ChIPpeakAnno 3.24.2 – Annotate ChIP-seq and ChIP-chip data

ChIPpeakAnno 3.24.21

:: DESCRIPTION

ChIPpeakAnno is a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions.

::DEVELOPER

Program in Gene Function and Expression@umassmed

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
BioConductor
R package

:: DOWNLOAD

ChIPpeakAnno

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2010 May 11;11:237. doi: 10.1186/1471-2105-11-237.
ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data.
Zhu LJ, Gazin C, Lawson ND, Pagès H, Lin SM, Lapointe DS, Green MR.

SeqGL 1.1.4 – Identifies Context-dependent Binding Signals in ChIP-seq and DNase-seq profiles

SeqGL 1.1.4

:: DESCRIPTION

SeqGL is a new group lasso-based algorithm to extract multiple transcription factor (TF) binding signals from ChIP- and DNase-seq profiles.

::DEVELOPER

Leslie Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R

:: DOWNLOAD

SeqGL

:: MORE INFORMATION

Citation

SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.
Setty M, Leslie CS.
PLoS Comput Biol. 2015 May 27;11(5):e1004271. doi: 10.1371/journal.pcbi.1004271.

HiChIP – A high-throughput pipeline for Integrative Analysis of ChIP-Seq data

HiChIP

:: DESCRIPTION

HiChIP pipeline is designed for performing comprehensive analysis of chromatin immunoprecipitation and sequencing (ChIP-Seq) data.

::DEVELOPER

Bioinformatics Program, Division of Biomedical Statistics and Informatics, Mayo Clinic Research

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / VirtualBox

:: DOWNLOAD

HiChIP

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2014 Aug 15;15:280. doi: 10.1186/1471-2105-15-280.
HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data.
Yan H, Evans J, Kalmbach M, Moore R, Middha S, Luban S, Wang L, Bhagwate A, Li Y, Sun Z, Chen X, Kocher JP

ChIP-Enrich 2.14.0 – Gene Set Enrichment Testing for ChIP-seq data

ChIP-Enrich 2.14.0

:: DESCRIPTION

ChIP-Enrich tests ChIP-seq peak data for enrichment of biological pathways, Gene Ontology terms, and other types of gene sets

::DEVELOPER

The Sartor Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
R package

:: DOWNLOAD

ChIP-Enrich

:: MORE INFORMATION

Citation

ChIP-Enrich: gene set enrichment testing for ChIP-seq data.
Welch RP, Lee C, Imbriano PM, Patil S, Weymouth TE, Smith RA, Scott LJ, Sartor MA.
Nucleic Acids Res. 2014 May 30. pii: gku463.

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

ARCHS4

:: 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.

CSEM v2.4 – ChIP-Seq multi-read allocation using Expectation-Maximization

CSEM v2.4

:: DESCRIPTION

CSEM: The ChIP-Seq sibling to RSEM. Using an EM-inspired heuristic, CSEM allocates reads from ChIP-Seq data sets, allocating reads that map to multiple positions fractionally.

::DEVELOPER

Colin Dewey, Bo Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

RSEM

:: MORE INFORMATION

SignalSpider – Probabilistic Modeling and Pattern Discovery on Multiple Normalized ChIP-Seq Signal Profile

SignalSpider

:: DESCRIPTION

SignalSpider is a probabilistic model for deciphering the combinatorial binding of DNA-binding proteins. The model (SignalSpider) aims at modeling and extracting patterns from multiple ChIP-Seq profiles.

::DEVELOPER

Ka-Chun Wong

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
MCR (Matlab Compiler Runtime)

:: DOWNLOAD

SignalSpider

:: MORE INFORMATION

Citation

SignalSpider: Probabilistic Pattern Discovery on Multiple Normalized ChIP-Seq Signal Profiles.
Wong KC, Li Y, Peng C, Zhang Z.
Bioinformatics. 2014 Sep 5. pii: btu604.

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

:: 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.