RSAT: regulatory sequence analysis tools

Abstract

The regulatory sequence analysis tools (RSAT, http://rsat.ulb.ac.be/rsat/) is a software suite that integrates a wide collection of modular tools for the detection of cis-regulatory elements in genome sequences. The suite includes programs for sequence retrieval, pattern discovery, phylogenetic footprint detection, pattern matching, genome scanning and feature map drawing. Random controls can be performed with random gene selections or by generating random sequences according to a variety of background models (Bernoulli, Markov). Beyond the original word-based pattern-discovery tools (oligo-analysis and dyad-analysis), we recently added a battery of tools for matrix-based detection of cis-acting elements, with some original features (adaptive background models, Markov-chain estimation of P-values) that do not exist in other matrix-based scanning tools. The web server offers an intuitive interface, where each program can be accessed either separately or connected to the other tools. In addition, the tools are now available as web services, enabling their integration in programmatic workflows. Genomes are regularly updated from various genome repositories (NCBI and EnsEMBL) and 682 organisms are currently supported. Since 1998, the tools have been used by several hundreds of researchers from all over the world. Several predictions made with RSAT were validated experimentally and published.

Figure 1.

Flow chart of the regulatory sequence analysis tools. Rounded boxes represent programs, rectangles data and results and trapezoid user input. Bold arrows highlight the succession of tools used by the tool footprint-discovery.

Figure 2.

Example of result from footprint-discovery. (A) overrepresented dyads detected in promoters of orthologs of the yeast gene MET1. (B) PSSM obtained by assembling the most significant dyads and using them as seeds to scan the input sequences. (C) Feature map of the significant dyads. The clumps of overlapping boxes are indicative of good predictions for binding sites.

Figure 3.

Example of matrix-scan result obtained by scanning yeast upstream sequences with matrices representing binding motifs for the transcription factors Met4p and Met31p. (A) Sequence logos representing binding motifs of the Met4p and Met31p transcription factors. (B) Feature map of the predicted sites and CRERs in upstream sequences of 26 yeast genes involved in methionine metabolism. (C) Random control: feature map of the predicted sites and CRERs detected in upstream sequences of 26 yeast genes selected at random. (D) Fragment of a matrix-scan result table reporting putative sites.