groHMM-package: groHMM: GRO-seq Analysis Pipeline
In omsai/groHMM: GRO-seq Analysis Pipeline

Description Details Author(s) References

groHMM was developed for analysis of GRO-seq data, which provides a genome wide 'map' of the position and orientation of all transcriptionally active RNA polymerases. groHMM predicts the boundaries of transcriptional activity across the genome de novo using a two-state hidden Markov model (HMM). The model essentially divides the genome into 'transcribed' and 'non-transcribed' regions in a strand specific manner.

We also use HMMs to identify the leading edge of Pol II at genes activated by a stimulus in GRO-seq time course data. This approach allows the genome-wide interrogation of transcription rates in cells.

In addition to these advanced features, groHMM provides wrapper functions for counting raw reads, and creating metagene (averaging) plots. Although groHMM is tailored towards GRO-seq data, the same functions and analytical methodologies can, in principal, be applied to a wide variety of other short read data sets.

Package:	groHMM
Type:	Package
Version:	0.99.0
Date:	2014-04-02
License:	GPL (>=3)
LazyLoad:	yes
Depends:	R (>= 2.14.0), MASS, GenomicRanges, rtracklayer, parallel

Charles G. Danko, Minho Chae, Andre Martins

Maintainer: Pariksheet Nanda <pariksheet.nanda@uconn.edu>

Luo, X., Chae, M., Krishnakumar, R,, Danko, C., Kraus, L. Dynamic reorganization of the AC16 cardiomyocyte transcriptome in response to TNFa signaling revealed by integrated genomic analyses. BMC Genomics. 2014 Feb 24;15(1):155

Hah, N., Danko, C., Core, L., Waterfall, J., Siepel, A., Lis, J., Kraus, L. A Rapid, Extensive, and Transient Transcriptional Response to Estrogen Signaling in Breast Cancer Cells. Cell. 2011 May 13;145(4):622-34

omsai/groHMM documentation built on May 24, 2019, 2:18 p.m.