RSEG 0.4.9 – Identify Epigenomic Domains from ChIP-Seq data

RSEG 0.4.9

:: DESCRIPTION

The RSEG software package is aimed to analyze ChIP-Seq data, especially for identifying genomic regions and their boundaries marked by diffusive histone modification markers, such as H3K36me3 and H3K27me3. It can work with or without control sample. It can be used to find regions with differential histone modifications patterns, either comparsion between two cell types or between two kinds of histone modifications.

::DEVELOPER

The Smith Lab

:: SCREENSHOTS

Command

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RSEG

:: MORE INFORMATION

Citation:

Bioinformatics. 2011 Mar 15;27(6):870-1. Epub 2011 Feb 16.
Identifying dispersed epigenomic domains from ChIP-Seq data.
Song Q, Smith AD.

TFBphylo 1.0 – Evolution of Transcription Factor Binding events using Multi-species ChIP-Seq data

TFBphylo 1.0

:: DESCRIPTION

TFBphylo is an R package implementing a phylogenetic tree based method to model the on/off rates of transcription factor binding (TFB) events, and uses the EM algorithm to estimate model parameters.

::DEVELOPER

Hongyu Zhao’s Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/MacOsX
  • R package

:: DOWNLOAD

  TFBphylo

:: MORE INFORMATION

Citation

Stat Appl Genet Mol Biol. 2013 Feb 27:1-15. doi: 10.1515/sagmb-2012-0004. [Epub ahead of print]
Studying the evolution of transcription factor binding events using multi-species ChIP-Seq data.
Zheng W, Zhao H.

MICSA – Identification of Transcription Factor binding sites in ChIP-Seq data

MICSA

:: DESCRIPTION

MICSA (Motif Identification for ChIP-Seq Analysis) is package for the identification of transcription factor binding sites in ChIP-Seq data

::DEVELOPER

Computational Systems Biology of Cancer group in Bioinformatics Laboratory of Institut Curie (Paris).

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Java

:: DOWNLOAD

 MICSA

:: MORE INFORMATION

Citation:

De novo motif identification improves the accuracy of predicting transcription factor binding sites in ChIP-Seq data analysis.
Boeva V, Surdez D, Guillon N, Tirode F, Fejes AP, Delattre O, Barillot E.
Nucleic Acids Res. 2010 Jun;38(11):e126. Epub 2010 Apr 7.

CASSys – Interactive Analysis of ChIP-seq data

CASSys

:: DESCRIPTION

CASSys (ChIP-seq data Analysis Software System) is an integrated system that supports all steps of computational ChIP-seq data analysis and supersedes the laborious application of several single command line tools. CASSys provides functionality ranging from quality assessment and -control of short reads, over the mapping of reads against a reference genome (readmapping) and the detection of enriched regions (peakdetection) to various follow-up analyses. The latter are accessible via a state-of-the-art web interface and can be performed interactively by the user. The follow-up analyses allow for flexible user defined association of putative interaction sites with genes, visualization of their genomic context with an integrated genome browser, the detection of putative binding motifs, the identification of over-represented Gene Ontology-terms, pathway analysis and the visualization of interaction networks. The system is client-server based, accessible via a web browser and does not require any software installation on the client side.

CASSys Online Server

::DEVELOPER

Junior Research Group for Application-oriented Bioinformatics, ,Center for Bioinformatics, University of Hamburg

:: SCREENSHOTS

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Alawi, M., Kurtz, S. Beckstette, M. (2011).
CASSys: an integrated software system for the interactive analysis of ChIP-seq data.
Journal of Integrative Bioinformatics, 8(2):155,2011.

coMotif 1.0 – Identify Transcription Co-regulator Binding Sites in ChIP-seq Data

coMotif 1.0

:: DESCRIPTION

coMotif is a software of three-component mixture framework to model the joint distribution of two motifs as well as the situation where some sequences contain only one or none of the motifs.

::DEVELOPER

Leping Li, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 coMotif 

:: MORE INFORMATION

Citation:

Mengyuan Xu, Clarice R. Weinberg, David M. Umbach and Leping Li
coMOTIF: a mixture framework for identifying transcription factor and a coregulator motif in ChIP-seq Data
Bioinformatics (2011) 27 (19): 2625-2632.

GPS 1.1 – Study Protein-DNA Interaction using ChIP-Seq data

GPS 1.1

:: DESCRIPTION

GPS (Genome Positioning System) is a software tool to study protein-DNA interaction using ChIP-Seq data. GPS builds a probabilistic mixture model to predict the most likely positions of binding events at single-base resolution.

::DEVELOPER

the Gifford Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java

:: DOWNLOAD

 GPS

:: MORE INFORMATION

Citation

Yuchun Guo, Georgios Papachristoudis, Robert C Altshuler, Georg K Gerber, Tommi S Jaakkola, David K Gifford & Shaun Mahony,
Discovering homotypic binding events at high spatial resolution.
Bioinformatics. 2010 Dec 15;26(24):3028-34. Epub 2010 Oct 21.

EpiCenter 1.7.0.8 – Analysis tool of Genome-wide mRNA-seq or ChIP-seq data

EpiCenter 1.7.0.8

:: DESCRIPTION

EpiCenter is a powerful analysis tool of genome-wide mRNA-seq or ChIP-seq data for detecting differentially expressed genes or identifying changes in epigenetic modifications.

::DEVELOPER

Leping Li, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX

:: DOWNLOAD

 EpiCenter

:: MORE INFORMATION

Citation:

W Huang, DM Umbach, N Vincent Jordan, AN Abell, GL Johnson, and L Li.
Efficiently identifying genome-wide changes with next-generation sequencing data.
Nucl. Acids Res. (2011)

Repeat Enrichment Estimator – Measure Enrichment of Annotated Repeat Types in ChIP-seq data

Repeat Enrichment Estimator

:: DESCRIPTION

Repeat enrichment estimator aims to measure the enrichment of annotated repeat types in ChIP-seq data

::DEVELOPER

Computational Genomics (PI: Peter J Park)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  Repeat enrichment estimator

:: MORE INFORMATION

Citation

Daniel S Day, Lovelace J Luquette, Peter J Park and Peter V Kharchenko.
Estimating enrichment of repetitive elements from high-throughput sequence data.
Genome Biology 2010, 11:R69 doi:10.1186/gb-2010-11-6-r69