Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model.

ABSTRACT: Hidden Markov models (HMMs) have been extensively used to dissect the genome into functionally distinct regions using data such as RNA expression or DNA binding measurements. It is a challenge to disentangle processes occurring on complementary strands of the same genomic region. We present the double-stranded HMM (dsHMM), a model for the strand-specific analysis of genomic processes. We applied dsHMM to yeast using strand specific transcription data, nucleosome data, and protein binding data for a set of 11 factors associated with the regulation of transcription.The resulting annotation recovers the mRNA transcription cycle (initiation, elongation, termination) while correctly predicting strand-specificity and directionality of the transcription process. We find that pre-initiation comple

SUBMITTER: Glas J 

PROVIDER: S-EPMC4797261 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature