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.

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Keywords Ontologies, Plant breeding, QTL, Quantitative trait locus, Semantic interoperability, Table mining
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Journal B M C Bioinformatics
Singh, G. (Gurnoor), Kuzniar, A. (Arnold), van Mulligen, E.M. (Erik M.), Gavai, A. (Anand), Bachem, C.W. (Christian W.), Visser, R.G.F, & Finkers, R. (Richard). (2018). QTLTableMiner++: Semantic mining of QTL tables in scientific articles. B M C Bioinformatics, 19(1). doi:10.1186/s12859-018-2165-7