Identifying Cis-Regulatory Elements and Modules Using Conditional Random Fields.
Accurate identification of cis-regulatory elements and their correlated modules is essential for analysis of transcriptional regulation, which is a challenging problem in computational biology. Unsupervised learning has the advantage of compensating for missing annotated data, and is thus promising to be effective to identify cis-regulatory elements and modules. We introduced a Conditional Random Fields model, referred to as CRFEM, to integrate sequence features and long-range dependency of genomic sequences such as epigenetic features to identify cis-regulatory elements and modules at the same time. The proposed method is able to automatically learn model parameters with no labeled data and explicitly optimize the predictive probability of cis-regulatory elements and modules. In comparison with existing methods, our method is more accurate and can be used for genome-wide studies of gene regulation.
Yanglan
Y
Gan
Jihong
J
Guan
Shuigeng
S
Zhou
Weixiong
W
Zhang
D000818:Animals; D001665:Binding Sites / Q000235:genetics*; D004330:Drosophila / Q000235:genetics; D023281:Genomics / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics*
D014157:Transcription Factors