BaalChIP 1.12.0 – Bayesian Analysis of Allele-specific Transcription Factor Binding in Cancer Genomes

BaalChIP 1.12.0

:: DESCRIPTION

BaalChIP ( Bayesian Analysis of Allelic imbalances from ChIP-seq data) corrects for the effect of background allele frequency on the observed ChIP-seq read counts jointly analyses multiple ChIP-seq samples across a single variant.

::DEVELOPER

the Markowetz lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • R
  • BioCOnductor

:: DOWNLOAD

BaalChIP

:: MORE INFORMATION

Citation

Genome Biol, 18 (1), 39 2017 Feb 24
BaalChIP: A probabilistic framework for reconstructing intra-tumor phylogenies
I. de Santiago, W. Liu, K. Yuan, M. O’Reilly, CS. Chilamakuri, B. Ponder, K. Meyer, F. Markowetz

Haystack 0.5.5 – Epigenetic Variability and Transcription Factor Motifs Analysis Pipeline

Haystack 0.5.5

:: DESCRIPTION

Haystack is a suite of computational tools implemented in a Python 2.7 package called haystack_bio to study epigenetic variability, cross-cell-type plasticity of chromatin states and transcription factors (TFs) motifs providing mechanistic insights into chromatin structure, cellular identity and gene regulation.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

Haystack

:: MORE INFORMATION

Citation

Bioinformatics, 34 (11), 1930-1933 2018 Jun 1
Haystack: Systematic Analysis of the Variation of Epigenetic States and Cell-Type Specific Regulatory Elements
Luca Pinello, Rick Farouni, Guo-Cheng Yuan

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.

activeTF – Identify coordinately activated Transcription Factors

activeTF

:: DESCRIPTION

activeTF is to find a set of coordinately activated Transcription Factors (TF) from a given gene expression dataset.

::DEVELOPER

Haiyan Nancy Hu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • Python

:: DOWNLOAD

 activeTF

:: MORE INFORMATION

Citation:

Genomics. 2010 Mar;95(3):143-50. doi: 10.1016/j.ygeno.2009.12.006. Epub 2010 Jan 6.
An efficient algorithm to identify coordinately activated transcription factors.
Hu H.

TFBS Evo 1.0 – Tracing the Evolution of Lineage-specific Transcription Factor Binding Sites

TFBS Evo 1.0

:: DESCRIPTION

TFBS Evo is a model which traces the evolution of lineage-specific transcription factor binding sites without relying on detailed base-by-base cross-species alignments.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

TFBS Evo

:: MORE INFORMATION

Citation

Tracing the evolution of lineage-specific transcription factor binding sites in a birth-death framework.
Yokoyama KD, Zhang Y, Ma J.
PLoS Comput Biol. 2014 Aug 21;10(8):e1003771. doi: 10.1371/journal.pcbi.1003771.

TFBIND – Searching Transcription Factor Binding Sites

TFBIND

:: DESCRIPTION

TFBIND is a software for searching transcription factor binding sites (including TATA boxes, GC boxes, CCAAT boxes, transcription start sites (TSS)).

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 1999 Jul-Aug;15(7-8):622-30.
Estimating transcription factor bindability on DNA.
Tsunoda T, Takagi T.

ZifNet – Context-depedent DNA Recognition Code for C2H2 Zinc Figner Transcription Factors

ZifNet

:: DESCRIPTION

ZifNet is the package that can be used to predict the DNA binding model for any given a C2H2 zinc finger based on back-propagation algorithm. It includes two parts: First, identify the optimal DNA-zinc finger interaction model with the core C programs of ZifNet. And second, predict DNA-binding weight matrix models for Zinc fingers using the auxiliary codes, and the defined DNA-zinc finger interaction model derived from the last step.

::DEVELOPER

Stormo Lab in Department of Genetics, Washington University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 ZifNet

:: MORE INFORMATION

DME 1.0.0 – Discover Transcription Factor Binding Site Motifs

DME 1.0.0

:: DESCRIPTION

DME (Discriminating Motif Enumerator) is a program that discovers transcription factor binding site motifs in nucleotide sequences. DME identifies motifs, represented as position weight matrices, that are overrepresented in one set of sequences relative to another set. The ability to directly optimize relative overrepresentation is a unique feature of DME, making DME an ideal tool for analyzing promoters of transcripts found to have differential expression in a particular context. The optimization procedure is based on an enumerative algorithm that is guaranteed to identify optimal motifs from a discrete space of matrices with a specific lower bound on information content. This strategy scales very well with the number and length of the sequences used, and is well-suited to analyzing very large data sets.

::DEVELOPER

The Smith Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

DME

MORE INFORMATION

Citation

Bioinformatics. 2007 Jan 1;23(1):21-9. Epub 2006 Oct 18.
Computational prediction of novel components of lung transcriptional networks.
Martinez MJ, Smith AD, Li B, Zhang MQ, Harrod KS.

TFBSs 1.0 – Predicting Transcription Factor Binding Sites

TFBSs 1.0

:: DESCRIPTION

TFBSs is a web server for Predicting transcription factor binding sites.

::DEVELOPER

The Li’s Group of Theoretical Biophysics and Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Borowser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Guo-liang Fan and Qianzhong Li, Keli Yang,
TFBSs: a web server for Predicting transcription factor binding sites.
2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2012,1:65-68

JASPAR 2014 1.9.0 – Transcription Factor Binding Profile Database

JASPAR 2014 1.9.0

:: DESCRIPTION

JASPAR is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species.

::DEVELOPER

The Wasserman Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/MacOsX/Linux
  • R package/BioConductor/BioPython

:: DOWNLOAD

  JASPAR for BioConductor/ for R / for Biopython

:: MORE INFORMATION

Citation

JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles.
Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, Buchman S, Chen CY, Chou A, Ienasescu H, Lim J, Shyr C, Tan G, Zhou M, Lenhard B, Sandelin A, Wasserman WW.
Nucleic Acids Res. 2013 Nov 4.