Publications by authors named "Charles E Chapple"

11 Publications

  • Page 1 of 1

VarSome: the human genomic variant search engine.

Bioinformatics 2019 06;35(11):1978-1980

Saphetor S.A., EPFL Innovation Park - C, Lausanne, Switzerland.

Summary: VarSome.com is a search engine, aggregator and impact analysis tool for human genetic variation and a community-driven project aiming at sharing global expertise on human variants.

Availability And Implementation: VarSome is freely available at http://varsome.com.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty897DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546127PMC
June 2019

Redefining protein moonlighting.

Oncotarget 2015 Jul;6(19):16812-3

Inserm, UMR1090 TAGC, Marseille, France.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627267PMC
http://dx.doi.org/10.18632/oncotarget.4793DOI Listing
July 2015

Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective.

Front Physiol 2015 23;6:171. Epub 2015 Jun 23.

INSERM, UMR_S1090 TAGC Marseille, France ; Aix-Marseille Université, UMR_S1090, TAGC Marseille, France ; Centre National de la Recherche Scientifique Marseille, France.

Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.
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http://dx.doi.org/10.3389/fphys.2015.00171DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4477069PMC
July 2015

PrOnto database : GO term functional dissimilarity inferred from biological data.

Front Genet 2015 3;6:200. Epub 2015 Jun 3.

Inserm, UMR_S1090 TAGC Marseille, France ; Aix-Marseille Université, UMR_S1090 TAGC Marseille, France ; Centre National de la Recherche Scientifique Marseille, France.

Moonlighting proteins are defined by their involvement in multiple, unrelated functions. The computational prediction of such proteins requires a formal method of assessing the similarity of cellular processes, for example, by identifying dissimilar Gene Ontology terms. While many measures of Gene Ontology term similarity exist, most depend on abstract mathematical analyses of the structure of the GO tree and do not necessarily represent the underlying biology. Here, we propose two metrics of GO term functional dissimilarity derived from biological information, one based on the protein annotations and the other on the interactions between proteins. They have been collected in the PrOnto database, a novel tool which can be of particular use for the identification of moonlighting proteins. The database can be queried via an web-based interface which is freely available at http://tagc.univ-mrs.fr/pronto.
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http://dx.doi.org/10.3389/fgene.2015.00200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452890PMC
June 2015

Extreme multifunctional proteins identified from a human protein interaction network.

Nat Commun 2015 Jun 9;6:7412. Epub 2015 Jun 9.

1] Aix-Marseille University, TAGC, Marseille F-13009, France [2] INSERM UMR_S1090, Marseille F-13009, France [3] CNRS, Marseille F-13009, France.

Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of 'extreme multifunctional' proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins.
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http://dx.doi.org/10.1038/ncomms8412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4468855PMC
June 2015

Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters.

Biosystems 2013 Aug 3;113(2):91-5. Epub 2013 Jun 3.

Institut de Mathématiques de Luminy, CNRS - FRE3529, Avenue de Luminy, 13288 Marseille Cedex 9, France.

Background And Scope: Large networks, such as protein interaction networks, are extremely difficult to analyze as a whole. We developed Clust&See, a Cytoscape plugin dedicated to the identification, visualization and analysis of clusters extracted from such networks.

Implementation And Performance: Clust&See provides the ability to apply three different, recently developed graph clustering algorithms to networks and to visualize: (i) the obtained partition as a quotient graph in which nodes correspond to clusters and (ii) the obtained clusters as their corresponding subnetworks. Importantly, tools for investigating the relationships between clusters and vertices as well as their organization within the whole graph are supplied.
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http://dx.doi.org/10.1016/j.biosystems.2013.05.010DOI Listing
August 2013

Multifunctional proteins revealed by overlapping clustering in protein interaction network.

Bioinformatics 2012 Jan 10;28(1):84-90. Epub 2011 Nov 10.

INSERM, U928, TAGC, France.

Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters.

Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein-protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks.

Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197

Contact: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btr621DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244771PMC
January 2012

The genome sequence of taurine cattle: a window to ruminant biology and evolution.

Science 2009 Apr;324(5926):522-8

To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.
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http://dx.doi.org/10.1126/science.1169588DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943200PMC
April 2009

SECISaln, a web-based tool for the creation of structure-based alignments of eukaryotic SECIS elements.

Bioinformatics 2009 Mar 29;25(5):674-5. Epub 2009 Jan 29.

Institut Municipal d'Investigació Mèdica, Universitat Pompeu Fabra and Parc de Recerca Biomedica de Barcelona, Carrer del Doctor Aiguader 88, 08003, Barcelona, Catalonia, Spain.

Summary: Selenoproteins contain the 21st amino acid selenocysteine which is encoded by an inframe UGA codon, usually read as a stop. In eukaryotes, its co-translational recoding requires the presence of an RNA stem-loop structure, the SECIS element in the 3 untranslated region of (UTR) selenoprotein mRNAs. Despite little sequence conservation, SECIS elements share the same overall secondary structure. Until recently, the lack of a significantly high number of selenoprotein mRNA sequences hampered the identification of other potential sequence conservation. In this work, the web-based tool SECISaln provides for the first time an extensive structure-based sequence alignment of SECIS elements resulting from the well-defined secondary structure of the SECIS RNA and the increased size of the eukaryotic selenoproteome. We have used SECISaln to improve our knowledge of SECIS secondary structure and to discover novel, conserved nucleotide positions and we believe it will be a useful tool for the selenoprotein and RNA scientific communities.

Availability: SECISaln is freely available as a web-based tool at http://genome.crg.es/software/secisaln/.
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http://dx.doi.org/10.1093/bioinformatics/btp020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2647825PMC
March 2009

Relaxation of selective constraints causes independent selenoprotein extinction in insect genomes.

PLoS One 2008 Aug 13;3(8):e2968. Epub 2008 Aug 13.

Center for Genomic Regulation, Universitat Pompeu Fabra and Institut Municipal d'Investigació Mèdica, Barcelona, Catalonia, Spain.

Background: Selenoproteins are a diverse family of proteins notable for the presence of the 21st amino acid, selenocysteine. Until very recently, all metazoan genomes investigated encoded selenoproteins, and these proteins had therefore been believed to be essential for animal life. Challenging this assumption, recent comparative analyses of insect genomes have revealed that some insect genomes appear to have lost selenoprotein genes.

Methodology/principal Findings: In this paper we investigate in detail the fate of selenoproteins, and that of selenoprotein factors, in all available arthropod genomes. We use a variety of in silico comparative genomics approaches to look for known selenoprotein genes and factors involved in selenoprotein biosynthesis. We have found that five insect species have completely lost the ability to encode selenoproteins and that selenoprotein loss in these species, although so far confined to the Endopterygota infraclass, cannot be attributed to a single evolutionary event, but rather to multiple, independent events. Loss of selenoproteins and selenoprotein factors is usually coupled to the deletion of the entire no-longer functional genomic region, rather than to sequence degradation and consequent pseudogenisation. Such dynamics of gene extinction are consistent with the high rate of genome rearrangements observed in Drosophila. We have also found that, while many selenoprotein factors are concomitantly lost with the selenoproteins, others are present and conserved in all investigated genomes, irrespective of whether they code for selenoproteins or not, suggesting that they are involved in additional, non-selenoprotein related functions.

Conclusions/significance: Selenoproteins have been independently lost in several insect species, possibly as a consequence of the relaxation in insects of the selective constraints acting across metazoans to maintain selenoproteins. The dispensability of selenoproteins in insects may be related to the fundamental differences in antioxidant defense between these animals and other metazoans.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002968PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2500217PMC
August 2008

A comparison of random sequence reads versus 16S rDNA sequences for estimating the biodiversity of a metagenomic library.

Nucleic Acids Res 2008 Sep 5;36(16):5180-8. Epub 2008 Aug 5.

Digestive System Research Unit, University Hospital Vall d'Hebron, Ciberehd, Bioinformatics and Genomics Program, Center for Genomic Regulation, Barcelona, Spain.

The construction of metagenomic libraries has permitted the study of microorganisms resistant to isolation and the analysis of 16S rDNA sequences has been used for over two decades to examine bacterial biodiversity. Here, we show that the analysis of random sequence reads (RSRs) instead of 16S is a suitable shortcut to estimate the biodiversity of a bacterial community from metagenomic libraries. We generated 10,010 RSRs from a metagenomic library of microorganisms found in human faecal samples. Then searched them using the program BLASTN against a prokaryotic sequence database to assign a taxon to each RSR. The results were compared with those obtained by screening and analysing the clones containing 16S rDNA sequences in the whole library. We found that the biodiversity observed by RSR analysis is consistent with that obtained by 16S rDNA. We also show that RSRs are suitable to compare the biodiversity between different metagenomic libraries. RSRs can thus provide a good estimate of the biodiversity of a metagenomic library and, as an alternative to 16S, this approach is both faster and cheaper.
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http://dx.doi.org/10.1093/nar/gkn496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2532719PMC
September 2008
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