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1. ABHD11, a new diacylglycerol lipase involved in weight gain regulation.

Author

Johanna Escoubet, Mireille Kenigsberg, Murielle Derock, Veeranagouda Yaligara, Marie-Dominique Bock, Sandrine Roche, Florence Massey, Hélène de Foucauld, Charles Bettembourg, Anne Olivier, Antoine Berthemy, Joël Capdevielle, Richard Legoux, Eric Perret, Armelle Buzy, Pascale Chardenot, Valérie Destelle, Aurélie Leroy, Christophe Cahours, Sandrine Teixeira, Patrick Juvet, Pascal Gauthier, Michaël Leguet, Laurence Rocheteau-Beaujouan, Marie-Agnès Chatoux, Willy Deshayes, Margerie Clement, Mostafa Kabiri, Cécile Orsini, Vincent Mikol, Michel Didier, and Jean-Claude Guillemot

Subjects
Medicine, Science
Abstract

Obesity epidemic continues to spread and obesity rates are increasing in the world. In addition to public health effort to reduce obesity, there is a need to better understand the underlying biology to enable more effective treatment and the discovery of new pharmacological agents. Abhydrolase domain-containing protein 11 (ABHD11) is a serine hydrolase enzyme, localized in mitochondria, that can synthesize the endocannabinoid 2-arachidonoyl glycerol (2AG) in vitro. In vivo preclinical studies demonstrated that knock-out ABHD11 mice have a similar 2AG level as WT mice and exhibit a lean metabolic phenotype. Such mice resist to weight gain in Diet Induced Obesity studies (DIO) and display normal biochemical plasma parameters. Metabolic and transcriptomic analyses on serum and tissues of ABHD11 KO mice from DIO studies show a modulation in bile salts associated with reduced fat intestinal absorption. These data suggest that modulating ABHD11 signaling pathway could be of therapeutic value for the treatment of metabolic disorders.

Published
2020
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2. Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI.

Author

Charles Bettembourg, Christian Diot, and Olivier Dameron

Subjects
Medicine, Science
Abstract

The analysis of gene annotations referencing back to Gene Ontology plays an important role in the interpretation of high-throughput experiments results. This analysis typically involves semantic similarity and particularity measures that quantify the importance of the Gene Ontology annotations. However, there is currently no sound method supporting the interpretation of the similarity and particularity values in order to determine whether two genes are similar or whether one gene has some significant particular function. Interpretation is frequently based either on an implicit threshold, or an arbitrary one (typically 0.5). Here we investigate a method for determining thresholds supporting the interpretation of the results of a semantic comparison.We propose a method for determining the optimal similarity threshold by minimizing the proportions of false-positive and false-negative similarity matches. We compared the distributions of the similarity values of pairs of similar genes and pairs of non-similar genes. These comparisons were performed separately for all three branches of the Gene Ontology. In all situations, we found overlap between the similar and the non-similar distributions, indicating that some similar genes had a similarity value lower than the similarity value of some non-similar genes. We then extend this method to the semantic particularity measure and to a similarity measure applied to the ChEBI ontology. Thresholds were evaluated over the whole HomoloGene database. For each group of homologous genes, we computed all the similarity and particularity values between pairs of genes. Finally, we focused on the PPAR multigene family to show that the similarity and particularity patterns obtained with our thresholds were better at discriminating orthologs and paralogs than those obtained using default thresholds.We developed a method for determining optimal semantic similarity and particularity thresholds. We applied this method on the GO and ChEBI ontologies. Qualitative analysis using the thresholds on the PPAR multigene family yielded biologically-relevant patterns.

Published
2015
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3. Semantic particularity measure for functional characterization of gene sets using gene ontology.

Author

Charles Bettembourg, Christian Diot, and Olivier Dameron

Subjects
Medicine, Science
Abstract

BACKGROUND: Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity. RESULTS: We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure. CONCLUSION: Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.

Published
2014
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4. Measuring the evolution of ontology complexity: the gene ontology case study.

Author

Olivier Dameron, Charles Bettembourg, and Nolwenn Le Meur

Subjects
Medicine, Science
Abstract

Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure. The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred. The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable.

Published
2013
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5. The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes.

Author

Marion Ouedraogo, Charles Bettembourg, Anthony Bretaudeau, Olivier Sallou, Christian Diot, Olivier Demeure, and Frédéric Lecerf

Subjects
Medicine, Science
Abstract

BACKGROUND: There has been a surge in studies linking genome structure and gene expression, with special focus on duplicated genes. Although initially duplicated from the same sequence, duplicated genes can diverge strongly over evolution and take on different functions or regulated expression. However, information on the function and expression of duplicated genes remains sparse. Identifying groups of duplicated genes in different genomes and characterizing their expression and function would therefore be of great interest to the research community. The 'Duplicated Genes Database' (DGD) was developed for this purpose. METHODOLOGY: Nine species were included in the DGD. For each species, BLAST analyses were conducted on peptide sequences corresponding to the genes mapped on a same chromosome. Groups of duplicated genes were defined based on these pairwise BLAST comparisons and the genomic location of the genes. For each group, Pearson correlations between gene expression data and semantic similarities between functional GO annotations were also computed when the relevant information was available. CONCLUSIONS: The Duplicated Gene Database provides a list of co-localised and duplicated genes for several species with the available gene co-expression level and semantic similarity value of functional annotation. Adding these data to the groups of duplicated genes provides biological information that can prove useful to gene expression analyses. The Duplicated Gene Database can be freely accessed through the DGD website at http://dgd.genouest.org.

Published
2012
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7. A2Sign: Agnostic algorithms for signatures - a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution

Author

Galina Boldina, Corinne Rocher, George Luta, Franck Auge, Charles Bettembourg, and Paul Fogel

Subjects
Statistics and Probability, Supplementary data, Tensor factorization, Computer science, Python (programming language), Biochemistry, Biological materials, Computer Science Applications, Transcriptome, Computational Mathematics, Identification (information), Computational Theory and Mathematics, Deconvolution, Molecular Biology, computer, Algorithm, computer.programming_language
Abstract

Motivation Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue. Results We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data. Availability and implementation All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021

8. Computational modelling of the cellular interplay in Rheumatoid Arthritis. Deciphering the role of innate and adaptive immunity in cartilage destruction and bone erosion

Author

Naouel Zerrouk, Charles Bettembourg, and Anna Niarakis

Subjects
Rheumatoid Arthritis - Computational modeling - cell-specific maps - multicellular model - bone erosion - Cartilage damage - inflammation - Immune cells - Synovial fibroblasts - Osteoblasts - Osteoclasts
Abstract

Immune dysregulation was first implicated in the pathogenesis of Rheumatoid Arthritis (RA) by the discovery of anti-immunoglobulin G (IgG) antibodies known as rheumatoid factors. However, concepts of how immune responses contribute to disease have evolved dramatically over the last 50 years. Many cells and their cytokines play critical roles in the development of RA. The synovial compartment is infiltrated by leukocytes and the synovial fluid is inundated with pro-inflammatory mediators that are produced to induce an inflammatory cascade, which is characterized by interactions of fibroblast-like synoviocytes with the cells of the innate immune system, including monocytes, macrophages, mast cells, dendritic cell as well as cells of the adaptive immune system such as T cells and B cells. The fulminant stage contains hyperplastic synovium, cartilage damage, bone erosion, and systemic consequence. The objective of my project is to construct a computational model able to decipher the interplay between cells of the innate and adaptive immunity in RA, that eventually leads to bone and cartilage breakdown. To do so, we will start by creating separate maps for T cells, B cells, macrophages, fibroblasts, osteoblasts and osteoclasts. We will use different data mining tools, appropriate data bases such as KEGG (Kanehisa et al, 2000), REACTOME (Fabregat et al, 2018) as well as internal data generated within Sanofi. We will exploit the graph editor CellDesigner (Funahashi et al, 2003) and the platform Minerva (Gawron et al, 2016) for automatic annotation and reference of the cell-specific maps. We will benefit greatly from a global, fully annotated RA specific map (Singh et al, 2018, Singh et al, 2020). This map features interactions implicated in RA coming from various cell types, but due to the extensive annotations, the user can opt for cell-specific interactions and extract the corresponding network. We are also going to use public datasets of expression data (microarrays, RNAseq, RNAseq single cell), data concerning metabolic pathways from MetaCyc and Sanofi’s proprietary datasets to enrich and expand existing pathway resources. These maps will be used to generate cell-specific dynamic models using the tool CaSQ (Aghamiri et al, 2020). The next step is the creation of a multicellular model to understand how the different cells interact, contributing to the emergent behavior of the system. We will prioritize signature pathways for each cell type and combine them to build a logical model that will represent the intra- and intercellular relationships.

Published
2020
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9. ABHD11, a new diacylglycerol lipase involved in weight gain regulation

Author

Laurence Rocheteau-Beaujouan, Michel Didier, Charles Bettembourg, Sandrine Teixeira, Valérie Destelle, Sandrine Roche, Armelle Buzy, Joël Capdevielle, Johanna Escoubet, Aurélie Leroy, Margerie Clement, Christophe Cahours, Helene de Foucauld, Mireille Kenigsberg, Richard Legoux, Vincent Mikol, Murielle Derock, Cécile Orsini, Eric Perret, Willy Deshayes, Marie-Dominique Bock, Michaël Leguet, Mostafa Kabiri, Jean-Claude Guillemot, Antoine Berthemy, Florence Massey, Anne Olivier, Veeranagouda Yaligara, Pascal Gauthier, Marie-Agnès Chatoux, Pascale Chardenot, and Patrick Juvet

Subjects
0301 basic medicine, Diacylglycerol lipase, Physiology, Hydrolases, medicine.medical_treatment, Weight Gain, Biochemistry, Intestinal absorption, Feces, Gene Knockout Techniques, Mice, 0302 clinical medicine, Endocrinology, Mathematical and Statistical Techniques, Medicine and Health Sciences, Bile, Insulin, Lipases, Energy-Producing Organelles, Multidisciplinary, biology, Statistics, Serine hydrolase, Genomics, Endocannabinoid system, Lipids, Mitochondria, Body Fluids, Enzymes, Physical Sciences, MCF-7 Cells, Medicine, Signal transduction, Cellular Structures and Organelles, Anatomy, Transcriptome Analysis, Signal Transduction, Research Article, medicine.medical_specialty, Science, Bioenergetics, Research and Analysis Methods, Gene Expression Regulation, Enzymologic, 03 medical and health sciences, In vivo, Internal medicine, medicine, Genetics, Animals, Humans, Statistical Methods, Nutrition, Diabetic Endocrinology, Analysis of Variance, Gene Expression Profiling, Biology and Life Sciences, Proteins, Computational Biology, Cell Biology, Genome Analysis, In vitro, Hormones, Diet, 030104 developmental biology, biology.protein, Enzymology, Serine Proteases, 030217 neurology & neurosurgery, Mathematics
Abstract

Obesity epidemic continues to spread and obesity rates are increasing in the world. In addition to public health effort to reduce obesity, there is a need to better understand the underlying biology to enable more effective treatment and the discovery of new pharmacological agents. Abhydrolase domain-containing protein 11 (ABHD11) is a serine hydrolase enzyme, localized in mitochondria, that can synthesize the endocannabinoid 2-arachidonoyl glycerol (2AG) in vitro. In vivo preclinical studies demonstrated that knock-out ABHD11 mice have a similar 2AG level as WT mice and exhibit a lean metabolic phenotype. Such mice resist to weight gain in Diet Induced Obesity studies (DIO) and display normal biochemical plasma parameters. Metabolic and transcriptomic analyses on serum and tissues of ABHD11 KO mice from DIO studies show a modulation in bile salts associated with reduced fat intestinal absorption. These data suggest that modulating ABHD11 signaling pathway could be of therapeutic value for the treatment of metabolic disorders.

Published
2020

11. BIPAA/Askomics, a new and easy approach for querying genomics and epigenomics elements in interaction

Author

Olivier Dameron, Charles Bettembourg, Denis Tagu, Yvanne Chaussin, Anthony Bretaudeau, Fabrice Legeai, Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), 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), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), 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 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)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), 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), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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), 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), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, and Université de Rennes (UR)

Subjects
Biological data, Information retrieval, Computer science, RDF Schema, AskOmics, computer.file_format, Linked data, computer.software_genre, Query language, SPARQL, RDF, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, data integration, Data integration, RDF query language, computer.programming_language, Semantic Web
Abstract

International audience; Research programs involving genetic, genomic and epigenetic of have become fast growing areas of biology. Once the computational challenges of analyzing datasets have been dealt with; large and complex biological data still remain in the hands of biologists for interpretation. Projects such as Biomart and Intermine have been developed to facilitate exchange and comparison of complex biological data; however access and interrogation can be time consuming for biologists and integrate all publicly available data still remains a challenge. Moreover for non-model organisms; large heterogeneous biological datasets can be difficult to associate in order to obtain a comprehensive view. The notion of “linked data” from semantic technologies benefits to biologists. Using RDF (Reference Description Framework); biological data can be stored in triples (subject, predicate, object) that define a relationship between two entities. Thanks to the SPARQL query language, RDF data can be queried to join different datasets. Nevertheless, understanding and acquiring the query language can be a daunting task for biologists. Here we present AskOmics, a tool supporting both intuitive data integration and querying while shielding the user from most of the technical difficulties underlying RDF and SPARQL. For data integration, the user loads his data as tabulation-separated files structured according to simple principles. This structure allows AskOmics to generate automatically RDF triples, and to store them. Finally, for data querying, AskOmics provides a visually intuitive interface.AskOmics has been applied successfully to the analysis of large scale datasets including lncRNA, miRNA, piRNA and transcriptomic profiles of the aphid embryogenesis.

Published
2016

12. GO2PUB: Querying PubMed with semantic expansion of gene ontology terms

Author

Anita Burgun, Charles Bettembourg, Christian Diot, Olivier Dameron, Modélisation Conceptuelle des Connaissances Biomédicales, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, CB was supported by a fellowship from the French ministry of research, 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), UMR 936, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), ProdInra, Archive Ouverte, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), 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), BMC, Ed., AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )

Subjects
PubMed, stratégie d'interrogation, Computer Networks and Communications, Computer science, [SDV]Life Sciences [q-bio], MEDLINE, Health Informatics, Semantic expansion, data mining, go annotation, gene ontology, semantic expansion, query enrichment, 03 medical and health sciences, 0302 clinical medicine, 030212 general & internal medicine, ontologie, 030304 developmental biology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], base de données, analyse sémantique, 0303 health sciences, relation semantique, Information retrieval, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Gene ontology, Research, base de données bibliographiques, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Term (time), [SDV] Life Sciences [q-bio], Gene nomenclature, ComputingMethodologies_PATTERNRECOGNITION, annotation sémantique, Informatique (Sciences cognitives), Query enrichment, requête lexicale, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Precision and recall, Information Systems
Abstract

Chantier qualité GA; International audience; ABSTRACT: BACKGROUND: With the development of high throughput methods of gene analyses, there is a growing need for mining tools to retrieve relevant articles in PubMed. As PubMed grows, literature searches become more complex and time-consuming. Automated search tools with good precision and recall are necessary. We developed GO2PUB to automatically enrich PubMed queries with gene names, symbols and synonyms annotated by a GO term of interest or one of its descendants. RESULTS: GO2PUB enriches PubMed queries based on selected GO terms and keywords. It processes the result and displays the PMID, title, authors, abstract and bibliographic references of the articles. Gene names, symbols and synonyms that have been generated as extra keywords from the GO terms are also highlighted. GO2PUB is based on a semantic expansion of PubMed queries using the semantic inheritance between terms through the GO graph. Two experts manually assessed the relevance of GO2PUB, GoPubMed and PubMed on three queries about lipid metabolism. Experts' agreement was high (kappa=0.88). GO2PUB returned 69 % of the relevant articles, GoPubMed: 40 % and PubMed: 29 %. GO2PUB and GoPubMed have 17 % of their results in common, corresponding to 24 % of the total number of relevant results. 70 % of the articles returned by more than one tool were relevant. 36 % of the relevant articles were returned only by GO2PUB, 17 % only by GoPubMed and 14 % only by PubMed. For determining whether these results can be generalized, we generated twenty queries based on random GO terms with a granularity similar to those of the first three queries and compared the proportions of GO2PUB and GoPubMed results. These were respectively of 77 % and 40 % for the first queries, and of 70 % and 38 % for the random queries. The two experts also assessed the relevance of seven of the twenty queries (the three related to lipid metabolism and four related to other domains). Expert agreement was high (0.93 and 0.8). GO2PUB and GoPubMed performances were similar to those of the first queries. CONCLUSIONS: We demonstrated that the use of genes annotated by either GO terms of interest or a descendant of these GO terms yields some relevant articles ignored by other tools. The comparison of GO2PUB, based on semantic expansion, with GoPubMed, based on text mining techniques, showed that both tools are complementary. The analysis of the randomly-generated queries suggests that the results obtained about lipid metabolism can be generalized to other biological processes. GO2PUB is available at http://go2pub.genouest.org.

Published
2012

13. The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes

Author

Anthony Bretaudeau, Olivier Sallou, Olivier Demeure, Christian Diot, Charles Bettembourg, Frédéric Lecerf, Marion Ouedraogo, 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), Plateforme bioinformatique GenOuest [Rennes], 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)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), This work was funded by INRA, Agrocampus Ouest and the Brittany Region., Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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)-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 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Université de Rennes (UR)-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), 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), and 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)

Subjects
lcsh:Medicine, Value (computer science), BIOLOGY, Sequence alignment, Biology, computer.software_genre, Genome, genome structure, Molecular Genetics, 03 medical and health sciences, Databases, 0302 clinical medicine, Semantic similarity, Genes, Duplicate, Gene Duplication, Gene expression, Ontology and Logics, Databases, Genetic, gène dupliqué, chromosome, lcsh:Science, Gene, 030304 developmental biology, base de données, 0303 health sciences, Internet, Multidisciplinary, Database, génome, lcsh:R, Chromosome, Computational Biology, Genomics, Functional Genomics, [SDV.SA.SPA]Life Sciences [q-bio]/Agricultural sciences/Animal production studies, Computer Science, gene expression, lcsh:Q, Structural Genomics, Genome Expression Analysis, Information Technology, computer, Sequence Analysis, 030217 neurology & neurosurgery, Function (biology), expression des gènes, Research Article
Abstract

Chantier qualité GA; BACKGROUND: There has been a surge in studies linking genome structure and gene expression, with special focus on duplicated genes. Although initially duplicated from the same sequence, duplicated genes can diverge strongly over evolution and take on different functions or regulated expression. However, information on the function and expression of duplicated genes remains sparse. Identifying groups of duplicated genes in different genomes and characterizing their expression and function would therefore be of great interest to the research community. The 'Duplicated Genes Database' (DGD) was developed for this purpose. METHODOLOGY: Nine species were included in the DGD. For each species, BLAST analyses were conducted on peptide sequences corresponding to the genes mapped on a same chromosome. Groups of duplicated genes were defined based on these pairwise BLAST comparisons and the genomic location of the genes. For each group, Pearson correlations between gene expression data and semantic similarities between functional GO annotations were also computed when the relevant information was available. CONCLUSIONS: The Duplicated Gene Database provides a list of co-localised and duplicated genes for several species with the available gene co-expression level and semantic similarity value of functional annotation. Adding these data to the groups of duplicated genes provides biological information that can prove useful to gene expression analyses. The Duplicated Gene Database can be freely accessed through the DGD website at http://dgd.genouest.org.

Published
2012

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