Minimizing proteome redundancy in the UniProt Knowledgebase

By Borisas Bursteinas, R. Britto, B. Bely, A. Auchincloss, C. Rivoire, Nicole Redaschi, C. O’Donovan, Maria Jesus Martin. 2016

23 citations recorded by the source corpus.

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/

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

The Pfam protein families database in 2019 (2018, background): “These new sequences cover an ever broader range of taxonomies, with the UniProtKB redundancy procedures (9) ensuring that growth reflects increased diversity, rather than, for instance, additional strains of the same bacterial species.”
Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species (2017, methodology): “This approach mirrors that already adopted by the UniProt Knowledgebase for filtering data from bacterial genomes (12).”
UniProt: the Universal Protein Knowledgebase in 2023 (2022, background): “The previously described algorithm (7) that detects redundant proteomes has been optimised to deal with a growing number of bacterial, archaeal and fungal species in UniProtKB.”
Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families (2017, methodology): “To deal with it, the UniProt database recently switched to annotating a non-redundant and regularly updated set of reference proteomes which are selected using a semi-automated approach to cover the taxonomic spread of species (10).”
Pfam: The protein families database in 2021 (2020, background): “Since 2015, highly redundant bacterial proteomes have been identified and removed from UniProtKB (8).”
Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0 (2020, methodology): “Speci ﬁ cally, we considered all high-quality microbial isolate genomes included in UniProt 51 (87,173 total), >154,000 MAGs from human-associated microbiomes 41 , and ~8000 MAGs from primarily non-human environments 52 .”