1 - 27 of 27 results


A method to enhance alignment of short motifs, even if their mutual sequence similarity is only partial. ConBind improves the identification of conserved transcription factor binding sites (TFBSs) by improving the alignment accuracy of TFBS families within orthologous DNA sequences. In addition to the analysis of known regulatory regions, our web tool is useful for the analysis of TFBSs on so far unknown DNA regions identified through ChIP-sequencing. Our pipeline is based on three steps: (i) identification of suitable orthologous regions, (ii) motif-aware alignment of the orthologous sequences and finally (iii) visualization of conserved TFBSs. ConBind exposes a RESTful API interface, which allows to programmatically access (i.e. without using the web user interface) and integrate ConBind in other analysis workflows and tools.

Master Regulator Identification

Identifies the master regulator transcription factor in a genome. Master Regulator Identification is advantageous in terms of narrowing down the search space for potential candidate transcription factor biomarkers that can be targeted for drug development of complex diseases. Also, the fact that our method uses only a single data source, e.g. gene expression data, for accurately identifying the master regulator transcription factor makes it very useful in case there is limitation in data sources and data from multiple platforms are not available.


A comparative tool for analyzing the regulatory potential of noncoding sequences. Our ability to experimentally identify functional noncoding sequences is extremely limited, therefore, rVISTA attempts to fill this great gap in genomic analysis by offering a powerful approach for eliminating TFBSs least likely to be biologically relevant. rVISTA analysis proceeds in four main steps: (i) detect TFBS matches in each individual sequence using PWMs from the TRANSFAC database, (ii) identify pairs of locally aligned TFBSs, (iii) select TFBSs present in regions of high DNA conservation and (iv) create a graphical display that dynamically overlays individual or clustered TFBSs with the conservation profile of the genomic locus. The rVISTA web server is closely interconnected with the TRANSFAC database, allowing users to either search for matrices present in the TRANSFAC library collection or search for user-defined consensus sequences.


Combines evidence from matching sites found in orthologous data from several related species with evidence from multiple sites within an intergenic region, to better detect regulons. The orthologous sequence data may be multiply aligned, unaligned, or a combination of aligned and unaligned. In aligned data, PhyloScan statistically accounts for the phylogenetic dependence of the species contributing data to the alignment and, in unaligned data, the evidence for sites is combined assuming phylogenetic independence of the species. The statistical significance of the gene predictions is calculated directly, without employing training sets.

iMADS / integrative Modeling and Analysis of Differential Specificity

Analyzes non-coding variants. iMADS is a general framework that contains a high-throughput data and computational models. It allows users to apply our models for each transcription factor (TF) or TF pair to make predictions on any genomic or custom DNA sequence. This method proves that genomic sites differentially preferred by TF paralogs have different sequence features and DNA shape profiles, and they are involved in distinct biological functions.


Gathers annotation and analysis of binding sites for site-specific transcription factors in the promoter and enhancer regions of genes. NFI-Regulome provides the control regions of genes that have been shown to be regulated by Nuclear Factor I (NFI) transcription factors in the primary literature. It enables rapid comparisons of the size, composition, and organizational structure of the cis-regulatory regions of NFI-regulated genes, selected either by disease-relevance, cell, tissue or developmental stage.


Incorporates a variety of genome-wide data types relevant to gene regulation. CressInt is a user-friendly, freely accessible web server for integrating and analyzing genome-scale A. thaliana gene datasets. It includes transcription factor (TF) binding site models, ChIP-seq, DNase-seq, eQTLs, and genome-wide association study (GWAS). This package is useful in (i) the identification of TFs binding to the promoter of a gene of interest, (ii) the identification of genetic variants that are likely to impact TF binding based on a ChIP-seq dataset and (iii) the identification of specific TFs whose binding might be impacted by phenotype-associated variants.

DISCOVER / DIScriminative COnditional random field for motif recoVERy

Enjoys the dual advantage of modeling cis-regulatory modules architecture of sequences. DISCOVER is used in sequence analysis, most notably in gene prediction since coding regions are much better characterized in terms of sequence level features with respect to regulatory regions. Among advantages of the conditional random field model are the facts that the user can incorporate new features at will and can configure publicly available tool to add new genetic and epigenetic features.


A motif finding method which constructs a subspace based on the covariance of numerical DNA sequences. When a candidate sequence is projected into the modeled subspace, a threshold in the Q-residuals confidence allows us to predict whether this sequence is a binding site. Using the TRANSFAC and JASPAR databases, we compared our Q-residuals detector with existing PSSM methods. In most of the studied TF binding sites, the Q-residuals detector performs significantly better and faster than MATCH and MAST. As compared with Motifscan, a method which takes into account interdependences, the performance of the Q-residuals detector is better when the number of available sequences is small.

ReLA / REgulatory region Local Alignment tool

A method optimized with the Smith-Waterman algorithm that identifies gene regulatory regions in genomic DNA sequences through local searches of clusters of coincident transcription factor binding sites between two or more DNA sequences. ReLA can identify these regulatory regions through the comparison of homologous (orthologs and paralogs) or co-expressed sequences. Our server also allows the identification of more than one regulatory region (alternative promoters) within the reference sequence.


A motif sampling algorithm that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and "background" intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account.


Identifies the correct motif from a set of co-expressed genes and predicts target genes of individual Transcription Factors (TFs). cisTargetX uses statistical correlations between co-expressed gene sets and genome-wide target prioritizations on the basis of rankings of conserved motif cluster predictions. It can determine whether a set of candidate genes, for example a mixture of direct and indirect target genes, is enriched for direct targets of a certain TF or combination of TFs.

iCR / identify Conserved target of a Regulon

Permits high throughput, detailed and fully automated prediction of potential binding. iCR is a web server tool, for identification of conserved high priority targets of a regulatory protein from heterologous sequence data of prokaryotes (which includes regulatory sequences of genes and their orthologs in other species). Users can easily distinguish biologically important motifs from background noise based on their cross-species conservation.


A graphical, web-based interface for computer-assisted prediction of regulatory regions in higher eukaryotes, powered by cross-species comparison. Besides the intuitive interface design, ConSite integrates several features not found in other transcription factor binding site (TFBS) prediction services such as TESS or rVista. The features include a curated model dataset, a computer-assisted input sequence selection and a powerful underlying set of programming modules for genome-scale analysis.