2004 MutationInformationExtraction

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Subject Headings: Protein NER Algorithm, Molecular Class-Specific Information System.

= Notes

  • A quick review of the paper suggests that a manual surface+NER pattern was used in that experiment.

Cited By

= Quotes

Abstract

Motivation: The amount of genomic and proteomic data that is published daily in the scientific literature is outstripping the ability of experimental scientists to stay current. Reviews, the traditional medium for collating published observations, are also unable to keep pace. For some specific classes of information (e.g. sequences and protein structures), obligatory data deposition policies have helped. However, a great deal of other valuable information is spread throughout the literature hindering coherent access. We are involved in the Molecular Class-Specific Information System (MCSIS) project, a collaborative effort to design and automate the maintenance of protein family databases. The first two databases, the GPCRDB and NucleaRDB, are focused on G protein-coupled receptors (GPCRs) and nuclear hormone receptors (NRs), respectively. The main aim of the MCSIS project is to gather heterogeneous data from across a variety of electronic and literature sources in order to draw new inferences about the target protein families.

  • Results: We present a computational method that identifies and extracts mutation data from the scientific literature. We focused on the extraction of single point mutations for the GPCR and NR superfamilies. After validation by plausibility filters, the mutation data is integrated into the corresponding MCSIS where it is combined with structural and sequence information already stored in these databases. We extracted and validated 2736 true point mutations from 914 articles on GPCRs and 785 true point mutations from 1094 articles on NRs. The current version of our automated extraction algorithm identifies 49.3% of the GPCR point mutations with a specificity of 87.9%, and 64.5% of the NR point mutations with a specificity of 85.8%. MuteXt routinely analyzes 100 electronic articles in approximately 1 h."

Systems and Methods: Information Extraction

  • The pattern must start with one amino acid in the one- or three-letter code followed by a number, and optimally by another amino acid encoded with the same letter code format as the first one.
  • The regular expression we use is: ([A–Z][1–9][0–9] + $)|([A–Z][1–9][0–9] ∗ [A–Z]$) |([A–Z][a–z][a–z][1–9][0–9] ∗ $) |([A–Z][a–z][a–z][1–9][0–9] ∗ [A–Z][a–z][a–z]$)

Citations

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 MutationInformationExtractionFlorence Horn
Anthony L. Lau
Fred E. Cohen
Automated Extraction of Mutation Data from the Literature: Application of MuteXt to G protein-coupled receptors and nuclear hormone receptorsBioinformatics Subject Areahttp://bioinformatics.oxfordjournals.org/cgi/reprint/20/4/557.pdf2004
AuthorFlorence Horn +, Anthony L. Lau + and Fred E. Cohen +
journalBioinformatics +
titleAutomated Extraction of Mutation Data from the Literature: Application of MuteXt to G protein-coupled receptors and nuclear hormone receptors +
titleUrlhttp://bioinformatics.oxfordjournals.org/cgi/reprint/20/4/557.pdf +
year2004 +