9 results on '"Jen Harrow"'
Search Results
2. Multiple laboratory mouse reference genomes define strain specific haplotypes and novel functional loci
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Dent Earl, Monica Abrudan, Benedict Paten, Adam Frankish, Lesley Shirley, Cristina Sisu, Ian T. Fiddes, Mark Gerstein, James G. R. Gilbert, Mark G. Thomas, Anne Czechanski, Duncan T. Odom, William Chow, Stephan C. Collins, Clayton E. Mathews, Mark Diekhans, Ruth Bennett, Jane E. Loveland, David J. Adams, Jingtao Lilue, Beiyuan Fu, Dirk-Dominic Dolle, Fengtang Yang, Laura G. Reinholdt, Glen Threadgold, Anne C. Ferguson-Smith, Jonathan Wood, Kim Wong, Leo Goodstadt, Paul R. Muir, Thomas M. Keane, Phan Sk, Jonathan Flint, Naomi R Park, Richard Mott, Joel Armstrong, Thybert D, Jen Harrow, Petr Danecek, Marcela K. Sjoberg-Herrera, Sarah Pelan, Anthony G. Doran, Kerstin Howe, Charles A. Steward, Mario Stanke, Binnaz Yalcin, Joanna Collins, Lelliott C, Matthew Dunn, Fabio C. P. Navarro, Michael A. Quail, Paul Flicek, James Torrance, Richard Durbin, Köenig S, Lars Romoth, and Mikhail Kolmogorov
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Genetics ,0303 health sciences ,Strain (biology) ,Haplotype ,Genomics ,Retrotransposon ,Biology ,Genome ,03 medical and health sciences ,0302 clinical medicine ,Genome Reference Consortium ,Gene ,030217 neurology & neurosurgery ,030304 developmental biology ,Reference genome - Abstract
The most commonly employed mammalian model organism is the laboratory mouse. A wide variety of genetically diverse inbred mouse strains, representing distinct physiological states, disease susceptibilities, and biological mechanisms have been developed over the last century. We report full length draft de novo genome assemblies for 16 of the most widely used inbred strains and reveal for the first time extensive strain-specific haplotype variation. We identify and characterise 2,567 regions on the current Genome Reference Consortium mouse reference genome exhibiting the greatest sequence diversity between strains. These regions are enriched for genes involved in defence and immunity, and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. Several immune related loci, some in previously identified QTLs for disease response have novel haplotypes not present in the reference that may explain the phenotype. We used these genomes to improve the mouse reference genome resulting in the completion of 10 new gene structures, and 62 new coding loci were added to the reference genome annotation. Notably this high quality collection of genomes revealed a previously unannotated gene (Efcab3-like) encoding 5,874 amino acids, one of the largest known in the rodent lineage. Interestingly, Efcab3-like−/− mice exhibit severe size anomalies in four regions of the brain suggesting a mechanism of Efcab3-like regulating brain development.
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- 2018
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3. Comparative Analysis of the Transcriptome across Distant Species
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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
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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.
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- 2014
4. The GENCODE exome: sequencing the complete human exome
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Daniel J. Turner, Alison J. Coffey, Anna-Elina Lehesjoki, Aarno Palotie, Eleanor Drury, Priit Palta, Christopher J. Joyce, Sarah E. Hunt, Tim Hubbard, Carol Scott, Maria S Calafato, Emily M LeProust, Jen Harrow, and Felix Kokocinski
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Sequence analysis ,Short Report ,Genomics ,Biology ,Polymorphism, Single Nucleotide ,Genome ,Open Reading Frames ,03 medical and health sciences ,0302 clinical medicine ,Consensus Sequence ,Databases, Genetic ,Genetics ,Humans ,Exome ,Genetics (clinical) ,Exome sequencing ,030304 developmental biology ,resequencing ,GENCODE ,0303 health sciences ,Genome, Human ,Computational Biology ,Exons ,Sequence Analysis, DNA ,human exome ,Human genetics ,3. Good health ,030220 oncology & carcinogenesis ,Human genome - Abstract
Sequencing the coding regions, the exome, of the human genome is one of the major current strategies to identify low frequency and rare variants associated with human disease traits. So far, the most widely used commercial exome capture reagents have mainly targeted the consensus coding sequence (CCDS) database. We report the design of an extended set of targets for capturing the complete human exome, based on annotation from the GENCODE consortium. The extended set covers an additional 5594 genes and 10.3 Mb compared with the current CCDS-based sets. The additional regions include potential disease genes previously inaccessible to exome resequencing studies, such as 43 genes linked to ion channel activity and 70 genes linked to protein kinase activity. In total, the new GENCODE exome set developed here covers 47.9 Mb and performed well in sequence capture experiments. In the sample set used in this study, we identified over 5000 SNP variants more in the GENCODE exome target (24%) than in the CCDS-based exome sequencing.
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- 2011
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5. Genome-wide end-sequenced BAC resources for the NOD/MrkTac☆ and NOD/ShiLtJ☆☆ mouse genomes
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Omid M. Gulban, Yoshihide Hayashizaki, Michael A. Quail, Paul A. Lyons, James K. Bonfield, John A. Todd, Jayne S. Danska, Jane Rogers, Robert L. Davies, Tim Hubbard, Bob Plumb, Jen Harrow, Thomas M. Keane, Charles A. Steward, Michael Nefedov, David J. Adams, Stephen Rice, Tony Cox, Matthew C. Jones, Linda S. Wicker, Pieter J. de Jong, and Sean Humphray
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Male ,Chromosomes, Artificial, Bacterial ,DNA, Complementary ,IDD ,Non-obese diabetic (NOD) ,Molecular Sequence Data ,NOD/MrkTac ,Mice, Inbred Strains ,Genomics ,Nod ,Biology ,Genome ,Article ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Mice, Inbred NOD ,Genetics ,NOD/ShiLtJ ,Animals ,Ensembl ,X chromosome ,030304 developmental biology ,NOD mice ,Whole genome sequencing ,0303 health sciences ,Bacterial artificial chromosome ,Mouse genome ,Sequence Analysis, DNA ,3. Good health ,Type 1 diabetes ,T1D ,Insulin-dependent diabetes ,030215 immunology - Abstract
Non-obese diabetic (NOD) mice spontaneously develop type 1 diabetes (T1D) due to the progressive loss of insulin-secreting β-cells by an autoimmune driven process. NOD mice represent a valuable tool for studying the genetics of T1D and for evaluating therapeutic interventions. Here we describe the development and characterization by end-sequencing of bacterial artificial chromosome (BAC) libraries derived from NOD/MrkTac (DIL NOD) and NOD/ShiLtJ (CHORI-29), two commonly used NOD substrains. The DIL NOD library is composed of 196,032 BACs and the CHORI-29 library is composed of 110,976 BACs. The average depth of genome coverage of the DIL NOD library, estimated from mapping the BAC end-sequences to the reference mouse genome sequence, was 7.1-fold across the autosomes and 6.6-fold across the X chromosome. Clones from this library have an average insert size of 150 kb and map to over 95.6% of the reference mouse genome assembly (NCBIm37), covering 98.8% of Ensembl mouse genes. By the same metric, the CHORI-29 library has an average depth over the autosomes of 5.0-fold and 2.8-fold coverage of the X chromosome, the reduced X chromosome coverage being due to the use of a male donor for this library. Clones from this library have an average insert size of 205 kb and map to 93.9% of the reference mouse genome assembly, covering 95.7% of Ensembl genes. We have identified and validated 191,841 single nucleotide polymorphisms (SNPs) for DIL NOD and 114,380 SNPs for CHORI-29. In total we generated 229,736,133 bp of sequence for the DIL NOD and 121,963,211 bp for the CHORI-29. These BAC libraries represent a powerful resource for functional studies, such as gene targeting in NOD embryonic stem (ES) cell lines, and for sequencing and mapping experiments.
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- 2010
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6. The genomic sequence and analysis of the swine major histocompatibility complex
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Harminder Sehra, Kerstin Howe, Helen Beasley, Asako Ando, Takashi Shiina, Atsuko Shigenari, Patrick Chardon, Jane Rogers, James G. R. Gilbert, E. Hart, Christine Renard, Penny Coggill, Hidetoshi Inoko, Jen Harrow, Stephan Beck, Sarah Sims, ProdInra, Migration, Laboratoire de radiobiologie et d'étude du génome (LREG), Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Recherche Agronomique (INRA)
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Male ,pig ,Chromosomes, Artificial, Bacterial ,Comparative sequence analysis ,Adaptive immune system ,Swine ,Evolution ,[SDV]Life Sciences [q-bio] ,Pseudogene ,Centromere ,Biology ,Major histocompatibility complex ,Genome ,Contig Mapping ,03 medical and health sciences ,0302 clinical medicine ,HLA Antigens ,Molecular evolution ,Putative gene ,Genetics ,Animals ,Humans ,Gene ,Phylogeny ,030304 developmental biology ,0303 health sciences ,Histocompatibility Antigens Class I ,Histocompatibility Antigens Class II ,Centromere repositioning ,major histocompatibility complex ,Histocompatibility ,[SDV] Life Sciences [q-bio] ,biology.protein ,dna sequence ,Swine leukocyte antigen (SLA) complex ,030215 immunology - Abstract
We describe the generation and analysis of an integrated sequence map of a 2.4-Mb region of pig chromosome 7, comprising the classical class I region, the extended and classical class II regions, and the class III region of the major histocompatibility complex (MHC), also known as swine leukocyte antigen (SLA) complex. We have identified and manually annotated 151 loci, of which 121 are known genes (predicted to be functional), 18 are pseudogenes, 8 are novel CDS loci, 3 are novel transcripts, and 1 is a putative gene. Nearly all of these loci have homologues in other mammalian genomes but orthologues could be identified with confidence for only 123 genes. The 28 genes (including all the SLA class I genes) for which unambiguous orthology to genes within the human reference MHC could not be established are of particular interest with respect to porcine-specific MHC function and evolution. We have compared the porcine MHC to other mammalian MHC regions and identified the differences between them. In comparison to the human MHC, the main differences include the absence of HLA-A and other class I-like loci, the absence of HLA-DP-like loci, and the separation of the extended and classical class II regions from the rest of the MHC by insertion of the centromere. We show that the centromere insertion has occurred within a cluster of BTNL genes located at the boundary of the class II and III regions, which might have resulted in the loss of an orthologue to human C6orf10 from this region.
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- 2006
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7. The pig X and Y chromosomes: structure, sequence and evolution
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Benjamin M. Skinner, Jonathan Wood, Philip Howden, Carol Churcher, Peter J.I. Ellis, Carole A. Sargent, Daria Gordon, William Chow, Denise Carvalho-Silva, Nabeel A. Affara, Giselle Kerry, James G. R. Gilbert, Bee Ling Ng, Heidi Hauser, Glen Threadgold, Toby Hunt, Thomas Wileman, Javier Herrero, Kerstin Howe, Jane E. Loveland, Jo Harley, Chris Tyler-Smith, William Cheng, Siobhan Austin-Guest, Beiyuan Fu, Kim Lachani, Sandra Louzada, Matthew Hardy, Matthew Dunn, Darren Grafham, Daniel Kelly, James Kerwin, Kathryn Beal, Jen Harrow, Fengtang Yang, Skinner, Benjamin [0000-0002-7152-1167], Sargent, Carole [0000-0002-4205-3085], and Apollo - University of Cambridge Repository
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Resource ,0301 basic medicine ,Male ,X Chromosome ,Swine ,Molecular Sequence Data ,Gene Conversion ,Gene Expression ,Genomics ,Biology ,Y chromosome ,Evolution, Molecular ,03 medical and health sciences ,Chromosome 16 ,Dogs ,0302 clinical medicine ,Chromosome 19 ,Y Chromosome ,Gene Order ,Genetics ,Animals ,Humans ,Gene family ,Gene conversion ,QH426 ,Gene ,Genetics (clinical) ,X chromosome ,Gene Library ,030304 developmental biology ,QL ,0303 health sciences ,Base Sequence ,Chromosome ,Sequence Analysis, DNA ,Chromosomes, Mammalian ,Chromosome 17 (human) ,Fosmid ,030104 developmental biology ,Chromosome 4 ,Chromosome 3 ,Cats ,Female ,Chromosome 21 ,Sequence Alignment ,030217 neurology & neurosurgery - Abstract
We have generated an improved assembly and gene annotation of the pig X chromosome, and a first draft assembly of the pig Y chromosome, by sequencing BAC and fosmid clones, and incorporating information from optical mapping and fibre-FISH. The X chromosome carries 1,014 annotated genes, 689 of which are protein-coding. Gene order closely matches that found in Primates (including humans) and Carnivores (including cats and dogs), which is inferred to be ancestral. Nevertheless, several protein-coding genes present on the human X chromosome were absent from the pig (e.g. the cancer/testis antigen family) or inactive (e.g. AWAT1), and 38 pig-specific X-chromosomal genes were annotated, 22 of which were olfactory receptors. The pig Y chromosome assembly focussed on two clusters of male-specific low-copy number genes, separated by an ampliconic region including the HSFY gene family, which together make up most of the short arm. Both clusters contain palindromes with high sequence identity, presumably maintained by gene conversion. The long arm of the chromosome is almost entirely repetitive, containing previously characterised sequences. Many of the ancestral X-related genes previously reported in at least one mammalian Y chromosome are represented either as active genes or partial sequences. This sequencing project has allowed us to identify genes - both single copy and amplified - on the pig Y, to compare the pig X and Y chromosomes for homologous sequences, and thereby to reveal mechanisms underlying pig X and Y chromosome evolution.
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- 2014
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8. RNAcentral: A vision for an international database of RNA sequences
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Simon Moxon, Marcel E. Dinger, Guy Cochrane, Mathew W. Wright, Kim D. Pruitt, Kelly P. Williams, Paul J. Kersey, Alex Bateman, Albert J. Vilella, Shipra Agrawal, Janusz M. Bujnicki, Krystyna A. Kelly, Todd M. Lowe, James R. Cole, Daniel Gautheret, Manja Marz, Jan Hinnerk Vogel, Javier Herrero, Peter F. Stadler, Jen Harrow, Paul P. Gardner, Anton J. Enright, Ana Kozomara, Elspeth A. Bruford, Ian Holmes, Tore Samuelsson, Hsien D A Huang, Christian W Zwieb, Ewan Birney, Sam Griffiths-Jones, Institut de génétique et microbiologie [Orsay] (IGM), and Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)
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Genetics ,0303 health sciences ,Sequence database ,Base Sequence ,RNA ,Biology ,Non-coding RNA ,Data science ,Variety (cybernetics) ,03 medical and health sciences ,0302 clinical medicine ,Resource (project management) ,[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology ,030220 oncology & carcinogenesis ,Key (cryptography) ,Animals ,Humans ,[SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology ,UniProt ,Databases, Nucleic Acid ,Molecular Biology ,Gene ,Letter to the Editor ,030304 developmental biology - Abstract
International audience; During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.
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- 2011
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9. The GENCODE human gene set
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S. Donaldson, James G. R. Gilbert, Denise Carvalho-Silva, Roderic Guigó, Michael F. Lin, If H. A. Barnes, Alfonso Valencia, Toby Hunt, Bronwen Aken, Thomas Derrien, Rachel A. Harte, Adam Frankish, Michael R. Brent, Manolis Kellis, Jessica Vamathevan, Jonathan M. Mudge, David Lloyd, Laurens G. Wilming, M. Kay, Alexandre Reymond, Claire Davidson, Mark Diekhans, S. Searle, Amonida Zadissa, Tim Hubbard, Jen Harrow, Felix Kokocinski, M. van Baren, Catherine E. Snow, Mark Gerstein, Jane E. Loveland, Cédric Howald, Alexandra Bignell, Michael L. Tress, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Kellis, Manolis, and Lin, M.
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0303 health sciences ,GENCODE ,030305 genetics & heredity ,Genomics ,Computational biology ,Biology ,Bioinformatics ,Ncrna gene ,Genome ,Human genetics ,03 medical and health sciences ,Manual annotation ,Human evolution ,Poster Presentation ,Gene ,030304 developmental biology - Abstract
This article is part of the supplement: Beyond the Genome: The true gene count, human evolution and disease genomics, Boston, MA, USA. 11-13 October 2010., The GENCODE consortium is a sub group of the ENCODE consortium. Its aim is to provide complete annotation of genes in the human genome including protein-coding loci, non-coding loci and pseudogenes, based on experimental evidence. The final aim is for the HAVANA team to manually annotate the complete genome. This is a time-consuming process which will be completed over the course of the ENCODE project. Currently, to provide a set of annotation covering the complete genome, rather than just the regions that have been manually annotated, a merge of manual annotation from HAVANA with automatic annotation from the Ensembl automatically annotated gene set is created. This process also adds unique full-length CDS predictions from the Ensembl protein coding set into manually annotated genes, to provide the most complete up to date annotation of the genome possible. Also included in the set are short and long ncRNA genes predicted by the Ensembl prediction pipelines and a consensus set of pseudogene predictions agreed between Havana, Yale and UCSC. The CCDS set is also fully represented within the GENCODE set. The GENCODE set is the default annotation available in Ensembl and is also available in the UCSC genome browser. All the annotation is tagged as to whether it is produced by manual annotation alone, automatic annotation alone, or by both approaches. We are currently working to provide confidence levels for annotation, based on depth and type of evidence supporting it.
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- 2010
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