Bioprospector: Discovering Conserved Dna Motifs in Upstream Regulatory Regions of Co-Expressed Genes

Abstract

The development of genome sequencing and DNA microarray analysis of gene expression gives rise to the demand for data-mining tools. BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs. BioProspector uses zero to third-order Markov background models whose parameters are either given by the user or estimated from a specified sequence file. The significance of each motif found is judged based on a motif score distribution estimated by a Monte Carlo method. In addition, BioProspector modifies the motif model used in the earlier Gibbs samplers to allow for the modeling of gapped motifs and motifs with palindromic patterns. All these modifications greatly improve the performance of the program. Although testing and development are still in progress, the program has shown preliminary success in finding the binding motifs for Saccharomyces cerevisiae RAP1, Bacillus subtilis RNA polymerase, and Escherichia coli CRP. We are currently working on combining BioProspector with a clustering program to explore gene expression networks and regulatory mechanisms. For a copy of the program and documentation for UNIX systems, please contact xliu@smi.stanford.edu.

A bacterial one-hybrid system for determining the DNA-binding specificity of transcription factors

“…In all cases the listed motif represents the best motif identified by MEME. The gapped motif present in the PLAG-1 consensus sequence was characterized in more detail using BioProspector 27 (http://robotics.stanford.edu/ ~xsliu/BioProspector/). Two 7 bp blocks were used in the motif analysis with an intervening gap of 0 to 4 bp allowed between these blocks.…”

“…a) Comparison of the binding site motifs produced for Zif268 by the B1H system and from a previously reported in vitro SELEX experiment 12 . b) PLAG1 can tolerate a 0 to 4 base pair gap between the two recognition motifs based on analysis of the raw sequences by Bioprospector 27 . A one base pair gap (position 8) is shown in its motif, but a 2 base pair gap was most prevalent in the isolated sequences (7 of 18).…”

A chromatin-mediated mechanism for specification of conditional transcription factor targets

“…Overrepresented sequence motifs were identified for low-glucose and static Rap1 targets using MDscan and BioProspector 37,38 . For these and previously known motifs, each Rap1 target was scanned using the MatrixScan module from BioProspector with a third-order Markov background model at a threshold of 0.0001.…”

“…A) The specificity of sequence motifs for low-glucose versus static targets was determined using ROVER 41 with a site p-value cutoff of 0.001. The value of each motif in predicting Rap1 binding was determined using the area under (AUC) the receiver operator characteristic (ROC) plot 42 , with each promoter represented by the motif score generated by the MatrixScan module of BioProspector 37 . Motif scores encapsulate the similarity of DNA sequences at each locus to the specified position weight matrix (PWM).…”

“…A motif that had no predictive value would have an AUC of about 0.5; higher values are better (maximum = 1). B) Overrepresented DNA sequence motifs for static and lowglucose Rap1 targets were determined using MDscan and BioProspector 37,38 . The motif found for the static targets is the archetypical Rap1 binding motif.…”