Tag: Transcription Factor

TRAP 3.05 – Transcription factor Affinity Prediction

TRAP 3.05

:: DESCRIPTION

TRAP (Transcription factor Affinity Prediction) calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation–sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism.

::DEVELOPER

TRAP Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ compiler
  • R Package

:: DOWNLOAD

  TRAP

:: MORE INFORMATION

Citation

Morgane Thomas-Chollier, Andrew Hufton, Matthias Heinig, Sean O’Keeffe, Nassim El Masri, Helge G Roider, Thomas Manke and Martin Vingron.
Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs.
Nature Protocols, 3;6(12):1860-9. (2011)

SABINE 1.2 – Prediction of the Binding Specificity of Transcription Factors using Support Vector Regression

SABINE 1.2

:: DESCRIPTION

SABINE (Stand-alone binding specificity estimator) is a tool to predict the binding specificity of a transcription factor (TF), given its amino acid sequence, species, structural superclass and DNA-binding domains. For convenience, the superclass and DNA-binding domains of a given TF can be predicted based on sequence homology with TFs in the training of SABINE.

::DEVELOPER

the Center for Bioinformatics Tübingen (Zentrum für Bioinformatik Tübingen, ZBIT).

:: SCREENSHOTS

SABINE

:: REQUIREMENTS

  • Linux
  • Java

:: DOWNLOAD

  SABINE

:: MORE INFORMATION

Citation

PLoS One. 2013 Dec 12;8(12):e82238. doi: 10.1371/journal.pone.0082238. eCollection 2013.
TFpredict and SABINE: Sequence-Based Prediction of Structural and Functional Characteristics of Transcription Factors.
Eichner J, Topf F1, Dräger A, Wrzodek C, Wanke D, Zell A.

MotifRaptor v0.3.0 – Explore the effect of Genetic Variants on Transcription Factor Binding Sites

MotifRaptor v0.3.0

:: DESCRIPTION

Motif-Raptor, a TF-centric computational tool that integrates sequence-based predic-tive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants

::DEVELOPER

Pinello Lab.

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

Motif-Raptor

:: MORE INFORMATION

Citation

Yao Q, Ferragina P, Reshef Y, Lettre G, Bauer DE, Pinello L.
Motif-Raptor: A Cell Type-Specific and Transcription Factor Centric Approach for Post-GWAS Prioritization of Causal Regulators.
Bioinformatics. 2021 Feb 3:btab072. doi: 10.1093/bioinformatics/btab072. Epub ahead of print. PMID: 33532840.

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

Pinello 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

TimeTP 1.0 – Influence Maximization in Time bounded network Identifies Transcription Factors Regulating Perturbed Pathways

TimeTP 1.0

:: DESCRIPTION

TimeTP is a novel time-series analysis method for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein-protein interaction network to locate TFs triggering the perturbation.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

TimeTP

:: MORE INFORMATION

Citation

Jo K, Jung I, Moon JH, Kim S.
Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways.
Bioinformatics. 2016 Jun 15;32(12):i128-i136. doi: 10.1093/bioinformatics/btw275. PMID: 27307609; PMCID: PMC4908359.

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.

MITSU 1.0 – Stochastic EM for Transcription Factor Binding Site Motif Discovery

MITSU 1.0

:: DESCRIPTION

MITSU (Motif discovery by ITerative Sampling and Updating) is a command line application for the discovery of transcription factor binding site (TFBS) motifs.

::DEVELOPER

Alastair M Kilpatrick

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  MacOsX / Windows
  • Java
  • BioJava

:: DOWNLOAD

 MITSU

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jun 15;30(12):i310-i318. doi: 10.1093/bioinformatics/btu286.
Stochastic EM-based TFBS motif discovery with MITSU.
Kilpatrick AM, Ward B, Aitken S.

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.

CSDeconv 1.03 – Determine Locations of Transcription Factor Binding from ChIP-seq data

CSDeconv 1.03

:: DESCRIPTION

CSDeconv maps transcription factor binding sites from ChIP-seq data to high resolution using a blind deconvolution approach

::DEVELOPER

Desmond Lun

:: SCREENSHOTS

N/a

::REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 CSDeconv

:: MORE INFORMATION

Citation

D. S. Lun, A. Sherrid, B. Weiner, D. R. Sherman, and J. E. Galagan.
A blind deconvolution approach to high-resolution mapping of transcription factor binding sites from ChIP-Seq data.
Genome Biol., 10(12):R142, December 2009.