GroHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data.

ABSTRACT:

Background

Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units.

Results

Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad region

SUBMITTER: Chae M 

PROVIDER: S-EPMC4502638 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature