ChIPModule 20130227 – Systematic discovery of Transcription Factors and their Cofactors from ChIP-seq data

ChIPModule 20130227

:: DESCRIPTION

ChIPModule is a software tool for systematical discoveray of transcription factors and their cofactors from ChIP-seq data. Given a ChIP-seq dataset and motifs of a large number of transcription factors, ChIPModule can efficiently identify groups of motifs,whose instances significantly co-occur in the ChIP-seq peak regions.

::DEVELOPER

Hu Lab – Data Integration and Knowledge Discovery @ UCF

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • Python

:: DOWNLOAD

 ChIPModule

:: MORE INFORMATION

Citation:

Pac Symp Biocomput. 2013:320-31.
ChIPModule: systematic discovery of transcription factors and their cofactors from ChIP-seq data.
Ding J, Cai X, Wang Y, Hu H, Li X.

SIOMICS 3.0 – Systematic Identification Of Motifs In ChIP-Seq data

SIOMICS 3.0

:: DESCRIPTION

SIOMICS is a software developed to de novo identify motifs in large sequence datasets such as those from ChIP-seq experiments. The output of the software is the ranked motifs and motif modules (significantly co-occurring motif combinations). The statistical evaluation of the predicted motifs and motif modules is also provided.

::DEVELOPER

Hu Lab – Data Integration and Knowledge Discovery @ UCF

:: SCREENSHOTS

SIOMICS

:: REQUIREMENTS

  • Linux/ Windows
  • Python
  • Tkinter
  • Java

:: DOWNLOAD

 SIOMICS

:: MORE INFORMATION

Citation:

Nucleic Acids Res. 2014 Mar;42(5):e35. doi: 10.1093/nar/gkt1288. Epub 2013 Dec 9.
SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data.
Ding J, Hu H, Li X.

SEME 1.0 – A de novo Motif Finder for ChIP-seq data

SEME 1.0

:: DESCRIPTION

SEME ( Sampling with Expectation maximization for Motif Elicitation) is a de novo motif discovery algorithm  which uses pure probabilistic mixture model to model the motif’s binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position, and sequence rank preferences without asking for any prior knowledge from the user.

::DEVELOPER

Sung Wing Kin, Ken

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 SEME 

:: MORE INFORMATION

Citation

J Comput Biol. 2013 Mar;20(3):237-48. doi: 10.1089/cmb.2012.0233.
Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.
Zhang Z, Chang CW, Hugo W, Cheung E, Sung WK.

MOCCS 2.0 – Motif Centrality Analysis of ChIP-Seq

MOCCS 2.0

:: DESCRIPTION

MOCCS is a method for for clarifying DNA-binding motif ambiguity.

::DEVELOPER

Haruka Ozaki, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • R
  • Perl

:: DOWNLOAD

 MOCCS

:: MORE INFORMATION

Citation

MOCCS: Clarifying DNA-binding motif ambiguity using ChIP-Seq data.
Ozaki H, Iwasaki W.
Comput Biol Chem. 2016 Feb 13. pii: S1476-9271(16)30030-5. doi: 10.1016/j.compbiolchem.2016.01.014.

MUSIC – MUltiScale enrIchment Calling for ChIP-Seq Datasets

MUSIC

:: DESCRIPTION

MUSIC is an algorithm for identification of enriched regions at multiple scales in the read depth signals from ChIP-Seq experiments.

::DEVELOPER

Gerstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MUSIC

:: MORE INFORMATION

Citation:

Genome Biol. 2014;15(10):474.
MUSIC: identification of enriched regions in ChIP-Seq experiments using a mappability-corrected multiscale signal processing framework.
Harmanci A1, Rozowsky J, Gerstein M.

ChiLin 2.1 – ChIP-seq Data quality and Analysis Pipeline

ChiLin 2.1

:: DESCRIPTION

ChiLin provides a more flexible handle for understanding the ChIP-seq analysis workflow.

::DEVELOPER

X. Shirley Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 ChiLin

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2016 Oct 3;17(1):404.
ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline.
Qin Q, et al.

scHMM 1.1.0 – Sparsely correlated hidden Markov model for ChIP-seq data Analysis

scHMM 1.1.0

:: DESCRIPTION

scHMM (correlated hidden Markov models) is a package for analyzing sequence read count data in multiple ChIPseq experiments using sparsely correlated hidden Markov models (HMM).

::DEVELOPER

scHMM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/ WIndows
  • C Compiler

:: DOWNLOAD

 scHMM

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Mar 1;29(5):533-41. doi: 10.1093/bioinformatics/btt012. Epub 2013 Jan 16.
Sparsely correlated hidden Markov models with application to genome-wide location studies.
Choi H1, Fermin D, Nesvizhskii AI, Ghosh D, Qin ZS.

ARCHS4 – All RNA-seq and CHIP-seq Signature Search Space

ARCHS4

:: DESCRIPTION

ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels.

::DEVELOPER

Ma’ayan Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nat Commun. 2018 Apr 10;9(1):1366. doi: 10.1038/s41467-018-03751-6.
Massive mining of publicly available RNA-seq data from human and mouse.
Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma’ayan A.

SeqSite 1.0.0 – ChIP-Seq Binding Site Identification

SeqSite 1.0.0

:: DESCRIPTION

SeqSite was developed for detecting transcription factor binding sites from ChIP-seq data.SeqSite is an efficient and easy-to-use software tool implementing a novel method for identifying and pinpointing transcription factor binding sites. It first detects transcription factor binding regions by clustering tags and statistical hypothesis testing, and locates every binding site in detected binding regions by modeling the tag profiles. It can pinpoint closely spaced adjacent binding sites from ChIP-seq data.

::DEVELOPER

SeqSite team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • C Compiler

:: DOWNLOAD

  SeqSite

:: MORE INFORMATION

Citation

Xi Wang and Xuegong Zhang.
Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite.
BMC Systems Biology, 5(Suppl 2):S3.

ChIPComp 1.15.0 – Quantatitive Comparison of multiple ChIP-seq dataset

ChIPComp 1.15.0

:: DESCRIPTION

ChIPComp is an R package for quantatitive comparison of multiple ChIP-seq datasets. ChIPComp improves the differential protein binding or histone modification analyses for ChIP-seq data by properly considering the background information from control samples and different signal to noise ratios. ChIPComp also works for general experimental design.

::DEVELOPER

Hao Wu, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows /Linux/ MacOsX
  • R

:: DOWNLOAD

 ChIPComp

:: MORE INFORMATION

Citation

A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.
Chen L, Wang C, Qin ZS, Wu H.
Bioinformatics. 2015 Feb 13. pii: btv094.