurman, Weisheng Wu, Samantha Kuan, Gregory E. Crawford, Yin Shen, Daniel Blankenberg, Wei Lin, Thomas R. Gingeras, Vaughan Roach, Roderic Guigó, James Taylor, Rajinder Kaul, Gilberto DeSalvo, Henry Amrhein, Carrie A. Davis, Lee Edsall, Amartya Sanyal, Katherine I. Fisher-Aylor, Feng Yue, Ali Mortazavi, Brian A. Williams, Gerd A. Blobel, Rebecca F. Lowdon, Chris Zaleski, Bryan R. Lajoie, John A. Stamatoyannopoulos, Peter J. Good, Katrina Learned, Christopher T. Morrissey, Georgi K. Marinov, Kaun-Bei Chen, Richard Sandstrom, Meagan Fastuca, Elise A. Feingold, Kate R. Rosenbloom, Max Pimkin, Leslie B Adams, Chenghai Xue, Rachel Byron, and Swathi Ashok Kumar

Subjects
ENCODE Project, Ratolins (Animals de laboratori), InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), Genomics, RNA-Seq, mouse genome, Data_CODINGANDINFORMATIONTHEORY, comparative genomics, Biology, ENCODE, Genome, chemistry.chemical_compound, Mice, DNaseI hypersensitive sites, Animals, Humans, Comparative genomics, Genetics, Internet, Genome, Human, histone modifications, Molecular Sequence Annotation, Human genetics, DNA binding site, ChIP-seq, Genòmica, ComputingMethodologies_PATTERNRECOGNITION, chemistry, transcription factor binding sites, Open Letter, RNA-seq, Databases, Nucleic Acid, transcriptome, DNA
Abstract

To complement the human Encyclopedia of DNA Elements (ENCODE) project and to enable a broad range of mouse genomics efforts, the Mouse ENCODE Consortium is applying the same experimental pipelines developed for human ENCODE to annotate the mouse genome.

Published
2012

39. Evidence for Transcript Networks Composed of Chimeric RNAs in Human Cells

Author

Vincent Lacroix, Sylvain Foissac, Ryan R. Murray, Sarah Djebali, Cédric Howald, Josep Lluís Gelpí, Paolo Ribeca, Alexandre Reymond, Kourosh Salehi-Ashtiani, Marc Vidal, Roderic Guigó, Thomas R. Gingeras, John A. Stamatoyannopoulos, Erica Dumais, Alexander Dobin, Bryan R. Lajoie, Tim Hubbard, Stylianos E. Antonarakis, Catherine Ucla, Ian Bell, Jacqueline Chrast, Alfonso Valencia, Modesto Orozco, Philippe Batut, Christelle Borel, Adam Frankish, Philipp Kapranov, Jorg Drenkow, Michael L. Tress, Chenwei Lin, Jennifer Harrow, Xinping Yang, Lila Ghamsari, Job Dekker, David C. Martin, Jonathan M. Mudge, Julien Lagarde, Nynke L. van Berkum, Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Universitat Pompeu Fabra [Barcelona] (UPF), Affymetrix Inc., Geneva University Hospital (HUG), The Wellcome Trust Sanger Institute [Cambridge], Laboratoire de Génétique Cellulaire (LGC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université de Lausanne (UNIL), Dana-Farber Cancer Institute [Boston], Harvard University [Cambridge], Cold Spring Harbor Laboratory, Spanish National Cancer Research Centre, University of Barcelona, Sagot, Marie-France, Universitat de Barcelona, Centro de Regulación Genómica (CRG), Affymetrix, An algorithmic view on genomes, cells, and environments (BAMBOO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université de Genève (UNIGE), Sanger Institute, Wellcome Trust, Harvard Medical School [Boston] (HMS), Cold Spring Harbor Laboratory (CSHL), Spanish National Cancer Research Center (CNIO), Universitat de Barcelona (UB), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), University of Washington [Seattle], Universitat Pompeu Fabra [Barcelona], Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Université de Lausanne = University of Lausanne (UNIL), Harvard University, Université de Genève = University of Geneva (UNIGE), Borel, Christelle, Ucla, Catherine, Martin, David, Reymond, Alexandre, and Antonarakis, Stylianos

Subjects
Male, Transcription, Genetic, Microarrays, [SDV]Life Sciences [q-bio], Gene Identification and Analysis, lcsh:Medicine, Gene Expression, Validation Studies as Topic, Transcriptomes, Exon, Transcriptome/physiology, 0302 clinical medicine, Transcripció genètica, Gene Regulatory Networks/genetics/physiology, RNA Isoforms, Gene expression, Molecular Cell Biology, site, ddc:576.5, Cells/metabolism, Gene Regulatory Networks, Molecular genetics, Nucleic Acid Amplification Techniques/methods, lcsh:Science, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Genetics, 0303 health sciences, Multidisciplinary, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Genetic transcription, element, Statistics, [SDV.BBM.MN]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular Networks [q-bio.MN], tool, Genomics, reverse transcription, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, encode, Chromosomes, Human, Pair 1/genetics, annotation, Chromosomes, Human, Pair 1, RNA/genetics/physiology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, Sequence Analysis, Nucleic Acid Amplification Techniques, Algorithms, Research Article, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Cells, cloning, Chimerin Proteins/chemistry/genetics, Chimeric gene, Biology, Metabolisme cel·lular, Biostatistics, Models, Biological, Genètica molecular, Molecular Genetics, 03 medical and health sciences, Genome Analysis Tools, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], human genome, Chimerin Proteins/chemistry, Chimerin Proteins/genetics, Gene Expression Profiling, Gene Regulatory Networks/genetics, Gene Regulatory Networks/physiology, Humans, Microarray Analysis/methods, RNA/genetics, RNA/physiology, RNA Isoforms/chemistry, RNA Isoforms/genetics, Transcription, Genetic/genetics, Gene, 030304 developmental biology, RNA Isoforms/chemistry/genetics/metabolism, [SDV.OT] Life Sciences [q-bio]/Other [q-bio.OT], lcsh:R, Chimerin Proteins, RNA, gene fusion, Computational Biology, Microarray Analysis, Gene expression profiling, [SDV.BBM.MN] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular Networks [q-bio.MN], Human genome, lcsh:Q, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], discovery, Genome Expression Analysis, Transcriptome, 030217 neurology & neurosurgery, Genètica, Mathematics
Abstract

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network. This work has been supported by grants U01HG003150 and U01HG003147 from the National Human Genome Research for the ENCODE project. RG was also supported by a grant from the Spanish Ministry of Education and Science. In addition, KS-A, RM, XY, CL, LG and MV were supported by a grant from the Ellison Foundation and as Institute Sponsored Research from the Dana Farber Cancer Institute Strategic Initiative. The laboratories of SA and AR were supported by grants from the Swiss National Science Foundation and the European Commission AnEUploidy Integrated Project. SA was also supported by the National Center of Excellence ‘‘Frontiers in Genetics’’, ChildCare Foundation and an ERC grant from the European Union. AF, TH and JM acknowledge support from the Wellcome Trust. The work of JLG and MO has been supported by the Spanish Ministry of Science (CTQ2005-09365-C02-02, BIO2009-10964), Instituto Nacional de Bioinformática, the Consolider E-science project (CSD2007-00050), COMBIOMED RETICS and the Fundación Marcelino Botin. JD is supported by a grant from the National Institutes of Health (HG003143) and a W. M. Keck Foundation Distinguished Young Scholar Award. JS is supported by a grant from the National Institutes of Health (U54 HG004592). The work of MT and AV was supported by Consolider E-Science (CSD2007-00050) and the Instituto Nacional de Bioinformática. MV (Center for Cancer Systems Biology, CCSB) is a ‘‘Chercheur Qualifié Honoraire’’ from the Fonds de la Recherche Scientifique (FRS-FNRS, French Community of Belgium). This work has also been funded by grant to TG by NHGRI (U54HG004557) and partially byAffymetirix, Corp

Published
2012

40. Understanding transcriptional regulation by integrative analysis of transcription factor binding data

Author

Yijun Ruan, Joel Rozowsky, Carrie A. Davis, Michael Snyder, Roger P. Alexander, Ewan Birney, Mark Gerstein, Xianjun Dong, Timo Lassman, Koon-Kiu Yan, Jing Leng, Rengqiang Min, Sarah Djebali, Roderic Guigó, Kevin Y. Yip, Piero Carninci, Thomas R. Gingeras, Zhiping Weng, and Chao Cheng

Subjects
Transcription, Genetic, Computational biology, Biology, Models, Biological, Cell Line, Histones, Transcripció genètica, Transcription (biology), Gene expression, Genetics, Transcriptional regulation, Humans, Promoter Regions, Genetic, Transcription factor, Genetics (clinical), Regulation of gene expression, Base Composition, Binding Sites, Research, Computational Biology, Genomics, Expressió gènica, Chromatin, Genòmica, Gene Expression Regulation, CpG site, Transcription Initiation Site, Hypersensitive site, Protein Binding, Transcription Factors
Abstract

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner. This work has been carried out under AL Williams Professorship funds

Published
2012

41. An integrated encyclopedia of DNA elements in the human genome

Author

Robert Altshuler, Laura Elnitski, Michael Anaya, Alec Victorsen, Deborah Winter, Javier Herrero, Katherine Varley, Andrea Sboner, Oscar Junhong Luo, Marco Mariotti, Cristina Sisu, Mike Kay, Timothy Dreszer, Jane Loveland, Alexandra Bignell, Ewan Birney, Tim @timjph Hubbard, Kuljeet Sandhu, Eric Haugen, Chris Gunter, Alexej Abyzov, Lucas Ward, Georgi Marinov, Michael Pazin, Thomas Gingeras, Alexander Dobin, Kimberly Foss, Xianjun Dong, Benoit Miotto, Piotr Mieczkowski, Cedric Notredame, Andrew Berry, Shawn Gillespie, Axel Visel, Shawn Levy, Richard Sandstrom, Jose M Gonzalez, Melissa Fullwood, Timo Lassmann, Michael Tress, Julien Lagarde, Kevin Yip, Leslie Adams, Sylvain Foissac, Bronwen Aken, Piero Carninci, Suganthi Balasubramanian, Andrea Tanzer, Sarah Djebali, Michael Hoffman, Gloria Despacio-Reyes, Peter Park, Felix Kokocinski, Katherine Fisher-Aylor, Juan M Vaquerizas, Peggy Farnham, Patrick Collins, Amonida Zadissa, Pedro Ferreira, Philippe Batut, Michael Snyder, Electra Tapanari, Adam Frankish, Paul Flicek, AMARTYA SANYAL, Tyler Alioto, Giovanni Bussotti, Laurence Meyer, Jingyi Jessica Li, Matthew Blow, Tristan FRUM, Roger Alexander, Rory Johnson, Charles Steward, Meizhen Zheng, Margus Lukk, Ross Hardison, Claire Davidson, Gary Saunders, Alan Boyle, Luiz Penalva, Rajinder Kaul, Lazaro Centanin, Florencia Pauli Behn, Thomas Derrien, Nathan Sheffield, Toby Hunt, Eric Nguyen, Jeff Vierstra, Konrad Karczewski, Kimberly Bell, Yanbao Yu, Hagen U Tilgner, James Taylor, Balázs Bánfai, Catherine Snow, Benjamin Vernot, Stephan Kirchmaier, Michael Sammeth, Steven Wilder, Angelika Merkel, Joanna Mieczkowska, Guoliang Li, Wei Lin, Jennifer Harrow, Thomas Oliver Auer, Daniel Barrell, Eddie Park, Alvis Brazma, Hazuki Takahashi, Nathan Johnson, Daniel Sobral, Terry Furey, Alexandre Reymond, Jonathan Mudge, Anshul Kundaje, Jose Rodriguez, Akshay Bhinge, James Gilbert, Jakub Karczewski, Venkat Malladi, Troy Whitfield, Orion Buske, Ian Dunham, Jennifer Moran, Joachim Wittbrodt, Charles B. Epstein, Laurens Wilming, Jason Gertz, Joshua Akey, Joel Rozowsky, Laboratoire de Génétique Cellulaire (LGC), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), National Human Genome Research Institute (NHGRI), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Antonarakis, Stylianos, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Altshuler, Robert Charles, Ernst, Jason, Kellis, Manolis, Kheradpour, Pouya, Ward, Lucas D., Eaton, Matthew Lucas, Hendrix, David A., Jungreis, Irwin, Lin, Michael F., Washietl, Stefan, Lists of participants and their affiliations appear at the end of the paper and in the 'Collaboration/Projet' field., The Consortium is funded by grants from the NHGRI as follows: production grants: U54HG004570 (B. E. Bernstein), U01HG004695 (E. Birney), U54HG004563 (G. E. Crawford), U54HG004557 (T. R. Gingeras), U54HG004555 (T. J. Hubbard), U41HG004568 (W. J. Kent), U54HG004576 (R. M. Myers), U54HG004558 (M. Snyder), U54HG004592 (J. A. Stamatoyannopoulos). Pilot grants: R01HG003143 (J. Dekker), RC2HG005591 and R01HG003700 (M. C. Giddings), R01HG004456-03 (Y. Ruan), U01HG004571 (S. A. Tenenbaum), U01HG004561 (Z. Weng), RC2HG005679 (K. P. White). This project was supported in part by American Recovery and Reinvestment Act (ARRA) funds from the NHGRI through grants U54HG004570, U54HG004563, U41HG004568, U54HG004592, R01HG003143, RC2HG005591, R01HG003541,U01HG004561,RC2HG005679andR01HG003988(L. Pennacchio). In addition, work from NHGRI Groups was supported by the Intramural Research Program of the NHGRI (L. Elnitski, ZIAHG200323, E. H. Margulies, ZIAHG200341). Research in the Pennachio laboratory was performed at Lawrence Berkeley National Laboratory and at the United States Department of Energy Joint Genome Institute, Department of Energy Contract DE-AC02-05CH11231, University of California., Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, Epstein CB, Frietze S, Harrow J, Kaul R, Khatun J, Lajoie BR, Landt SG, Lee BK, Pauli F, Rosenbloom KR, Sabo P, Safi A, Sanyal A, Shoresh N, Simon JM, Song L, Trinklein ND, Altshuler RC, Birney E, Brown JB, Cheng C, Djebali S, Dong X, Dunham I, Ernst J, Furey TS, Gerstein M, Giardine B, Greven M, Hardison RC, Harris RS, Herrero J, Hoffman MM, Iyer S, Kellis M, Khatun J, Kheradpour P, Kundaje A, Lassmann T, Li Q, Lin X, Marinov GK, Merkel A, Mortazavi A, Parker SC, Reddy TE, Rozowsky J, Schlesinger F, Thurman RE, Wang J, Ward LD, Whitfield TW, Wilder SP, Wu W, Xi HS, Yip KY, Zhuang J, Pazin MJ, Lowdon RF, Dillon LA, Adams LB, Kelly CJ, Zhang J, Wexler JR, Green ED, Good PJ, Feingold EA, Bernstein BE, Birney E, Crawford GE, Dekker J, Elnitski L, Farnham PJ, Gerstein M, Giddings MC, Gingeras TR, Green ED, Guigó R, Hardison RC, Hubbard TJ, Kellis M, Kent W, Lieb JD, Margulies EH, Myers RM, Snyder M, Stamatoyannopoulos JA, Tenenbaum SA, Weng Z, White KP, Wold B, Khatun J, Yu Y, Wrobel J, Risk BA, Gunawardena HP, Kuiper HC, Maier CW, Xie L, Chen X, Giddings MC, Bernstein BE, Epstein CB, Shoresh N, Ernst J, Kheradpour P, Mikkelsen TS, Gillespie S, Goren A, Ram O, Zhang X, Wang L, Issner R, Coyne MJ, Durham T, Ku M, Truong T, Ward LD, Altshuler RC, Eaton ML, Kellis M, Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Röder M, Kokocinski F, Abdelhamid RF, Alioto T, Antoshechkin I, Baer MT, Batut P, Bell I, Bell K, Chakrabortty S, Chen X, Chrast J, Curado J, Derrien T, Drenkow J, Dumais E, Dumais J, Duttagupta R, Fastuca M, Fejes-Toth K, Ferreira P, Foissac S, Fullwood MJ, Gao H, Gonzalez D, Gordon A, Gunawardena HP, Howald C, Jha S, Johnson R, Kapranov P, King B, Kingswood C, Li G, Luo OJ, Park E, Preall JB, Presaud K, Ribeca P, Risk BA, Robyr D, Ruan X, Sammeth M, Sandhu KS, Schaeffer L, See LH, Shahab A, Skancke J, Suzuki AM, Takahashi H, Tilgner H, Trout D, Walters N, Wang H, Wrobel J, Yu Y, Hayashizaki Y, Harrow J, Gerstein M, Hubbard TJ, Reymond A, Antonarakis SE, Hannon GJ, Giddings MC, Ruan Y, Wold B, Carninci P, Guigó R, Gingeras TR, Rosenbloom KR, Sloan CA, Learned K, Malladi VS, Wong MC, Barber GP, Cline MS, Dreszer TR, Heitner SG, Karolchik D, Kent W, Kirkup VM, Meyer LR, Long JC, Maddren M, Raney BJ, Furey TS, Song L, Grasfeder LL, Giresi PG, Lee BK, Battenhouse A, Sheffield NC, Simon JM, Showers KA, Safi A, London D, Bhinge AA, Shestak C, Schaner MR, Kim SK, Zhang ZZ, Mieczkowski PA, Mieczkowska JO, Liu Z, McDaniell RM, Ni Y, Rashid NU, Kim MJ, Adar S, Zhang Z, Wang T, Winter D, Keefe D, Birney E, Iyer VR, Lieb JD, Crawford GE, Li G, Sandhu KS, Zheng M, Wang P, Luo OJ, Shahab A, Fullwood MJ, Ruan X, Ruan Y, Myers RM, Pauli F, Williams BA, Gertz J, Marinov GK, Reddy TE, Vielmetter J, Partridge E, Trout D, Varley KE, Gasper C, Bansal A, Pepke S, Jain P, Amrhein H, Bowling KM, Anaya M, Cross MK, King B, Muratet MA, Antoshechkin I, Newberry KM, McCue K, Nesmith AS, Fisher-Aylor KI, Pusey B, DeSalvo G, Parker SL, Balasubramanian S, Davis NS, Meadows SK, Eggleston T, Gunter C, Newberry J, Levy SE, Absher DM, Mortazavi A, Wong WH, Wold B, Blow MJ, Visel A, Pennachio LA, Elnitski L, Margulies EH, Parker SC, Petrykowska HM, Abyzov A, Aken B, Barrell D, Barson G, Berry A, Bignell A, Boychenko V, Bussotti G, Chrast J, Davidson C, Derrien T, Despacio-Reyes G, Diekhans M, Ezkurdia I, Frankish A, Gilbert J, Gonzalez JM, Griffiths E, Harte R, Hendrix DA, Howald C, Hunt T, Jungreis I, Kay M, Khurana E, Kokocinski F, Leng J, Lin MF, Loveland J, Lu Z, Manthravadi D, Mariotti M, Mudge J, Mukherjee G, Notredame C, Pei B, Rodriguez JM, Saunders G, Sboner A, Searle S, Sisu C, Snow C, Steward C, Tanzer A, Tapanari E, Tress ML, van Baren MJ, Walters N, Washietl S, Wilming L, Zadissa A, Zhang Z, Brent M, Haussler D, Kellis M, Valencia A, Gerstein M, Reymond A, Guigó R, Harrow J, Hubbard TJ, Landt SG, Frietze S, Abyzov A, Addleman N, Alexander RP, Auerbach RK, Balasubramanian S, Bettinger K, Bhardwaj N, Boyle AP, Cao AR, Cayting P, Charos A, Cheng Y, Cheng C, Eastman C, Euskirchen G, Fleming JD, Grubert F, Habegger L, Hariharan M, Harmanci A, Iyengar S, Jin VX, Karczewski KJ, Kasowski M, Lacroute P, Lam H, Lamarre-Vincent N, Leng J, Lian J, Lindahl-Allen M, Min R, Miotto B, Monahan H, Moqtaderi Z, Mu XJ, O'Geen H, Ouyang Z, Patacsil D, Pei B, Raha D, Ramirez L, Reed B, Rozowsky J, Sboner A, Shi M, Sisu C, Slifer T, Witt H, Wu L, Xu X, Yan KK, Yang X, Yip KY, Zhang Z, Struhl K, Weissman SM, Gerstein M, Farnham PJ, Snyder M, Tenenbaum SA, Penalva LO, Doyle F, Karmakar S, Landt SG, Bhanvadia RR, Choudhury A, Domanus M, Ma L, Moran J, Patacsil D, Slifer T, Victorsen A, Yang X, Snyder M, Auer T, Centanin L, Eichenlaub M, Gruhl F, Heermann S, Hoeckendorf B, Inoue D, Kellner T, Kirchmaier S, Mueller C, Reinhardt R, Schertel L, Schneider S, Sinn R, Wittbrodt B, Wittbrodt J, Weng Z, Whitfield TW, Wang J, Collins PJ, Aldred SF, Trinklein ND, Partridge EC, Myers RM, Dekker J, Jain G, Lajoie BR, Sanyal A, Balasundaram G, Bates DL, Byron R, Canfield TK, Diegel MJ, Dunn D, Ebersol AK, Frum T, Garg K, Gist E, Hansen R, Boatman L, Haugen E, Humbert R, Jain G, Johnson AK, Johnson EM, Kutyavin TV, Lajoie BR, Lee K, Lotakis D, Maurano MT, Neph SJ, Neri FV, Nguyen ED, Qu H, Reynolds AP, Roach V, Rynes E, Sabo P, Sanchez ME, Sandstrom RS, Sanyal A, Shafer AO, Stergachis AB, Thomas S, Thurman RE, Vernot B, Vierstra J, Vong S, Wang H, Weaver MA, Yan Y, Zhang M, Akey JM, Bender M, Dorschner MO, Groudine M, MacCoss MJ, Navas P, Stamatoyannopoulos G, Kaul R, Dekker J, Stamatoyannopoulos JA, Dunham I, Beal K, Brazma A, Flicek P, Herrero J, Johnson N, Keefe D, Lukk M, Luscombe NM, Sobral D, Vaquerizas JM, Wilder SP, Batzoglou S, Sidow A, Hussami N, Kyriazopoulou-Panagiotopoulou S, Libbrecht MW, Schaub MA, Kundaje A, Hardison RC, Miller W, Giardine B, Harris RS, Wu W, Bickel PJ, Banfai B, Boley NP, Brown JB, Huang H, Li Q, Li JJ, Noble WS, Bilmes JA, Buske OJ, Hoffman MM, Sahu AD, Kharchenko PV, Park PJ, Baker D, Taylor J, Weng Z, Iyer S, Dong X, Greven M, Lin X, Wang J, Xi HS, Zhuang J, Gerstein M, Alexander RP, Balasubramanian S, Cheng C, Harmanci A, Lochovsky L, Min R, Mu XJ, Rozowsky J, Yan KK, Yip KY, Birney E., and Miotto, Benoit

Subjects
Encyclopedias as Topic, [SDV]Life Sciences [q-bio], DNA Footprinting, Genoma humà, Binding Sites/genetics, Histones/chemistry/metabolism, 0302 clinical medicine, Exons/genetics, ddc:576.5, 0303 health sciences, Multidisciplinary, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], DNA-Binding Proteins/metabolism, region, Chemistry, Genetic Predisposition to Disease/genetics, Genomics, Polymorphism, Single Nucleotide/genetics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Neoplasms/genetics, Chromatin, Cell biology, in vivo, Genetic Variation/genetics, 030220 oncology & carcinogenesis, Deoxyribonuclease I/metabolism, Proteins/genetics, transcription factor-binding, chromosome conformation capture, DNA Methylation/genetics, Chromosomes, Human/genetics/metabolism, Chromatin Immunoprecipitation, Mammals/genetics, DNA/genetics, determinant, Article, 03 medical and health sciences, map, Animals, Humans, Transcription Factors/metabolism, Alleles, mouse, 030304 developmental biology, Transcription, Genetic/genetics, Chromatin/genetics/metabolism, Sequence Analysis, RNA, human cell, Molecular Sequence Annotation, Regulatory Sequences, Nucleic Acid/genetics, Promoter Regions, Genetic/genetics, DNA binding site, Genòmica, Genome, Human/genetics, chromatin, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Genètica, Genome-Wide Association Study
Abstract

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research. The Consortium is funded by grants from the NHGRI as follows: production grants: U54HG004570 (B. E. Bernstein); U01HG004695 (E. Birney); U54HG004563 (G. E. Crawford); U54HG004557 (T. R. Gingeras); U54HG004555 (T. J. Hubbard); U41HG004568 /n(W. J. Kent); U54HG004576 (R. M. Myers); U54HG004558 (M. Snyder);/nU54HG004592 (J. A. Stamatoyannopoulos). Pilot grants: R01HG003143 (J. Dekker); RC2HG005591 and R01HG003700 (M. C. Giddings); R01HG004456-03 (Y. Ruan); U01HG004571 (S. A. Tenenbaum); U01HG004561 (Z. Weng); RC2HG005679 (K. P. White). This project was supported in part by American Recovery and/nReinvestment Act (ARRA) funds from the NHGRI through grants U54HG004570, U54HG004563, U41HG004568, U54HG004592, R01HG003143, RC2HG005591,R01HG003541, U01HG004561, RC2HG005679andR01HG003988(L. Pennacchio). In addition, work from NHGRI Groups was supported by the Intramural Research/nProgram of the NHGRI (L. Elnitski, ZIAHG200323; E. H. Margulies, ZIAHG200341). Research in the Pennachio laboratory was performed at Lawrence Berkeley National Laboratory and at the United States Department of Energy Joint Genome Institute, Department of Energy Contract DE-AC02-05CH11231, University of California.

Published
2012

42. Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs

Author

Michael Snyder, Hagen Tilgner, Rory Johnson, Joao Curado, Sarah Djebali, Thomas R. Gingeras, Sudipto K. Chakrabortty, Roderic Guigó, Carrie A. Davis, and David G. Knowles

Subjects
Transcription, Genetic, RNA Splicing, Trans-splicing, Exonic splicing enhancer, Biology, Genoma humà, 03 medical and health sciences, Splicing factor, Exon, 0302 clinical medicine, Transcripció genètica, Genetics, Cluster Analysis, Humans, Empalmament (Genètica), Genetics (clinical), 030304 developmental biology, 0303 health sciences, Genome, Human, Sequence Analysis, RNA, Research, Alternative splicing, Intron, Computational Biology, High-Throughput Nucleotide Sequencing, Exons, Chromatin, Post-transcriptional modification, RNA no missatgers, RNA splicing, Spliceosomes, RNA, RNA, Long Noncoding, 030217 neurology & neurosurgery, Subcellular Fractions
Abstract

Splicing remains an incompletely understood process. Recent findings suggest that chromatin structure participates in its regulation. Here, we analyze the RNA from subcellular fractions obtained through RNA-seq in the cell line K562. We show that in the human genome, splicing occurs predominantly during transcription. We introduce the coSI measure, based on RNA-seq reads mapping to exon junctions and borders, to assess the degree of splicing completion around internal exons. We show that, as expected, splicing is almost fully completed in cytosolic polyA+ RNA. In chromatin-associated RNA (which includes the RNA that is being transcribed), for 5.6% of exons, the removal of the surrounding introns is fully completed, compared with 0.3% of exons for which no intron-removal has occurred. The remaining exons exist as a mixture of spliced and fewer unspliced molecules, with a median coSI of 0.75. Thus, most RNAs undergo splicing while being transcribed: "co-transcriptional splicing." Consistent with co-transcriptional spliceosome assembly and splicing, we have found significant enrichment of spliceosomal snRNAs in chromatin-associated RNA compared with other cellular RNA fractions and other nonspliceosomal snRNAs. CoSI scores decrease along the gene, pointing to a "first transcribed, first spliced" rule, yet more downstream exons carry other characteristics, favoring rapid, co-transcriptional intron removal. Exons with low coSI values, that is, in the process of being spliced, are enriched with chromatin marks, consistent with a role for chromatin in splicing during transcription. For alternative exons and long noncoding RNAs, splicing tends to occur later, and the latter might remain unspliced in some cases. This work has been carried out under grants RD07/0067/0012, BIO2006-03380, and CSD2007-00050 from the Spanish Ministry of Science, and grants 1U54HG004557-01 and 1U54HG004555-01 from the National Institutes of Health

Published
2012

43. Landscape of transcription in human cells

Author

Tyler Alioto, Colin Kingswood, Lorian Schaffer, Sylvain Foissac, Kimberly Bell, Diane Trout, Wei Lin, Nathalie Walters, Thomas R. Gingeras, Oscar Junhong Luo, Pedro G. Ferreira, Atif Shahab, Assaf Gordon, Kata Fejes-Toth, Joao Curado, Jacqueline Chrast, Thomas Derrien, Georgi K. Marinov, Hazuki Takahashi, Alexander Dobin, Nadav Bar, Lei-Hoon See, Morgan C. Giddings, Erica Dumais, Michael Sammeth, Philipp Kapranov, Jainab Khatun, Meagan Fastuca, Jørgen Skancke, Ian Bell, Emilie Falconnet, Roderic Guigó, Huaien Wang, Kimberly Persaud, Alexandre Reymond, Carrie A. Davis, Xiaoan Ruan, Rehab F. Abdelhamid, Daniel Robyr, Joel Rozowsky, Ana Maria Suzuki, Sonali Jha, Marion S. Röder, Sudipto K. Chakrabortty, Brian A. Risk, Hagen Tilgner, Eddie Park, Stylianos E. Antonarakis, Piero Carninci, Hui Gao, Sarah Djebali, Timo Lassmann, Andrea Tanzer, Philippe Batut, Cédric Howald, Yanbao Yu, Ali Mortazavi, Felix Kokocinski, Jennifer Harrow, Mark Gerstein, Melissa J. Fullwood, Chenghai Xue, Gregory J. Hannon, Xian Chen, Igor Antoshechkin, Yoshihide Hayashizaki, Brian A. Williams, Chris Zaleski, John A. Wrobel, Radha Duttagupta, Barbara J. Wold, Julien Lagarde, David Gonzalez, Yijun Ruan, Tim Hubbard, Angelika Merkel, Rory Johnson, Jonathan B. Preall, Jorg Drenkow, Brandon King, Paolo Ribeca, Harsha P. Gunawardena, Jacqueline Dumais, Felix Schlesinger, Michael T. Baer, Centre for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra [Barcelona] (UPF), Cold Spring Harbor Laboratory, Riken Yokohama Institute, California Institute of Technology (CALTECH), University of California [Irvine] (UCI), University of California, Boise State University, Yale University [New Haven], The Wellcome Trust Sanger Institute [Cambridge], Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Affymetrix Inc., University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Université de Lausanne (UNIL), Laboratoire de Génétique Cellulaire (LGC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Geneva [Switzerland], (IGE3 Inst Genet & Genom Geneva), University of Geneva Medical School, Genome Institute of Singapore (GIS), St Laurent Institut, Falconnet, Emilie, Robyr, Daniel, Reymond, Alexandre, Antonarakis, Stylianos, Universitat Pompeu Fabra [Barcelona], Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)

Subjects
Encyclopedias as Topic, Repetitive Sequences, Nucleic Acid/genetics, Polyadenylation, [SDV]Life Sciences [q-bio], Genoma humà, Genome, Repetitive Sequences, RNA Editing/genetics, 0302 clinical medicine, Exons/genetics, Protein Isoforms, RNA/biosynthesis/genetics, ddc:576.5, RNA Splicing/genetics, Genetics, 0303 health sciences, Multidisciplinary, Exons, Genomics, Transcriptome/genetics, Long non-coding RNA, Enhancer Elements, Genetic, 030220 oncology & carcinogenesis, Sequence Analysis, Transcription, Human, Enhancer Elements, General Science & Technology, 1.1 Normal biological development and functioning, RNA Splicing, DNA, Intergenic/genetics, Computational biology, DNA/genetics, Biology, Article, Cell Line, 03 medical and health sciences, Genetic, Underpinning research, Protein Isoforms/genetics, map, expression, Humans, Genes/genetics, genome, Gene, Alleles, Transcription, Genetic/genetics, 030304 developmental biology, Intergenic, Nucleic Acid, small rnas, Sequence Analysis, RNA, Gene Expression Profiling, Human Genome, RNA, Molecular Sequence Annotation, DNA, Regulatory Sequences, Nucleic Acid/genetics, Gene expression profiling, Genes, Genome, Human/genetics, Polyadenylation/genetics, RNA/biosynthesis, RNA/genetics, enhancer, identification, Human genome, Generic health relevance, RNA Editing, Transcriptome, Regulatory Sequences, Genètica
Abstract

Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell's regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene. This work was supported by the National Human Genome Research Institute (NHGRI) production grants U54HG004557, U54HG004555, U54HG004576 and U54HG004558, and by the NHGRI pilot grant R01HG003700. It was also supported by the NHGRI ARRA stimulus grant 1RC2HG005591, the National Science Foundation (SNF) grant 127375, the European Research Council (ERC) grant/n249968, a research grant for the RIKEN Omics Science Center from the Japanese Ministry of Education, Culture, Sports, Science and Technology, and grants BIO2011-26205, CSD2007-00050 and INB GNV-1 from the Spanish Ministry of Science

Published
2012

44. A User's Guide to the Encyclopedia of DNA Elements (ENCODE)

Author

Zhi Lu, Giltae Song, Troy W. Whitfield, Vishwanath R. Iyer, Teresa Vales, Angelika Merkel, Max Libbrecht, David Haussler, Ting Wang, Kristen Lee, Lingyun Song, Richard M. Myers, Alfonso Valencia, Rachel A. Harte, Xiaoqin Xu, Lucas D. Ward, Hazuki Takahashi, Nathan C. Sheffield, Thomas Derrien, Georgi K. Marinov, Eric D. Nguyen, Bernard B. Suh, Brian J. Raney, Richard Sandstrom, Thomas D. Tullius, Benoit Miotto, Alexander Dobin, Youhan Xu, Lukas Habegger, Ian Dunham, Brian A. Risk, Paul G. Giresi, Morgan C. Giddings, Hualin Xi, Anshul Kundaje, Robert S. Harris, Devin Absher, Peter J. Bickel, Yanbao Yu, Browen Aken, Colin Kingswood, Bryan R. Lajoie, Peter J. Good, Katrina Learned, Laura Elnitski, Shirley Pepke, Brandon King, Piero Carninci, Xinqiong Yang, Ghia Euskirchen, Kathryn Beal, Christelle Borel, Michael Muratet, Robert L. Grossman, David G. Knowles, Zarmik Moqtaderi, Veronika Boychenko, Steven P. Wilder, Michael L. Tress, Florencia Pauli, Alan P. Boyle, Andrea Tanzer, Philipp Kapranov, Serafim Batzoglou, Audra K. Johnson, Jun Neri, Nitin Bhardwaj, Elise A. Feingold, Venkat S. Malladi, Michael M. Hoffman, William Stafford Noble, Andrea Sboner, Mark Gerstein, Stephanie L. Parker, Jacqueline Dumais, Felix Schlesinger, Deborah R. Winter, Randall H. Brown, Thanh Truong, Rebecca F. Lowdon, Paolo Ribeca, Brooke Rhead, Peggy J. Farnham, Krista Thibeault, Terrence S. Furey, Donna Karolchik, Alec Victorsen, Xiaoan Ruan, Rehab F. Abdelhamid, Amy S. Nesmith, Jing Wang, Nicholas M. Luscombe, Alina R. Cao, Diane Trout, Teri Slifer, Peter E. Newburger, Cricket A. Sloan, Dimitra Lotakis, Stephen M. J. Searle, Ali Mortazavi, Alexandra Bignell, Alex Reynolds, Orion J. Buske, Chris Zaleski, Theresa K. Canfield, Ian Bell, Jin Lian, Vanessa K. Swing, Katalin Toth Fejes, Catherine Ucla, Robert E. Thurman, Jacqueline Chrast, Wei Lin, Tim Hubbard, Gary Saunders, Minyi Shi, Vihra Sotirova, Sherman M. Weissman, Jason D. Lieb, Richard Humbert, Kevin M. Bowling, Assaf Gordon, Tarjei S. Mikkelsen, Jing Leng, Thomas R. Gingeras, Fabian Grubert, Nader Jameel, Jost Vielmetter, Hannah Monahan, Preti Jain, Lindsay L. Waite, Tony Shafer, Joel Rozowsky, Michael Coyne, Brian Reed, M. Kay, Harsha P. Gunawardena, Ross C. Hardison, Gavin Sherlock, Alexandra Charos, Joseph D. Fleming, Ann S. Zweig, Jason Gertz, Rajinder Kaul, Xianjun Dong, Alexandre Reymond, Carrie A. Davis, Haiyan Huang, Chao Cheng, Marco Mariotti, Phil Lacroute, Jason A. Dilocker, Kenneth McCue, R. Robilotto, Stylianos E. Antonarakis, Sridar V. Chittur, Justin Jee, Barbara J. Wold, Sudipto K. Chakrabortty, Erica Dumais, Amartya Sanyal, Nathan Boley, Tianyuan Wang, Julien Lagarde, Anthony Kirilusha, Jonathan B. Preall, Kevin Roberts, Erika Giste, Hugo Y. K. Lam, Alvis Brazma, Gregory J. Hannon, Eric Rynes, Philippe Batut, Kevin Struhl, Margus Lukk, Manching Ku, Suganthi Balasubramanian, Sonali Jha, Jorg Drenkow, W. James Kent, Michael Snyder, Jie Wang, Anna Battenhouse, Charles B. Epstein, Rami Rauch, Christopher Shestak, John A. Stamatoyannopoulos, Gaurab Mukherjee, Cédric Howald, Tanya Kutyavin, Huaien Wang, Scott A. Tenenbaum, Wan Ting Poh, Kate R. Rosenbloom, Manolis Kellis, Pauline A. Fujita, Linfeng Wu, Anita Bansal, Molly Weaver, Linda L. Grasfeder, Peter J. Sabo, Qiang Li, Melissa S. Cline, Robert M. Kuhn, Darin London, Seth Frietze, Atif Shahab, Shane Neph, Damian Keefe, James B. Brown, Mark Diekhans, Webb Miller, Katherine Aylor Fisher, Jiang Du, Hadar H. Sheffer, Sarah Djebali, Frank Doyle, Nathan Lamarre-Vincent, Chia-Lin Wei, Laura A.L. Dillon, Jennifer Harrow, Robert C. Altshuler, Tyler Alioto, Raymond K. Auerbach, Adam Frankish, Rebekka O. Sprouse, Patrick J. Collins, E. Christopher Partridge, Zheng Liu, Yoichiro Shibata, Elliott H. Margulies, Abigail K. Ebersol, Kimberly A. Showers, Eric D. Green, Krishna M. Roskin, Job Dekker, Barbara N. Pusey, Ekta Khurana, Gilberto DeSalvo, Yijun Ruan, Hao Wang, Jainab Khatun, Henriette O'Geen, Alexej Abyzov, Brian Williams, Ryan M. McDaniell, Maya Kasowski, Manoj Hariharan, Felix Kokocinski, Gloria Despacio-Reyes, Zhancheng Zhang, Subhradip Karmakar, Ewan Birney, Koon-Kiu Yan, Xian Chen, Shinny Vong, Daniel Sobral, Nick Bild, Seul K.C. Kim, Timo Lassmann, Li Wang, Minerva E. Sanchez, Vaughan Roach, Theodore Gibson, Stephen C. J. Parker, Michael F. Lin, Patrick A. Navas, Laurence R. Meyer, Luiz O. F. Penalva, Bradley E. Bernstein, Kevin P. White, Emilie Aït Yahya Graison, Juan M. Vaquerizas, Sushma Iyengar, Kimberly M. Newberry, Akshay Bhinge, Xiaolan Zhang, Kim Bell, Yoshihide Hayashizaki, Lucas Lochovsky, Noam Shoresh, Hagen Tilgner, Philip Cayting, Dorrelyn Patacsil, Timothy E. Reddy, Eric Haugen, Katherine E. Varley, M. van Baren, Nathan D. Trinklein, Bum Kyu Lee, Tristan Frum, Marianne Lindahl-Allen, Timothy Durham, Roderic Guigó, Christopher W. Maier, Micha Sammeth, Debasish Raha, Timothy R. Dreszer, Benedict Paten, Robbyn Issner, Michael R. Brent, Kevin Y. Yip, Kim Blahnik, Jason Ernst, Zhiping Weng, Henry Amrhein, Arend Sidow, Javier Herrero, Hui Gao, Stephen G. Landt, Pouya Kheradpour, Galt P. Barber, Gregory E. Crawford, Toby Hunt, HudsonAlpha Institute for Biotechnology [Huntsville, AL], ENCODE Project Consortium : Myers RM, Stamatoyannopoulos J, Snyder M, Dunham I, Hardison RC, Bernstein BE, Gingeras TR, Kent WJ, Birney E, Wold B, Crawford GE, Bernstein BE, Epstein CB, Shoresh N, Ernst J, Mikkelsen TS, Kheradpour P, Zhang X, Wang L, Issner R, Coyne MJ, Durham T, Ku M, Truong T, Ward LD, Altshuler RC, Lin MF, Kellis M, Gingeras TR, Davis CA, Kapranov P, Dobin A, Zaleski C, Schlesinger F, Batut P, Chakrabortty S, Jha S, Lin W, Drenkow J, Wang H, Bell K, Gao H, Bell I, Dumais E, Dumais J, Antonarakis SE, Ucla C, Borel C, Guigo R, Djebali S, Lagarde J, Kingswood C, Ribeca P, Sammeth M, Alioto T, Merkel A, Tilgner H, Carninci P, Hayashizaki Y, Lassmann T, Takahashi H, Abdelhamid RF, Hannon G, Fejes-Toth K, Preall J, Gordon A, Sotirova V, Reymond A, Howald C, Graison E, Chrast J, Ruan Y, Ruan X, Shahab A, Ting Poh W, Wei CL, Crawford GE, Furey TS, Boyle AP, Sheffield NC, Song L, Shibata Y, Vales T, Winter D, Zhang Z, London D, Wang T, Birney E, Keefe D, Iyer VR, Lee BK, McDaniell RM, Liu Z, Battenhouse A, Bhinge AA, Lieb JD, Grasfeder LL, Showers KA, Giresi PG, Kim SK, Shestak C, Myers RM, Pauli F, Reddy TE, Gertz J, Partridge EC, Jain P, Sprouse RO, Bansal A, Pusey B, Muratet MA, Varley KE, Bowling KM, Newberry KM, Nesmith AS, Dilocker JA, Parker SL, Waite LL, Thibeault K, Roberts K, Absher DM, Wold B, Mortazavi A, Williams B, Marinov G, Trout D, Pepke S, King B, McCue K, Kirilusha A, DeSalvo G, Fisher-Aylor K, Amrhein H, Vielmetter J, Sherlock G, Sidow A, Batzoglou S, Rauch R, Kundaje A, Libbrecht M, Margulies EH, Parker SC, Elnitski L, Green ED, Hubbard T, Harrow J, Searle S, Kokocinski F, Aken B, Frankish A, Hunt T, Despacio-Reyes G, Kay M, Mukherjee G, Bignell A, Saunders G, Boychenko V, Van Baren M, Brown RH, Khurana E, Balasubramanian S, Zhang Z, Lam H, Cayting P, Robilotto R, Lu Z, Guigo R, Derrien T, Tanzer A, Knowles DG, Mariotti M, James Kent W, Haussler D, Harte R, Diekhans M, Kellis M, Lin M, Kheradpour P, Ernst J, Reymond A, Howald C, Graison EA, Chrast J, Tress M, Rodriguez JM, Snyder M, Landt SG, Raha D, Shi M, Euskirchen G, Grubert F, Kasowski M, Lian J, Cayting P, Lacroute P, Xu Y, Monahan H, Patacsil D, Slifer T, Yang X, Charos A, Reed B, Wu L, Auerbach RK, Habegger L, Hariharan M, Rozowsky J, Abyzov A, Weissman SM, Gerstein M, Struhl K, Lamarre-Vincent N, Lindahl-Allen M, Miotto B, Moqtaderi Z, Fleming JD, Newburger P, Farnham PJ, Frietze S, O'Geen H, Xu X, Blahnik KR, Cao AR, Iyengar S, Stamatoyannopoulos JA, Kaul R, Thurman RE, Wang H, Navas PA, Sandstrom R, Sabo PJ, Weaver M, Canfield T, Lee K, Neph S, Roach V, Reynolds A, Johnson A, Rynes E, Giste E, Vong S, Neri J, Frum T, Johnson EM, Nguyen ED, Ebersol AK, Sanchez ME, Sheffer HH, Lotakis D, Haugen E, Humbert R, Kutyavin T, Shafer T, Dekker J, Lajoie BR, Sanyal A, James Kent W, Rosenbloom KR, Dreszer TR, Raney BJ, Barber GP, Meyer LR, Sloan CA, Malladi VS, Cline MS, Learned K, Swing VK, Zweig AS, Rhead B, Fujita PA, Roskin K, Karolchik D, Kuhn RM, Haussler D, Birney E, Dunham I, Wilder SP, Keefe D, Sobral D, Herrero J, Beal K, Lukk M, Brazma A, Vaquerizas JM, Luscombe NM, Bickel PJ, Boley N, Brown JB, Li Q, Huang H, Gerstein M, Habegger L, Sboner A, Rozowsky J, Auerbach RK, Yip KY, Cheng C, Yan KK, Bhardwaj N, Wang J, Lochovsky L, Jee J, Gibson T, Leng J, Du J, Hardison RC, Harris RS, Song G, Miller W, Haussler D, Roskin K, Suh B, Wang T, Paten B, Noble WS, Hoffman MM, Buske OJ, Weng Z, Dong X, Wang J, Xi H, Tenenbaum SA, Doyle F, Penalva LO, Chittur S, Tullius TD, Parker SC, White KP, Karmakar S, Victorsen A, Jameel N, Bild N, Grossman RL, Snyder M, Landt SG, Yang X, Patacsil D, Slifer T, Dekker J, Lajoie BR, Sanyal A, Weng Z, Whitfield TW, Wang J, Collins PJ, Trinklein ND, Partridge EC, Myers RM, Giddings MC, Chen X, Khatun J, Maier C, Yu Y, Gunawardena H, Risk B, Feingold EA, Lowdon RF, Dillon LA, Good PJ, Harrow J, Searle S., Becker, Peter B, Broad Institute of MIT and Harvard, Lincoln Laboratory, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Department of Physics, Kellis, Manolis, Epstein, Charles B., Bernstein, Bradley E., Shoresh, Noam, Ernst, Jason, Mikkelsen, Tarjei Sigurd, Kheradpour, Pouya, Zhang, Xiaolan, Wang, Li, Issner, Robbyn, Coyne, Michael J., Durham, Timothy, Ku, Manching, Truong, Thanh, Ward, Lucas D., Altshuler, Robert Charles, Lin, Michael F., ENCODE Project Consortium, Antonarakis, Stylianos, and Miotto, Benoit

Subjects
RNA, Messenger/genetics, [SDV]Life Sciences [q-bio], Messenger, Genoma humà, Genome, Medical and Health Sciences, 0302 clinical medicine, Models, ddc:576.5, Biology (General), Conserved Sequence, Genetics, 0303 health sciences, General Neuroscience, RNA-Binding Proteins, Genomics, Biological Sciences, Chromatin, 3. Good health, [SDV] Life Sciences [q-bio], DNA-Binding Proteins, Gene Components, 030220 oncology & carcinogenesis, DNA methylation, Encyclopedia, HIV/AIDS, Proteïnes de la sang -- Aspectes genètics, General Agricultural and Biological Sciences, Databases, Nucleic Acid, Human, Research Article, Quality Control, Process (engineering), QH301-705.5, 1.1 Normal biological development and functioning, Computational biology, Biology, ENCODE, General Biochemistry, Genetics and Molecular Biology, Chromatin/metabolism, Vaccine Related, 03 medical and health sciences, Databases, Genetic, Underpinning research, Humans, RNA, Messenger, RNA-Binding Proteins/genetics/metabolism, Vaccine Related (AIDS), Gene, 030304 developmental biology, Internet, General Immunology and Microbiology, Nucleic Acid, Agricultural and Veterinary Sciences, Base Sequence, Models, Genetic, Genome, Human, Prevention, Human Genome, Computational Biology, DNA Methylation, ENCODE Project Consortium, Gene Expression Regulation, DNA-Binding Proteins/genetics/metabolism, RNA, Human genome, Immunization, Generic health relevance, Developmental Biology
Abstract

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome., National Human Genome Research Institute (U.S.), National Institutes of Health (U.S.)

Published
2011

45. Exogean: a framework for annotating protein-coding genes in eukaryotic genomic DNA

Author

Sarah, Djebali, Franck, Delaplace, and Hugues, Roest Crollius

Subjects
Expressed Sequence Tags, Base Sequence, Genome, Human, Research, Computational Biology, Proteins, DNA, Genomics, Sequence Analysis, DNA, ComputingMethodologies_PATTERNRECOGNITION, Genes, Sequence Analysis, Protein, Humans, Amino Acid Sequence, RNA Splice Sites, RNA, Messenger, Sequence Alignment, Software
Abstract

Background Accurate and automatic gene identification in eukaryotic genomic DNA is more than ever of crucial importance to efficiently exploit the large volume of assembled genome sequences available to the community. Automatic methods have always been considered less reliable than human expertise. This is illustrated in the EGASP project, where reference annotations against which all automatic methods are measured are generated by human annotators and experimentally verified. We hypothesized that replicating the accuracy of human annotators in an automatic method could be achieved by formalizing the rules and decisions that they use, in a mathematical formalism. Results We have developed Exogean, a flexible framework based on directed acyclic colored multigraphs (DACMs) that can represent biological objects (for example, mRNA, ESTs, protein alignments, exons) and relationships between them. Graphs are analyzed to process the information according to rules that replicate those used by human annotators. Simple individual starting objects given as input to Exogean are thus combined and synthesized into complex objects such as protein coding transcripts. Conclusion We show here, in the context of the EGASP project, that Exogean is currently the method that best reproduces protein coding gene annotations from human experts, in terms of identifying at least one exact coding sequence per gene. We discuss current limitations of the method and several avenues for improvement.

Published
2006

46. [Untitled]

Author

Sarah Djebali, Franck Delaplace, and Hugues Roest Crollius

Subjects
Genetics, 0303 health sciences, Expressed sequence tag, Genomics, Context (language use), Gene Annotation, Computational biology, Biology, Genome, 03 medical and health sciences, genomic DNA, ComputingMethodologies_PATTERNRECOGNITION, 0302 clinical medicine, 030220 oncology & carcinogenesis, Coding region, Human genome, 030304 developmental biology
Abstract

Accurate and automatic gene identification in eukaryotic genomic DNA is more than ever of crucial importance to efficiently exploit the large volume of assembled genome sequences available to the community. Automatic methods have always been considered less reliable than human expertise. This is illustrated in the EGASP project, where reference annotations against which all automatic methods are measured are generated by human annotators and experimentally verified. We hypothesized that replicating the accuracy of human annotators in an automatic method could be achieved by formalizing the rules and decisions that they use, in a mathematical formalism. We have developed Exogean, a flexible framework based on directed acyclic colored multigraphs (DACMs) that can represent biological objects (for example, mRNA, ESTs, protein alignments, exons) and relationships between them. Graphs are analyzed to process the information according to rules that replicate those used by human annotators. Simple individual starting objects given as input to Exogean are thus combined and synthesized into complex objects such as protein coding transcripts. We show here, in the context of the EGASP project, that Exogean is currently the method that best reproduces protein coding gene annotations from human experts, in terms of identifying at least one exact coding sequence per gene. We discuss current limitations of the method and several avenues for improvement.

Published
2006

47. ChimPipe: accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data

Author

Graciela Alonso, Thasso Griebel, Roderic Guigó, Alberto Rastrojo, Santiago Marco-Sola, Begoña Aguado, Bernardo Rodriguez Martin, Emilio Palumbo, Sarah Djebali, Paolo Ribeca, Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra [Barcelona] (UPF), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Ctr Nacl Anal Genom, Pirbright Institute, Consejo Superior de Investigaciones Científicas, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT), European Project: 609398,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,AGREENSKILLSPLUS(2014), Ministerio de Economía y Competitividad (España), Ministerio de Educación y Ciencia (España), Fundación Severo Ochoa, Fundación 'la Caixa', National Human Genome Research Institute (US), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Universitat Pompeu Fabra [Barcelona], and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]

Subjects
0301 basic medicine, Isoform, Oncogene Proteins, Fusion, Transcription, Genetic, [SDV]Life Sciences [q-bio], RNA-Seq, Benchmark, Fusion gene, Genome, chimera, Mice, 0302 clinical medicine, benchmark, Transcription (biology), False positive paradox, Cancer, Recombination, Genetic, Genetics, 0303 health sciences, Splice junction, Methodology Article, Benchmarks, High-Throughput Nucleotide Sequencing, isoform, Genomics, transcript, fusion gene, cancer, simulation, splice junction, Transcript, 3. Good health, 030220 oncology & carcinogenesis, DNA microarray, Simulation, Biotechnology, Computational biology, Biology, ENCODE, Chimera (genetics), 03 medical and health sciences, Chimeras, Animals, Humans, Computer Simulation, Gene, 030304 developmental biology, Sequence Analysis, RNA, Chimera, Computational Biology, Reproducibility of Results, Fusion protein, 030104 developmental biology, RNA-seq, Software
Abstract

Background: Chimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment. Results: Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated. Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role. Applying ChimPipe to human and mouse ENCODE RNA-seq data led to the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved. Conclusions: ChimPipe combines discordant paired-end reads and split-reads to detect any kind of chimeras, including those originating from polymerase read-through, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy., National Human Genome Research Institute of the National Institutes of Health; by Obra Social Fundación ’la Caixa’ under the Severo Ochoa 2014 program; by grant BIO2011-26205 from the Spanish Ministry of Economy and Competitiveness (MINECO); Centro de Excelencia Severo Ochoa 2013-2017 (SEV-2012-0208); and by grant BFU2009-09117 from the Spanish Ministery of Science and Education (MICINN)

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48. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression

Author

Cedric Notredame, Timo Lassmann, Carrie A. Davis, Thomas Derrien, Roderic Guigó, Tim Hubbard, Sarah Djebali, Julien Lagarde, Giovanni Bussotti, Gregory Guernec, Jennifer Harrow, José M. González, Yijun Ruan, Ramin Shiekhattar, Xiaoan Ruan, Angelika Merkel, Hagen Tilgner, David C. Martin, Mark G. Thomas, James B. Brown, Leonard Lipovich, Rory Johnson, Lavanya Veeravalli, Thomas R. Gingeras, Piero Carninci, Andrea Tanzer, David G. Knowles, Bioinformatics and Genomics, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra [Barcelona], Laboratoire de Physiologie et Génomique des Poissons (LPGP), Institut National de la Recherche Agronomique (INRA)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut Fédératif de Recherche - Génétique Fonctionnelle Agronomie et Santé (IFR 140 GFAS), Plateforme bioinformatique GenOuest [Rennes], Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Plateforme Génomique Santé Biogenouest®-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec, Agency for Science, Technology and Research, Genome, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research Genome, Riken Omics Science Center, Riken Yokohama Institute, Department of Statistics, University of California, Center for Molecular Medicine and Genetics, Wayne State University, Wellcome Trust Genome Campus, The Wellcome Trust Sanger Institute [Cambridge], Cold Spring Harbor, Cold Spring Harbor Laboratory, The Wistar Institute, Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra [Barcelona] (UPF), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Wayne State University [Detroit], Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Recherche Agronomique (INRA), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and University of California (UC)

Subjects
Primates, Resource, Transcription, Genetic, Computational biology, Biology, Evolution, Molecular, Histones, Open Reading Frames, 03 medical and health sciences, 0302 clinical medicine, Databases, Genetic, Genetics, Animals, Cluster Analysis, Humans, RNA, Messenger, RNA Processing, Post-Transcriptional, Selection, Genetic, Gene, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Genètica -- Bases de dades, Genetics (clinical), 030304 developmental biology, Cell Nucleus, Regulation of gene expression, 0303 health sciences, GENCODE, Gene Expression Profiling, Alternative splicing, Intron, Molecular Sequence Annotation, Exons, RNA no missatgers, Gene expression profiling, Alternative Splicing, Gene Expression Regulation, Organ Specificity, 030220 oncology & carcinogenesis, RNA splicing, RNA, Long Noncoding, Human genome, RNA Splice Sites
Abstract

The human genome contains many thousands of long noncoding RNAs (lncRNAs). While several studies have demonstrated compelling biological and disease roles for individual examples, analytical and experimental approaches to investigate these genes have been hampered by the lack of comprehensive lncRNA annotation. Here, we present and analyze the most complete human lncRNA annotation to date, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts. Our analyses indicate that lncRNAs are generated through pathways similar to that of protein-coding genes, with similar histone-modification profiles, splicing signals, and exon/intron lengths. In contrast to protein-coding genes, however, lncRNAs display a striking bias toward two-exon transcripts, they are predominantly localized in the chromatin and nucleus, and a fraction appear to be preferentially processed into small RNAs. They are under stronger selective pressure than neutrally evolving sequences-particularly in their promoter regions, which display levels of selection comparable to protein-coding genes. Importantly, about one-third seem to have arisen within the primate lineage. Comprehensive analysis of their expression in multiple human organs and brain regions shows that lncRNAs are generally lower expressed than protein-coding genes, and display more tissue-specific expression patterns, with a large fraction of tissue-specific lncRNAs expressed in the brain. Expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes. This GENCODE annotation represents a valuable resource for future studies of lncRNAs. This work has been carried out under grants RD07/0067/0012, BIO2006-03380, and CSD2007-00050 from the Spanish Ministry of Science, grant SGR-1430 from the Catalan Government, grants 1U54HG004557-01 and 1U54HG004555-01 from the National Institutes of Health, and INBISCIII from Instituto de Salud Carlos III and FEDER. This research project has been cofinanced by the European Commission, within the 7th Framework Programme, Grant Agreement KBBE-2A-222664 (‘‘Quantomics’’). C.N. is financed by the Plan Nacional BFU2008-00419. G.B. is supported by the La Caixa Ph.D. program

49. Exogean: a framework for annotating protein-coding genes in eukaryotic genomic DNA

Author

Sarah Djebali, Delaplace, F., Crollius, H. R., Duponchelle, Martine, Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), DYnamique et Organisation des GENomes - Equipe de l'IBENS (DYOGEN), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Régulation de l'expression génétique (REG), Département de Biologie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-GT]Computer Science [cs]/Computer Science and Game Theory [cs.GT], [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SCCO.COMP]Cognitive science/Computer science, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.MHEP.UN]Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology
Abstract

International audience; Background Accurate and automatic gene identification in eukaryotic genomic DNA is more than ever of crucial importance to efficiently exploit the large volume of assembled genome sequences available to the community. Automatic methods have always been considered less reliable than human expertise. This is illustrated in the EGASP project, where reference annotations against which all automatic methods are measured are generated by human annotators and experimentally verified. We hypothesized that replicating the accuracy of human annotators in an automatic method could be achieved by formalizing the rules and decisions that they use, in a mathematical formalism. Results We have developed Exogean, a flexible framework based on directed acyclic colored multigraphs (DACMs) that can represent biological objects (for example, mRNA, ESTs, protein alignments, exons) and relationships between them. Graphs are analyzed to process the information according to rules that replicate those used by human annotators. Simple individual starting objects given as input to Exogean are thus combined and synthesized into complex objects such as protein coding transcripts. Conclusion We show here, in the context of the EGASP project, that Exogean is currently the method that best reproduces protein coding gene annotations from human experts, in terms of identifying at least one exact coding sequence per gene. We discuss current limitations of the method and several avenues for improvement.

50. Efficient targeted transcript discovery via array-based normalization of RACE libraries

Author

Sarah Djebali, Philipp Kapranov, Sylvain Foissac, Julien Lagarde, Alexandre Reymond, Catherine Ucla, Carine Wyss, Jorg Drenkow, Erica Dumais, Ryan R Murray, Chenwei Lin, David Szeto, France Denoeud, Miquel Calvo, Adam Frankish, Jennifer Harrow, Periklis Makrythanasis, Marc Vidal, Kourosh Salehi-Ashtiani, Stylianos E Antonarakis, Thomas R Gingeras, Roderic Guigó, Universitat Pompeu Fabra [Barcelona], Affymetrix Inc., Laboratoire de Génétique Cellulaire (LGC), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Université de Lausanne (UNIL), University of Geneva, Harvard University, University of Barcelona, The Wellcome Trust Sanger Institute [Cambridge], Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra [Barcelona] (UPF), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
Transcription, Genetic, [SDV]Life Sciences [q-bio], Oligonucleotide Array Sequence Analysis/methods, Genètica -- Tècnica, Biochemistry, project, 0302 clinical medicine, Rapid amplification of cDNA ends, Nucleic Acid Amplification Techniques/ methods, collection, ddc:576.5, Genomic library, Cloning, Molecular, Genetics, 0303 health sciences, education.field_of_study, Perfil d'expressió gènica, Reverse Transcriptase Polymerase Chain Reaction, element, Chromosomes, Human, Pair 21/genetics, Exons, Alternative Splicing, Chromosomes, Human, Pair 22/genetics, DNA, Complementary/genetics, Gene Expression Profiling/methods, Gene Library, Genome, Human, Humans, Molecular Sequence Data, Nucleic Acid Amplification Techniques/methods, Protein Isoforms/genetics, RNA/genetics, 3. Good health, annotation, 030220 oncology & carcinogenesis, cap analysis, Biotechnology, ADN complementari, Population, Biology, Article, 03 medical and health sciences, human genome, Bancs de gens, education, cdna library, Molecular Biology, mouse, 030304 developmental biology, Tiling array, cDNA library, Alternative splicing, Cell Biology, Nucleic acid amplification technique, Gene Expression Profiling/ methods, DNA, Complementary/ genetics, gene expression, identification, RNA, Human genome, RNA/ genetics
Abstract

Rapid amplification of cDNA ends (RACE) is a widely used approach for transcript identification. Random clone selection from the RACE mixture, however, is an ineffective sampling strategy if the dynamic range of transcript abundances is large. To improve sampling efficiency of human transcripts, we hybridized the products of the RACE reaction onto tiling arrays and used the detected exons to delineate a series of reverse-transcriptase (RT)-PCRs, through which the original RACE transcript population was segregated into simpler transcript populations. We independently cloned the products and sequenced randomly selected clones. This approach, RACEarray, is superior to direct cloning and sequencing of RACE products because it specifically targets new transcripts and often results in overall normalization of transcript abundance. We show theoretically and experimentally that this strategy leads indeed to efficient sampling of new transcripts, and we investigated multiplexing the strategy by pooling RACE reactions from multiple interrogated loci before hybridization. The project at IMIM, CRG, the Universities of Lausanne and Geneva, and Affymetrix is supported by grants U01HG003150 and U01HG003147 from the National Human Genome Research Institute, NIH. Institut Municipal d’Investigació Mèdica and Center for Genomic Regulation (CRG) have also been funded by grant BIO2006-03380 from the Spanish Ministry of Education and Science and from the European BioSapiens Consortium. Affymetrix has also received funds from the National Cancer Institute, NIH, under contract no. N01-CO-12400 and by Affymetrix, Inc. The portion of this work carried out at Center for Cancer Systems Biology was funded by a grant from the Ellison Foundation (awarded to MV) and as Institute Sponsored Research from the Dana Farber Cancer Institute Strategic Initiative

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