QSLiMFinder: improved short linear motif prediction using specific query protein data | Zendy

Nicolás Palópoli | Zendy; Kieren Lythgow | Zendy; Richard J. Edwards | Zendy

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QSLiMFinder: improved short linear motif prediction using specific query protein data

Author(s) -

Nicolás Palópoli,

Kieren Lythgow,

Richard J. Edwards

Publication year - 2015

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/btv155

Subject(s) - false positive paradox , computer science , benchmarking , motif (music) , data mining , software , true positive rate , computational biology , machine learning , artificial intelligence , biology , programming language , physics , marketing , acoustics , business

The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions.

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