ArticleBMC bioinformatics2008
Automatically extracting functionally equivalent proteins from SwissProt.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- Physiological Analysis and Genetic Mapping of Short Hypocotyl Trait inInternational journal of molecular sciences · 2023Article
- Molecular Mechanism of Spontaneous Nucleosome Unraveling.Journal of molecular biology · 2019Article
- IntPred: a structure-based predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2018Article
- WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.PLoS computational biology · 2016Article
- Article
- The Tarenaya hassleriana genome provides insight into reproductive trait and genome evolution of crucifers.The Plant cell · 2013Article
- Deep brain stimulation modulates nonsense-mediated RNA decay in Parkinson's patients leukocytes.BMC genomics · 2013Article
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2 authors.
Funding
Abstract
backgroundThere is a frequent need to obtain sets of functionally equivalent homologous proteins (FEPs) from different species. While it is usually the case that orthology implies functional equivalence, this is not always true; therefore datasets of orthologous proteins are not appropriate. The information relevant to extracting FEPs is contained in databanks such as UniProtKB/Swiss-Prot and a manual analysis of these data allow FEPs to be extracted on a one-off basis. However there has been no resource allowing the easy, automatic extraction of groups of FEPs - for example, all instances of protein C.We have developed FOSTA, an automatically generated database of FEPs annotated as having the same function in UniProtKB/Swiss-Prot which can be used for large-scale analysis. The method builds a candidate list of homologues and filters out functionally diverged proteins on the basis of functional annotations using a simple text mining approach.
resultsLarge scale evaluation of our FEP extraction method is difficult as there is no gold-standard dataset against which the method can be benchmarked. However, a manual analysis of five protein families confirmed a high level of performance. A more extensive comparison with two manually verified functional equivalence datasets also demonstrated very good performance.
conclusionIn summary, FOSTA provides an automated analysis of annotations in UniProtKB/Swiss-Prot to enable groups of proteins already annotated as functionally equivalent, to be extracted. Our results demonstrate that the vast majority of UniProtKB/Swiss-Prot functional annotations are of high quality, and that FOSTA can interpret annotations successfully. Where FOSTA is not successful, we are able to highlight inconsistencies in UniProtKB/Swiss-Prot annotation. Most of these would have presented equal difficulties for manual interpretation of annotations. We discuss limitations and possible future extensions to FOSTA, and recommend changes to the UniProtKB/Swiss-Prot format, which would facilitate text-mining of UniProtKB/Swiss-Prot.
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