1 - 40 of 40 results

TFBS / Transcription Factor Binding Site analysis

Permits transcription factor binding site detection and analysis. TFBS is a package that provides a set of integrated, object-oriented Perl modules. It integrates generation, manipulation, storage and retrieval of patterns for transcription factor binding sites. It also allows users to scan sequences and alignments of sequences for matches to these patterns. This method reduces program coding time, enabling computational biologists to explore biologically meaningful topics rather than managing low-level data structures.

CMA / Composite Module Analyst

Identifies promoter-enhancer models based on the composition of transcription factor binding sites (TFBS) and their pairs. CMA uses an approach for defining a promoter model based on composition of single TFBS as well as their pairs located inside local regulatory domains. It can be used to analyze data and proposes factor combinations that are playing key roles in transcriptional regulation in a given biological context. CMA is a part of the commercial software ExPlain.

INSECT / IN-silico SEarch for Co-occurring Transcription factors

Allows to analyze genomic sequence data for in silico cis-regulatory modules (CRMs) prediction and analysis. INSECT is a web server which allows a complete and flexible analysis of the predicted co-regulating Transcription Factors (TFs) and Transcription Factor Binding Sites (TFBSs). The software integrates many different search options and additional results such as automatic regulatory sequences retrieval from Ensembl, phylogenetic footprinting, nucleosome occupancy calculations, and gene ontology (GO) information.


Allows identification of transcription factor binding sites (TFBS) in nucleotide sequences, using a large library of matrix descriptions. MatInspector is a TFBS prediction programs that uses the information of core positions, nucleotide distribution matrix and Ci-vector to scan sequences of unlimited length for pattern matches. The software can find the potential binding sites of various activators and repressors that bind to specific DNA regulatory sequences. It is part of the Genomatix Software Suite.


Implements a method for accurately predicting cell-specific transcription factor (TF) binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. TFImpute is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of single nucleotides polymorphisms (SNP)s on TF binding. The TFImpute method was implemented using Theano and is freely available for download for non-commercial use.


A virtual laboratory for the study of transcription factor binding sites (TFBS) in DNA sequences. PROMO is a program for the prediction of TFBS in a single sequence or in a group of related sequences. Other programs exist for the prediction of TFBS that use weight matrices but PROMO contains a number of unique features. Among them we would like to highlight the following: (i) the possibility to select sites from any species or group of species of interest; (ii) the automatic construction of matrices that correspond to the selected taxonomic level; (iii) information in the output on other genes that may be similarly regulated; and (iv) the possibility to analyze and compare multiple sequences at the same time.


Predicts transcription factor binding sites (TFBS). PhysBinder algorithm makes use of both direct (the sequence) and indirect readout features (biophysical properties such as the bendability of the DNA) of protein-DNA complexes and significantly outperforms current state of the art approaches for in silico TFBS identification. Users can submit sequences for analysis in the PhysBinder integrative algorithm and choose from more than 60 different TF binding models. The results of this analysis are shown in an intuitive visualization and offer a way to steer future wet-lab experiments.

ORCAtk / ORCA toolkit

A software package developed in support of diverse regulatory sequence analysis projects associated to the PAZAR database of gene regulatory information. ORCAtk is a system for finding putative regulatory regions and transcription factor binding sites (TFBSs) within those regions. This is accomplished through phylogenetic footprinting by either aligning orthologous sequences with the ORCA aligner (pairwise analysis) or by extracting a phastCons score profile (multi-species analysis) and then identifying regions of significant identity. These regions may then be scanned with selected TFBS profile matrices. Matrices which score about a given threshold are then reported as putative TFBSs.

BPAC / Binding Prediction from ACcessibility data

Allows to predict transcription factor (TF) binding events, using available TF ChIP-Seq data as a gold standard. BPAC is a supervised classification approach which selects the features from sequence related information, gene related information, and chromatin accessibility information. This tool was developed in order to work on TF binding sites based on the universal “rules”. It is available through a package software and a web application.

EPIANN / Enhancer-Promoter Interaction Attention-based Neural Network

Predicts enhancer-promoter interactions exclusively using sequence features. EPIANN is able to learn an attention matrix for each enhancer-promoter pair. It recognizes corresponding and important sub-regions within the enhancer and promoter. This tool can highlight the parts of enhancer and promoter sequence that drive predictions. It can be useful to analyze features in the original sequence space and provides insights into the mechanism of enhancer-promoter interaction (EPI) events.


Identifies putative transcription factor-binding sites (TFBS) in bacterial genomes. PREDetector allows users to freely fix the DNA-motif screening parameters, and provides a statistical means to estimate the reliability of the prediction output. The version 2.0 offers an interactive table as well as graphics to dynamically alter the main screening parameters with automatic update of the list of identified putative TFBS. PREDetector 2.0 also has the following additional options: (i) access to genome sequences from different databases, (ii) access to weight matrices from public repositories, (iii) visualization of the predicted hits in their genomic context, (iv) grouping of hits identified in the same upstream region, (v) possibility to store the performed jobs, and (vi) automated export of the results in various formats.


Searches putative transcription factor binding sites in DNA sequences. Match is closely interconnected and distributed with the TRANSFAC database. It uses a library of mononucleotide weight matrices from TRANSFAC database and provides the possibility to search for a great variety of different transcription factor binding sites. Several sets of optimized matrix cut-off values are built in the system to provide a variety of search modes of different stringency. Users may construct and save their profile which are selected subsets of matrices including default or user-defined cut-off values.

ProbTF / Probabilistic Inference of Transcription Factor Binding

Predicts transcription factor binding using experimentally verified Position Weight Matrices (PWMs). ProbTF provides a probabilistic framework which has three important features. First, ProbTF is probabilistic in nature and thus outputs a probability of binding (as opposed to a p-value). Second, the method answers the question of whether the whole promoter has a binding site. Third, ProbTF provides a principled way of combining multiple data sources, such as evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip, and other prior knowledge, into a unified probabilistic framework.

IMAGE / Integrated analysis of Motif Activity and Gene Expression changes of transcription factors

Determines causal transcription factors, as well as their binding sites and target genes. IMAGE is based on machine learning methods. It is able to predict motif activity based on contribution to enhancer activity, as well as, motif activity based on contribution to gene expression. This tool can deduce target sites of transcription factors that play a causal role in mediating a given transcriptional response.


Enables users to find the positions of DNA matrices on over 900 human miRNA genomic sequences. Infinity requires as input a consensus matrix, limited between 4 and 30 nucleotides, and users have to select the DNA region of interest. This tool allows selection of the type of pattern matching algorithm, and assists users to search about each miRNA such as: accession number, genomic location, number of chromosome, strand, transcription start site (TSS) position, length, cluster, or host gene.

SiTaR / Site Tracking and Recognition

A straightforward method for nucleotide composition-based detection of non-random matching motifs applied to prediction of transcription factor binding sites (TFBSs). One of the evident advantages of SiTaR is that it does not require equal length of the searching motifs, nor their alignment, nor a construction of a position weight matrix (PWM) nor any other modeling prior to the search. Any of those steps (aligning, trimming to equallength, etc.) leads to the loss of information (as any generalization) and hence introduces some degree of uncertainty in the final result. In comparison with position weight matrices-based tools, SiTaR shows better performance false discovery rate (FDR) and F-measure, which is the weighted harmonic mean of precision and recall.

TFBIND / Transcription Factor BINDing site

Searches transcription factor binding sites (TFBS). TFBIND uses weight matrix in TRANSFAC R.3.4 database for detecting its preferred binding region in promoters. TFBIND includes the method of re-estimating cut-off values of TFs that mis-recognize other TF preferred regions. Our data source comprised 433 non-redundant vertebrate promoters including viral promoters, from Eukaryotic Promoter Database (EPD) R.50. The method is applied to 205 vertebrate TFs that have frequency matrices in TRANSFAC Ver.3.4 and the cut-off values of all of them can be determined.

PIPES / Probabilistic Integration of PBM Epigenetics and Sequence data

Predicts tissue-specific transcription factor (TF) binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic information. PIPES is able to boost significantly the context specific prediction results compared with using PBM data alone. PIPES also improves upon other methods developed for integrating data to predict transcription factor binding site (TFBS).


A scoring method for measuring sequence-motif affinity based on intuitionistic fuzzy sets (IFS) theory. SCintuit is designed to prevent overestimation of less conserved positions of transcription factor binding sites (TFBSs). For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. SCintuit was used to identify known TFBSs in DNA sequences. This method provides excellent results when dealing with both synthetic and real data.

NemaFootPrinter / Nematode Transcription Factor Scan Through Philogenetic Footprinting

Assists researchers in the identification of conserved non-coding sequence regions between the genomes of the two nematodes C. elegans and C. briggsae. NemaFootPrinter is a web application that proceeds to the identification of conserved functional segments outside exons (putative new gene expression control elements) through comparative genomics. Moreover, the investigator can retrieve the genome DNA sequences of the two orthologous genes, visualize graphically the genes' intron/exon structure and the surrounding DNA regions.


Allows integrated analysis of nucleosome positioning and transcription factor (TF) binding sites in the promoter regions of yeast genes. Ceres is a web-based software platform that provides analysis, visualization and mining tools. The software offers five features: (1) visualization, (2) chromatin viewer, (3) gene set analysis, (4) data mining, and (5) analysis suite. It also provides access to predicted, conserved and experimentally identified binding sites throughout the yeast genome for 105 distinct yeast TFs.


Identifies transcription factor binding sites (TFBS) in DNA sequences. tfscan scans one or more DNA sequences for TFBS from the TRANSFAC database. The taxonomic group can be specified. An output file is written with information on the matches, including sequence ID and accession number, the start and end positions of the match in an input sequence and the sequence of the region where a match has been found. Binding factor information, where available, is given at the end of the matches for each matching entry.


Improves the recognition and the prediction of binding sites for transcription factors. VOMBAT allows users to apply different combinations of markov models and bayesian trees to DNA sequences. Tasks available are learning statistical models from data, predicting putative binding sites, and stratified holdout experiments for different model combinations. For advanced users, VOMBAT provides features for stratified holdout sampling analyses of different model combinations. This allows the user to find the optimal model combination for his or her classification problem and data sets.