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QTLTableMiner(++): semantic mining of QTL tables in scientific articles

BACKGROUND: A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text ra...

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Autores principales: Singh, Gurnoor, Kuzniar, Arnold, van Mulligen, Erik M., Gavai, Anand, Bachem, Christian W., Visser, Richard G.F., Finkers, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970438/
https://www.ncbi.nlm.nih.gov/pubmed/29801439
http://dx.doi.org/10.1186/s12859-018-2165-7
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author Singh, Gurnoor
Kuzniar, Arnold
van Mulligen, Erik M.
Gavai, Anand
Bachem, Christian W.
Visser, Richard G.F.
Finkers, Richard
author_facet Singh, Gurnoor
Kuzniar, Arnold
van Mulligen, Erik M.
Gavai, Anand
Bachem, Christian W.
Visser, Richard G.F.
Finkers, Richard
author_sort Singh, Gurnoor
collection PubMed
description BACKGROUND: A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner(++) (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. RESULTS: The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. CONCLUSION: QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2165-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-59704382018-05-30 QTLTableMiner(++): semantic mining of QTL tables in scientific articles Singh, Gurnoor Kuzniar, Arnold van Mulligen, Erik M. Gavai, Anand Bachem, Christian W. Visser, Richard G.F. Finkers, Richard BMC Bioinformatics Software BACKGROUND: A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner(++) (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. RESULTS: The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. CONCLUSION: QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2165-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-25 /pmc/articles/PMC5970438/ /pubmed/29801439 http://dx.doi.org/10.1186/s12859-018-2165-7 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Singh, Gurnoor
Kuzniar, Arnold
van Mulligen, Erik M.
Gavai, Anand
Bachem, Christian W.
Visser, Richard G.F.
Finkers, Richard
QTLTableMiner(++): semantic mining of QTL tables in scientific articles
title QTLTableMiner(++): semantic mining of QTL tables in scientific articles
title_full QTLTableMiner(++): semantic mining of QTL tables in scientific articles
title_fullStr QTLTableMiner(++): semantic mining of QTL tables in scientific articles
title_full_unstemmed QTLTableMiner(++): semantic mining of QTL tables in scientific articles
title_short QTLTableMiner(++): semantic mining of QTL tables in scientific articles
title_sort qtltableminer(++): semantic mining of qtl tables in scientific articles
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970438/
https://www.ncbi.nlm.nih.gov/pubmed/29801439
http://dx.doi.org/10.1186/s12859-018-2165-7
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