The identification of potential regulatory motifs in new sequence data is increasingly important for experimental design. Those motifs are commonly located by matches to IUPAC strings derived from consensus sequences. Although this method is simple and widely used, a major drawback of IUPAC strings is that they necessarily remove much of the information originally present in the set of sequences. Nucleotide distribution matrices retain most of the information and are thus better suited to evaluate new potential sites. However, sufficiently large libraries of pre-compiled matrices are a prerequisite for practical application of any matrix-based approach and are just beginning to emerge. Here we present a set of tools for molecular biologists that allows generation of new matrices and detection of potential sequence matches by automatic searches with a library of pre-compiled matrices. We also supply a large library (> 200) of transcription factor binding site matrices that has been compiled on the basis of published matrices as well as entries from the TRANSFAC database, with emphasis on sequences with experimentally verified binding capacity. Our search method includes position weighting of the matrices based on the information content of individual positions and calculates a relative matrix similarity. We show several examples suggesting that this matrix similarity is useful in estimating the functional potential of matrix matches and thus provides a valuable basis for designing appropriate experiments.

We present a new version of the program MatInspector that identifies TFBS in nucleotide sequences using a large library of weight matrices. By introducing a matrix family concept, optimized thresholds, and comparative analysis, the enhanced program produces concise results avoiding redundant and false-positive matches. We describe a number of programs based on MatInspector allowing in-depth promoter analysis (DiAlignTF, FrameWorker) and targeted design of regulatory sequences (SequenceShaper).

werner, morgenstern@gsf.de

The WWW version of FastM is accessible at: http://www.gsf.de/cgi-bin/fastm. pl and http://genomatix.gsf.de/cgi-bin/fastm2/fastm.pl

DNA sequences that are involved in the control of gene expression in eukaryotes have been collected in conjunction with the proteins binding to and acting through them (TRANSFAC data set). To make these data accessible, the TRANSFAC retrieval program (TRP) has been developed as a database management system which is based upon the network model. This database model possesses particular advantages for data management of a complex structure. The aim of TRP is to provide an easily handled statistical basis for a computational approach to transcription control.