Search

Your search keyword '"European Project: 222886,EC:FP7:KBBE,FP7-KBBE-2007-2A,MICROME(2009)"' showing total 3 results
Start Over Author "European Project: 222886,EC:FP7:KBBE,FP7-KBBE-2007-2A,MICROME(2009)"
3 results on '"European Project: 222886,EC:FP7:KBBE,FP7-KBBE-2007-2A,MICROME(2009)"'

1. Reconciliation of metabolites and biochemical reactions for metabolic networks

Author

Alan Bridge, Sébastien Moretti, Anne Morgat, Thomas Bernard, Ioannis Xenarios, Marco Pagni, Swiss Institute of Bioinformatics [Genève] (SIB), Swiss-Prot Group, 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), Vital-IT, European Project: 222886,EC:FP7:KBBE,FP7-KBBE-2007-2A,MICROME(2009), University of Zurich, and Pagni, Marco

Subjects
Databases, Factual, Computer science, Process (engineering), SX20 Research, Technology and Development Projects, Metabolic network, Scientific literature, Metabolic networks, computer.software_genre, 1710 Information Systems, Manual curation, Models, Biological, 03 medical and health sciences, SX00 SystemsX.ch, Metabolic resources, 1312 Molecular Biology, Data interoperability, Biochemical reactions, Computer Simulation, Molecular Biology, 030304 developmental biology, 0303 health sciences, Molecular Structure, Cheminformatics, 030302 biochemistry & molecular biology, Computational Biology, Genomics, Data science, data integration, data interoperability, metabolic resources, metabolic networks, cheminformatics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], SX10 MetaNetX, Papers, 570 Life sciences, biology, Data integration, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Metabolic Networks and Pathways, Software, Information Systems
Abstract

International audience; Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.

Published
2012
Full Text
View/download PDF

2. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets

Author

Denis Thieffry, Carl Herrmann, Morgane Thomas-Chollier, Olivier Sand, Matthieu Defrance, Jacques van Helden, Max Planck Institute for Molecular Genetics (MPIMG), Max-Planck-Gesellschaft, Technologies avancées pour le génôme et la clinique (TAGC), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universidad Nacional Autónoma de México (UNAM), Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (GI3M), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), 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), Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université libre de Bruxelles (ULB), Alexander von Humboldt foundation (to M.T.C.), Agence Nationale de la Recherche (ANR) partner of the ERASysBio+ initiative supported under the EU ERA-NET Plus scheme in FP7, ANR Young Researchers Grant ‘CardiHox’ (to C.H.), the Belgian Program on Interuniversity Attraction Poles, initiated by the Belgian Federal Science Policy Office [project P6/25 (BioMaGNet)], EU-funded COST action [BM1006 ‘Next Generation Sequencing Data Analysis Network’], FP7 MICROME Collaborative Project (‘Microbial genomics and bio-informatics’, contract number 222886-2). Funding for open access charge: Belgian Program on Interuniversity Attraction Poles [project P6/25 (BioMaGNet)]., European Project: 222886,EC:FP7:KBBE,FP7-KBBE-2007-2A,MICROME(2009), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (EGENODIA (GI3M)), Institut de biologie de l'ENS Paris (IBENS), École normale supérieure - Paris (ENS-PSL), 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-PSL), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), 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), Spinelli, Lionel, and The Microme Project: A Knowledge-Based Bioinformatics Framework for Microbial Pathway Genomics - MICROME - - EC:FP7:KBBE2009-12-01 - 2013-11-30 - 222886 - VALID

Subjects
Chromatin Immunoprecipitation, Sequence analysis, [SDV]Life Sciences [q-bio], Time efficiency, P300-CBP Transcription Factors, Genome browser, Computational biology, Biology, Genome, p300-CBP Transcription Factors -- metabolism, Mice, User-Computer Interface, 03 medical and health sciences, 0302 clinical medicine, Genetics, Animals, p300-CBP Transcription Factors, Regulatory Elements, Transcriptional, Nucleotide Motifs, Transcription Factors -- metabolism, Embryonic Stem Cells, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 030304 developmental biology, 0303 health sciences, Massively Parallel (Deep) Sequencing, Sequence Analysis, DNA, Genomics, Sciences bio-médicales et agricoles, Chip, Visualization, [SDV] Life Sciences [q-bio], Methods Online, Motif (music), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Embryonic Stem Cells -- metabolism, Software, 030217 neurology & neurosurgery, Transcription Factors, Computational Methods
Abstract

ChIP-seq is increasingly used to characterize transcription factor binding and chromatin marks at a genomic scale. Various tools are now available to extract binding motifs from peak data sets. However, most approaches are only available as command-line programs, or via a website but with size restrictions. We present peak-motifs, a computational pipeline that discovers motifs in peak sequences, compares them with databases, exports putative binding sites for visualization in the UCSC genome browser and generates an extensive report suited for both naive and expert users. It relies on time- and memory-efficient algorithms enabling the treatment of several thousand peaks within minutes. Regarding time efficiency, peak-motifs outperforms all comparable tools by several orders of magnitude. We demonstrate its accuracy by analyzing data sets ranging from 4000 to 1,28,000 peaks for 12 embryonic stem cell-specific transcription factors. In all cases, the program finds the expected motifs and returns additional motifs potentially bound by cofactors. We further apply peak-motifs to discover tissue-specific motifs in peak collections for the p300 transcriptional co-activator. To our knowledge, peak-motifs is the only tool that performs a complete motif analysis and offers a user-friendly web interface without any restriction on sequence size or number of peaks., Journal Article, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2012
Full Text
View/download PDF

3. Characterization of NrnA homologs from Mycobacterium tuberculosis and Mycoplasma pneumoniae

Author

Undine Mechold, Guillaume Postic, Antoine Danchin, Génétique des Génomes Bactériens, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), AMAbiotics SAS, Biochimie des Interactions Macromoléculaires / Biochemistry of Macromolecular Interactions, Part of this work was supported by MICROME, a Collaborative Project funded by the European Commission within its FP7 Program, under thethematic area ‘‘BIO-INFORMATICS-Microbial genomics and bio-informatics,’’ contract no. 222886-2., European Project: 222886,EC:FP7:KBBE,FP7-KBBE-2007-2A,MICROME(2009), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)

Subjects
MESH: Mutation, MESH: Mycobacterium tuberculosis, [SDV]Life Sciences [q-bio], Mutant, Bacillus subtilis, medicine.disease_cause, Article, MESH: Recombinant Proteins, Streptococcus mutans, 03 medical and health sciences, medicine, Escherichia coli, Nucleotide, Molecular Biology, 030304 developmental biology, chemistry.chemical_classification, MESH: Genetic Complementation Test, 0303 health sciences, Mutation, biology, MESH: Escherichia coli, 030306 microbiology, Oligonucleotide, Genetic Complementation Test, RNA, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Mycobacterium tuberculosis, biology.organism_classification, MESH: Streptococcus mutans, Phosphoric Monoester Hydrolases, Recombinant Proteins, 3. Good health, Mycoplasma pneumoniae, MicroRNAs, Enzyme, chemistry, Biochemistry, MESH: Exoribonucleases, Exoribonucleases, MESH: Phosphoric Monoester Hydrolases, MESH: Mycoplasma pneumoniae, MESH: MicroRNAs
Abstract

Processive RNases are unable to degrade efficiently very short oligonucleotides, and they are complemented by specific enzymes, nanoRNases, that assist in this process. We previously identified NrnA (YtqI) from Bacillus subtilis as a bifunctional protein with the ability to degrade nanoRNA (RNA oligos ≤5 nucleotides) and to dephosphorylate 3′-phosphoadenosine 5′-phosphate (pAp) to AMP. While the former activity is analogous to that of oligoribonuclease (Orn) from Escherichia coli, the latter corresponds to CysQ. NrnA homologs are widely present in bacterial and archaeal genomes. They are found preferably in genomes that lack Orn or CysQ homologs. Here, we characterize NrnA homologs from important human pathogens, Mpn140 from Mycoplasma pneumoniae, and Rv2837c from Mycobacterium tuberculosis. Like NrnA, these enzymes degrade nanoRNA and dephosphorylate pAp in vitro. However, they show dissimilar preferences for specific nanoRNA substrate lengths. Whereas NrnA prefers RNA 3-mers with a 10-fold higher specific activity compared to 5-mers, Rv2837c shows a preference for nanoRNA of a different length, namely, 2-mers. Mpn140 degrades Cy5-labeled nanoRNA substrates in vitro with activities varying within one order of magnitude as follows: 5-mer>4-mer>3-mer>2-mer. In agreement with these in vitro activities, both Rv2837c and Mpn140 can complement the lack of their functional counterparts in E. coli: CysQ and Orn. The NrnA homolog from Streptococcus mutans, SMU.1297, was previously shown to hydrolyze pAp and to complement an E. coli cysQ mutant. Here, we show that SMU.1297 can complement an E. coli orn− mutant, suggesting that having both pAp-phosphatase and nanoRNase activity is a common feature of NrnA homologs.

Published
2012
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results