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Fast motif matching revisited: high-order PWMs, SNPs and indels
Author(s) -
Janne H. Korhonen,
Kimmo Palin,
Jussi Taipale,
Esko Ukkonen
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/btw683
Subject(s) - motif (music) , computer science , software , sequence motif , data mining , artificial intelligence , theoretical computer science , genetics , programming language , biology , gene , physics , acoustics
While the position weight matrix (PWM) is the most popular model for sequence motifs, there is growing evidence of the usefulness of more advanced models such as first-order Markov representations, and such models are also becoming available in well-known motif databases. There has been lots of research of how to learn these models from training data but the problem of predicting putative sites of the learned motifs by matching the model against new sequences has been given less attention. Moreover, motif site analysis is often concerned about how different variants in the sequence affect the sites. So far, though, the corresponding efficient software tools for motif matching have been lacking.

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