title: Minimizing proteome redundancy in the UniProt Knowledgebase.
abstract: Advances in high-throughput sequencing have led to an unprecedented growth in genome sequences being submitted to biological databases. In particular, the sequencing of large numbers of nearly identical bacterial genomes during infection outbreaks and for other large-scale studies has resulted in a high level of redundancy in nucleotide databases and consequently in the UniProt Knowledgebase (UniProtKB). Redundancy negatively impacts on database searches by causing slower searches, an increase in statistical bias and cumbersome result analysis. The redundancy combined with the large data volume increases the computational costs for most reuses of UniProtKB data. All of this poses challenges for effective discovery in this wealth of data. With the continuing development of sequencing technologies, it is clear that finding ways to minimize redundancy is crucial to maintaining UniProt's essential contribution to data interpretation by our users. We have developed a methodology to identify and remove highly redundant proteomes from UniProtKB. The procedure identifies redundant proteomes by performing pairwise alignments of sets of sequences for pairs of proteomes and subsequently, applies graph theory to find dominating sets that provide a set of non-redundant proteomes with a minimal loss of information. This method was implemented for bacteria in mid-2015, resulting in a removal of 50 million proteins in UniProtKB. With every new release, this procedure is used to filter new incoming proteomes, resulting in a more scalable and scientifically valuable growth of UniProtKB.Database URL: http://www.uniprot.org/proteomes/.
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Borisas
authors.initials: B
authors.lastname: Bursteinas
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Ramona
authors.initials: R
authors.lastname: Britto
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Benoit
authors.initials: B
authors.lastname: Bely
authors.affiliation: SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, Geneva 4 1211, Switzerland.
authors.forename: Andrea
authors.initials: A
authors.lastname: Auchincloss
authors.affiliation: SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, Geneva 4 1211, Switzerland.
authors.forename: Catherine
authors.initials: C
authors.lastname: Rivoire
authors.affiliation: SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, Geneva 4 1211, Switzerland.
authors.forename: Nicole
authors.initials: N
authors.lastname: Redaschi
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Claire
authors.initials: C
authors.lastname: O'Donovan
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK martin@ebi.ac.uk.
authors.forename: Maria Jesus
authors.initials: MJ
authors.lastname: Martin
mesh_terms: D001419:Bacteria / Q000235:genetics* / Q000378:metabolism; D001426:Bacterial Proteins / Q000235:genetics* / Q000378:metabolism; D030562:Databases, Protein; D058977:Molecular Sequence Annotation / Q000379:methods*; D020543:Proteome / Q000235:genetics* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D001426:Bacterial Proteins; D020543:Proteome