Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models.

ABSTRACT: We present a discriminative learning method for pattern discovery of binding sites in nucleic acid sequences based on hidden Markov models. Sets of positive and negative example sequences are mined for sequence motifs whose occurrence frequency varies between the sets. The method offers several objective functions, but we concentrate on mutual information of condition and motif occurrence. We perform a systematic comparison of our method and numerous published motif-finding tools. Our method achieves the highest motif discovery performance, while being faster than most published methods. We present case studies of data from various technologies, including ChIP-Seq, RIP-Chip and PAR-CLIP, of embryonic stem cell transcription factors and of RNA-binding proteins, demonstrating practicality an

SUBMITTER: Maaskola J 

PROVIDER: S-EPMC4245949 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature