TFBSs 1.0 – Predict Transcription Factor Binding Sites

TFBSs 1.0

:: DESCRIPTION

TFBSs is a web-servers for predicting transcription factor binding sites in the Saccharomyces cerevisiae genome.

::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.

BioSpique 1.0 – Transcription Factor Binding Site (TFBS) Modeling and Discovery

BioSpique 1.0

:: DESCRIPTION

BioSpique is a suite of software tools to be used for transcription factor binding site (TFBS) modeling and discovery.

::DEVELOPER

University of Florida Genetics Institute

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 BioSpique

:: MORE INFORMATION

Citation

Evolving Spiking Neural Networks for Predicting Transcription Factor Binding Sites
Sichtig H, Schaffer JD, Riva A.
In: Proceedings of IEEE International Joint Conference on Neural Networks (ICNN 2010) and World Congress on Computational Intelligence (WCCI 2010). Barcelona, Spain. 2010

CTF 0.91 – CRF-based Transcription Factor Binding Sites Finding System

CTF 0.91

:: DESCRIPTION

CTF is a conditional random field (CRF)based transcript factor binding site (TFBS) finding system.

::DEVELOPER

Dr. Chaochun Wei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 CTF

:: MORE INFORMATION

Citation:

BMC Genomics. 2012;13 Suppl 8:S18. doi: 10.1186/1471-2164-13-S8-S18. Epub 2012 Dec 17.
CTF: a CRF-based transcription factor binding sites finding system.
He Y1, Zhang Y, Zheng G, Wei C.

MatCompare 1.3 – Compare Transcription Factor Binding Site (TFBS) position Frequency Matrices (PFMs)

MatCompare 1.3

:: DESCRIPTION

The MatCompare software provides methods for comparing transcription factor binding site (TFBS) position frequency matrices (PFMs). The user has a choice of using the Kullback-Leibler divergence, the chi squared test or the Fisher-Irwin exact test of similarity.

::DEVELOPER

zhang lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 MatCompare

:: MORE INFORMATION

Citation:

Dustin E Schones, Pavel Sumazin and Michael Q Zhang
Similarity of position frequency matrices for transcription factor binding sites
Bioinformatics 2005 21(3):307-313; doi:10.1093/bioinformatics/bth480

ORCAtk 1.0.0 – Transcription Factor Binding Site Detection using Phylogenetic Footprinting

ORCAtk 1.0.0

:: DESCRIPTION

The ORCA Toolkit is a system for finding putative regulatory regions and transcription factor binding sites (TFBSs) within those regions.

::DEVELOPER

The Wasserman Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/MacOsX/Linux
  • Perl

:: DOWNLOAD

 ORCAtk

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2009 Jan;37(Database issue):D54-60. doi: 10.1093/nar/gkn783. Epub 2008 Oct 29.
The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences.
Portales-Casamar E, Arenillas D, Lim J, Swanson MI, Jiang S, McCallum A, Kirov S, Wasserman WW.

LocaMo Finder – Predicting local Enrichment of Transcription Factor Binding sites

LocaMo Finder

:: DESCRIPTION

LocaMo (Local Motif) Finder is a program for the detection of local enrichment of transcription factor binding sites (TFBSs) in DNA sequences.

::DEVELOPER

The Systems Immunology Laboratory, Osaka University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013 Jan 21;14:26. doi: 10.1186/1471-2105-14-26.
A Parzen window-based approach for the detection of locally enriched transcription factor binding sites.
Vandenbon A, Kumagai Y, Teraguchi S, Amada KM, Akira S, Standley DM.

MILLIPEDE 1.1.0 – Model for Identifying Transcription Factor Binding Sites

MILLIPEDE 1.1.0

:: DESCRIPTION

MILLIPEDE is a method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). The sofware is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well.

::DEVELOPER

Alex Hartemink

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • R package

:: DOWNLOAD

 MILLIPEDE

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2013:80-91.
Using DNase digestion data to accurately identify transcription factor binding sites.
Luo K, Hartemink AJ.