SETH detects and normalizes genetic variants in text | Zendy

Philippe Thomas | Zendy; Tim Rocktäschel | Zendy; Jörg Hakenberg | Zendy; Yvonne Lichtblau | Zendy; Ulf Leser | Zendy

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SETH detects and normalizes genetic variants in text

Author(s) -

Philippe Thomas,

Tim Rocktäschel,

Jörg Hakenberg,

Yvonne Lichtblau,

Ulf Leser

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw234

Subject(s) - scripting language , computer science , dbsnp , documentation , annotation , uniprot , normalization (sociology) , information retrieval , variation (astronomy) , natural language processing , artificial intelligence , world wide web , programming language , biology , gene , genetics , genotype , single nucleotide polymorphism , physics , sociology , anthropology , astrophysics

: Descriptions of genetic variations and their effect are widely spread across the biomedical literature. However, finding all mentions of a specific variation, or all mentions of variations in a specific gene, is difficult to achieve due to the many ways such variations are described. Here, we describe SETH, a tool for the recognition of variations from text and their subsequent normalization to dbSNP or UniProt. SETH achieves high precision and recall on several evaluation corpora of PubMed abstracts. It is freely available and encompasses stand-alone scripts for isolated application and evaluation as well as a thorough documentation for integration into other applications.

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