Biomedical subjects

H J Bussemaker

Publications and source records attributed to H J Bussemaker.

3 recordsLinked to original sources

PubMed2001

Regulatory element detection using correlation with expression.

We present here a new computational method for discovering cis-regulatory elements that circumvents the need to cluster genes based on their expression profiles. Based on a model in which upstream motifs contribute additively to the log-expression level of a gene, this method requires a single genome-wide set of expression ratios and the upstream sequence for each gene, and outputs statistically significant motifs. Analysis of publicly available expression data for Saccharomyces cerevisiae reveals several new putative regulatory elements, some of which plausibly control the early, transient induction of genes during sporulation. Known motifs generally have high statistical significance.

Algorithms↗

PubMed2000

Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis.

The availability of complete genome sequences and mRNA expression data for all genes creates new opportunities and challenges for identifying DNA sequence motifs that control gene expression. An algorithm, "MobyDick," is presented that decomposes a set of DNA sequences into the most probable dictionary of motifs or words. This method is applicable to any set of DNA sequences: for example, all upstream regions in a genome or all genes expressed under certain conditions. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter ones of various lengths, eliminating the need for a separate set of reference data to define probabilities. We have built a dictionary with 1,200 words for the 6, 000 upstream regulatory regions in the yeast genome; the 500 most significant words (some with as few as 10 copies in all of the upstream regions) match 114 of 443 experimentally determined sites (a significance level of 18 standard deviations). When analyzing all of the genes up-regulated during sporulation as a group, we find many motifs in addition to the few previously identified by analyzing the subclusters individually to the expression subclusters. Applying MobyDick to the genes derepressed when the general repressor Tup1 is deleted, we find known as well as putative binding sites for its regulatory partners.

Algorithms↗

PubMed2000

Regulatory element detection using a probabilistic segmentation model.

The availability of genome-wide mRNA expression data for organisms whose genome is fully sequenced provides a unique data set from which to decipher how transcription is regulated by the upstream control region of a gene. A new algorithm is presented which decomposes DNA sequence into the most probable "dictionary" of motifs or words. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter words of various length. This eliminates the need for a separate set of reference data to define probabilities, and genome-wide applications are therefore possible. For the 6,000 upstream regulatory regions in the yeast genome, the 500 strongest motifs from a dictionary of size 1,200 match at a significance level of 15 standard deviations to a database of cis-regulatory elements. Analysis of sets of genes such as those up-regulated during sporulation reveals many new putative regulatory sites in addition to identifying previously known sites.

Algorithms↗