TESS 1.0 – Predict Transcription Factor Binding Sites in DNA sequence

TESS 1.0

:: DESCRIPTION

TESS (Transcription Element Search System) reads (selected) PWMs (Partial Weight Matrices) from a file and predicts binding sites on DNA sequences read from another file.

::DEVELOPER

the Computational Biology and Informatics Laboratory at the University of Pennsylvania

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX
  • C Compiler

:: DOWNLOAD

 TESS 

:: MORE INFORMATION

Citation:

Curr Protoc Bioinformatics. 2008 Mar;Chapter 2:Unit 2.6. doi: 10.1002/0471250953.bi0206s21.
Using TESS to predict transcription factor binding sites in DNA sequence.
Schug J.

LASAGNA-Search 2.0 – Searching for Transcription Factor Binding Sites (TFBSs)

LASAGNA-Search 2.0

:: DESCRIPTION

LASAGNA-Search (Length-Aware Site Alignment Guided by Nucleotide Association) is an integrated webtool for transcription factor (TF) binding site search and visualization.

::DEVELOPER

LASAGNA-Search team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/MacOSX
  • Python

:: DOWNLOAD

 LASAGNA-Search

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Mar 13.
LASAGNA-Search 2.0: integrated transcription factor binding site search and visualization in a browser.
Lee C, Huang CH.

TFBayes – Identification of Transcription Factor Binding Sites

TFBayes

:: DESCRIPTION

TFBayes is a software for bayesian analysis of ChIP-Seq data for the identification of transcription factor binding sites.

::DEVELOPER

Philipp Benner

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 TFBayes

:: MORE INFORMATION

Citation

Point estimates in phylogenetic reconstructions.
Benner P, Bačák M, Bourguignon PY.
Bioinformatics. 2014 Sep 1;30(17):i534-i540. doi: 10.1093/bioinformatics/btu461.

PocketDepth – A new depth based algortihm for Identification of Ligand Binding Sites

PocketDepth

:: DESCRIPTION

PocketDepth implements an algorithm for finding binding site (pockets) in protein structures.

::DEVELOPER

Chandra lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No

:: MORE INFORMATION

Citation

J Struct Biol. 2008 Jan;161(1):31-42. Epub 2007 Sep 15.
PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins.
Kalidas Y1, Chandra N.

RamseyHAc 2010 – Prediction of Mammalian Transcription Factor Binding Sites

RamseyHAc 2010

:: DESCRIPTION

RamseyHAc (Histone acetylation) is a package of MATLAB M-files and data files is provided. The data files contain the feature tracks used for predicting TF binding sites, and the “ground truth” binding location data used to train the prediction model (both in MATLAB “.mat” file format). Also included is the file “GenomeRegions.bed” (in UCSC BED format) which describes the genome regions analyzed in this study. The M-files are the functions used to predict transcription factor binding, train the prediction model, and test the prediction model in a cross-validation framework.

::DEVELOPER

MAGNET project

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • MatLab

 RamseyHAc

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Sep 1;26(17):2071-5. doi: 10.1093/bioinformatics/btq405. Epub 2010 Jul 27.
Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites.
Ramsey SA1, Knijnenburg TA, Kennedy KA, Zak DE, Gilchrist M, Gold ES, Johnson CD, Lampano AE, Litvak V, Navarro G, Stolyar T, Aderem A, Shmulevich I.

TFBS Scanner – Scanning for Transcription Factor Binding Sites

TFBS Scanner

:: DESCRIPTION

TFBS Scanner provides command line software toolsets for two useful types of TFBS (Transcription Factor Binding Site) Scanning using Positional Weight Matrices (PWMs):

The Scanner Toolset
Scanning with a fixed background model (can be toggled to 0th order or 1st order Markov, currently set as 1st order), with PWM-specific thresholds generated using this same background

The SpeakerScan Toolset
Scanning with a local background correction, outputs log-likelihood scores which can then be post-processed as desired

::DEVELOPER

Megraw Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Java

:: DOWNLOAD

  TFBS Scanner

:: MORE INFORMATION

Citation

Morton T, Petricka J, Corcoran DL, Li S, Winter CM, Carda A, Benfey PN, Ohler U, Megraw M. (2014).
Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures.
Plant Cell, 26:2746-60.

Megraw M, Pereira F, Jensen ST, Ohler U, Hatzigeorgiou AG. (2009).
A transcription factor affinity based code for mammalian transcription initiation.
Genome Research, 19:644-56.

NHR-scan – Predictor of Nuclear Hormone Receptor Binding Sites

NHR-scan

:: DESCRIPTION

NHR-scan is a computational predictor of nuclear hormone receptor binding sites (NHRBS).

::DEVELOPER

The Wasserman Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Mol Endocrinol. 2005 Mar;19(3):595-606. Epub 2004 Nov 24.
Prediction of nuclear hormone receptor response elements.
Sandelin A, Wasserman WW.

TargetS – Predictor for Targeting Protein-ligand Binding Sites

TargetS

:: DESCRIPTION

TargetS is a new ligand-specific template-free predictor for targeting protein-ligand binding sites from primary sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

IEEE/ACM Trans Comput Biol Bioinform. 2013 Jul-Aug;10(4):994-1008. doi: 10.1109/TCBB.2013.104.
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering.
Dong-Jun Yu, Jun Hu, Jing Yang, Hong-Bin Shen, Jinhui Tang, and Jing-Yu Yang,

OSML – Predicting Protein-Ligand Binding Sites

OSML

:: DESCRIPTION

OSML is a query-driven dynamic machine learning model for predicting protein-ligand binding sites

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Dong-Jun Yu, Jun Hu, Hong-Bin Shen et al.,
Constructing Query-Driven Dynamic Machine Learning Model with Application to Protein-Ligand Binding Sites Prediction,
IEEE Trans Nanobioscience. 2015 Jan;14(1):45-58. doi: 10.1109/TNB.2015.2394328.

HITS-CLIP – Detect RNA-protein Binding Sites

HITS-CLIP

:: DESCRIPTION

HITS-CLIP provides essential MATLAB functions to implement our model for the identification of binding sites using heterogeneous logit models via semi-supervised learning.

::DEVELOPER

The Quantitative Biomedical Research Center 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows / MacOsX
  • MatLab

:: DOWNLOAD

 HITS-CLIP

:: MORE INFORMATION