Automated annotation of microbial proteomes in SWISS-PROT

Gattiker, Alexandre; Michoud, Karine; Rivoire, Catherine; Auchincloss, Andrea H.; Coudert, Elisabeth; Lima, Tânia; Kersey, Paul J.; Pagni, Marco; Sigrist, Christian J. A.; Lachaize, Corinne; Veuthey, Anne‐Lise; Gasteiger, Elisabeth; Bairoch, Amos Marc

doi:10.1016/s1476-9271(02)00094-4

Cited by 129 publications

(90 citation statements)

References 21 publications

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“…The curated part of Swiss-Prot database was selected because of the extent and quality of annotations associated with each protein sequence [25], and the PDB provides an archive of experimentally-determined three-dimensional protein structures. BLAST matches were examined sequentially, searching for a match with direct experimental verification of the function.…”

Section: Manual Annotation Evidence Rating System and Comparison Genmentioning

confidence: 99%

“…The protein sequence for each gene was searched against the Swiss-Prot and Protein Data Bank (PDB) databases using BLAST [24] to identify the most closely related, experimentally characterized homolog available in the literature. The curated part of Swiss-Prot database was selected because of the extent and quality of annotations associated with each protein sequence [25], and the PDB provides an archive of experimentally-determined three-dimensional protein structures. BLAST matches were examined sequentially, searching for a match with direct experimental verification of the function.…”

Section: Supplementary Informationmentioning

confidence: 99%

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The genome sequence of the psychrophilic archaeon, Methanococcoides burtonii: the role of genome evolution in cold adaptation

et al. 2009

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burtonii has a high "IQ" (a measure of adaptive potential) compared to many methanogens.Numerous genes in these two over-represented COG categories appear to have been acquired from ε-and δ-proteobacteria, as do specific genes involved in central metabolism such as a novel B form of aconitase. Transposases also distinguish M. burtonii from other archaea, and their genomic characteristics indicate they play an important role in evolving the M. burtonii genome. Our study reveals a capacity for this model psychrophile to evolve through genome plasticity (including nucleotide skew, horizontal gene transfer and transposase activity) that enables adaptation to the cold, and to the biological and physical changes that have occurred over the last several thousand years as it adapted from a marine, to an Antarctic lake environment.

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Section: Manual Annotation Evidence Rating System and Comparison Genmentioning

confidence: 99%

Section: Supplementary Informationmentioning

confidence: 99%

The genome sequence of the psychrophilic archaeon, Methanococcoides burtonii: the role of genome evolution in cold adaptation

et al. 2009

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“…For the lifestyle analysis, we have manually grouped species into two groups: parasitic and non-parasitic (following the HAMAP database (Gattiker et al, 2003) and (Fitz-Gibbon and House, 1999;House and Fitz-Gibbon, 2002)). We classify as parasitic all organisms that are listed as symbionts, endosysmbionts, or parasites, because organisms living in any of these ecological relationships have been found to have suffered a genome reduction over time (Andersson and Kurland, 1998).…”

Section: Analysis Of Group Patterns In Protein Family Size Distributionsmentioning

confidence: 99%

Evolution of protein families: Is it possible to distinguish between domains of life?

Sales‐Pardo

Chan

Amaral

et al. 2007

Gene

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Understanding evolutionary relationships between species can shed new light into the rooting of the tree of life and the origin of eukaryotes, thus, resulting in a long standing interest in accurately assessing evolutionary parameters at time scales on the order of a billion of years. Prior work suggests large variability in molecular substitution rates, however, we still do not know whether such variability is due to species-specific trends at a genomic scale, or whether it can be attributed to the fluctuations inherent in any stochastic process. Here, we study the statistical properties of gene and protein family sizes in order to quantify the long time scale evolutionary differences and similarities across species. We first determine the protein families of 209 species of bacteria and 20 species of archaea. We find that we are unable to reject the null hypothesis that the protein family sizes of these species are drawn from the same distribution. In addition, we find that for species classified in the same phylogenetic branch or in the same lifestyle group, family size distributions are not significantly more similar than for species in different branches. These two findings can be accounted for in terms of a dynamical birth, death, and innovation model that assumes identical protein family evolutionary rates for all species. Our theoretical and empirical results thus strongly suggest that the variability empirically observed in protein family size distributions is compatible with the expected stochastic fluctuations for an evolutionary process with identical genomic evolutionary rates. Our findings hold special importance for the plausibility of some theories of the origin of eukaryotes which require drastic changes in evolutionary rates for some period during the last two billion years.

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“…The use of a whole protein classification database, such as PIRSF, combined with an insistence that predicted members of a given family exhibit (near) end-to-end similarity, obviates such problems. Other predominantly whole protein classification databases (which may or may not also contain domain families) include COGs, TIGRFAMs (Haft et al, 2003), PANTHER (Mi et al, 2005), and HAMAP (Gattiker et al, 2003).…”

Section: Hierarchical Whole-protein Classificationmentioning

confidence: 99%

“…PIR has developed manually-curated Site Rules (PIRSRs) and Name Rules (PIRNRs), each of which (described in detail below) is based on curated protein families of the PIRSF system. SIB has developed curated rules based on the curated HAMAP family system (Gattiker et al, 2003). EBI has multiple systems-the manually curated RuleBase system (Biswas et al, 2002) and the automatically generated Spearmint (Kretschmann et al, 2001) and Xanthippe (Wieser et al, 2004) rules.…”

Section: Rule-based Automated Annotationmentioning

confidence: 99%

Large‐scale, classification‐driven, rule‐based functional annotation of proteins

Natale

Vinayaka

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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Experimentally verified information on protein function lags far behind the rapid accumulation of protein sequences. The simple approach to propagating information from characterized proteins to unknown proteins – namely, by sequence similarity search against databases of individual proteins – may fail to produce accurate results, and typically is used to transfer only protein name information. A more accurate, consistent, and comprehensive approach for large‐scale automated annotation makes use of protein family classification‐driven rules. Unannotated proteins that satisfy a set of conditions for a particular rule can be annotated with the information appropriate for that rule. The approach leads to facile, accurate prediction and functional inference for uncharacterized proteins, allows systematic detection of genome annotation errors, and provides sensible propagation and standardization of protein annotation, including position‐specific sequence features, protein names and synonyms, and Gene Ontology terms. Rule‐based annotation will be discussed in the context of the PIRSF protein classification system, PIRNR Name Rule system, and the PIRSR Site Rule system.

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Automated annotation of microbial proteomes in SWISS-PROT

Cited by 129 publications

References 21 publications

The genome sequence of the psychrophilic archaeon, Methanococcoides burtonii: the role of genome evolution in cold adaptation

The genome sequence of the psychrophilic archaeon, Methanococcoides burtonii: the role of genome evolution in cold adaptation

Evolution of protein families: Is it possible to distinguish between domains of life?

Large‐scale, classification‐driven, rule‐based functional annotation of proteins

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