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ResidueFinder: extracting individual residue mentions from protein literature
BACKGROUND: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. Mu...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293528/ https://www.ncbi.nlm.nih.gov/pubmed/34289903 http://dx.doi.org/10.1186/s13326-021-00243-3 |
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author | Becker, Ton E Jakobsson, Eric |
author_facet | Becker, Ton E Jakobsson, Eric |
author_sort | Becker, Ton E |
collection | PubMed |
description | BACKGROUND: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. RESULTS: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called “cut”) which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute F(β) for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. CONCLUSIONS: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13326-021-00243-3. |
format | Online Article Text |
id | pubmed-8293528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82935282021-07-21 ResidueFinder: extracting individual residue mentions from protein literature Becker, Ton E Jakobsson, Eric J Biomed Semantics Software BACKGROUND: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. RESULTS: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called “cut”) which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute F(β) for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. CONCLUSIONS: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13326-021-00243-3. BioMed Central 2021-07-21 /pmc/articles/PMC8293528/ /pubmed/34289903 http://dx.doi.org/10.1186/s13326-021-00243-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Becker, Ton E Jakobsson, Eric ResidueFinder: extracting individual residue mentions from protein literature |
title | ResidueFinder: extracting individual residue mentions from protein literature |
title_full | ResidueFinder: extracting individual residue mentions from protein literature |
title_fullStr | ResidueFinder: extracting individual residue mentions from protein literature |
title_full_unstemmed | ResidueFinder: extracting individual residue mentions from protein literature |
title_short | ResidueFinder: extracting individual residue mentions from protein literature |
title_sort | residuefinder: extracting individual residue mentions from protein literature |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293528/ https://www.ncbi.nlm.nih.gov/pubmed/34289903 http://dx.doi.org/10.1186/s13326-021-00243-3 |
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