Automatically extracting functionally equivalent proteins from SwissProt
Autor: | Lisa E. M. McMillan, Andrew J. Martin |
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Jazyk: | angličtina |
Rok vydání: | 2008 |
Předmět: |
Manual interpretation
Computer science Genomics computer.software_genre lcsh:Computer applications to medicine. Medical informatics Biochemistry Database Sequence Analysis Protein Structural Biology Protein methods Gene duplication Animals Humans Databases Protein Molecular Biology lcsh:QH301-705.5 Internet Applied Mathematics Protein superfamily Computer Science Applications Identification (information) lcsh:Biology (General) lcsh:R858-859.7 Extraction methods Data mining UniProt DNA microarray computer Software |
Zdroj: | BMC Bioinformatics, Vol 9, Iss 1, p 418 (2008) BMC Bioinformatics |
ISSN: | 1471-2105 |
Popis: | Background There 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. Results Large 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. Conclusion In 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. |
Databáze: | OpenAIRE |
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