Predicting Gene Regulatory Elements in Silico on a Genomic Scale

Alvis Brazma, Inge Jonassen, Jaak Vilo, and Esko Ukkonen
Predicting Gene Regulatory Elements in Silico on a Genomic Scale.
Genome Research Vol. 8, Issue 11 (pp. 1202-1215) November 1998. Cold Spring Harbor Laboratory Press . (local pdf)


We performed a systematic analysis of gene upstream regions in the yeast genome for occurrences of regular expression-type patterns with the goal of identifying potential regulatory elements. To achieve this goal, we have developed a new sequence pattern discovery algorithm that searches exhaustively for a priori unknown regular expression-type patterns that are over-represented in a given set of sequences. We applied the algorithm in two cases, (1) discovery of patterns in the complete set of >6000 sequences taken upstream of the putative yeast genes and (2) discovery of patterns in the regions upstream of the genes with similar expression profiles. In the first case, we looked for patterns that occur more frequently in the gene upstream regions than in the genome overall. In the second case, first we clustered the upstream regions of all the genes by similarity of their expression profiles on the basis of publicly available gene expression data and then looked for sequence patterns that are over-represented in each cluster. In both cases we considered each pattern that occurred at least in some minimum number of sequences, and rated them on the basis of their over-representation. Among the highest rating patterns, most have matches to substrings in known yeast transcription factor-binding sites. Moreover, several of them are known to be relevant to the expression of the genes from the respective clusters. Experiments on simulated data show that the majority of the discovered patterns are not expected to occur by chance.

Look at the data and patterns

Pattern discovery methods

Yeast transcription site search


is a tool for finding and analysis of combinations for transcription factor binding sites in yeast (S.Cerevisiae) genome and gene upstream regions in particular. The tool is aimed at helping to identify potential promoter classes via applying data mining techniques.
Postscript version

The yeast genome and gene upstream regions are taken as described in MIPS database. Yeast transcription factor binding site descriptions are taken from IMD database. A combination of binding sites is characterized by the following parameters:

  1. coverage: the number of its occurrences in upstream regions;
  2. goodness: the ratio of the number of its occurrences in the upstream regions vs. the number of occurrences in random regions (of the same length and number); and
  3. unexpectedness: the ratio of its occurrences vs. the expected number of occurrences based on the individual sites.
The combinations with high values for all these parameters can possibly define promoter classes. The sufficient value of parameter (1) ensures that the combination is present in at least the given number of upstream regions, parameter (2) ensures that the rate of the occurrences of the combination in upstream regions are not just a consequence of high rate of occurrences in the genome as the whole, and parameter (3) that the rate of the occurrences of the combination is not only a consequence of the high rate of individual participating binding sites.

The TFCD (Transcription Factor Combination Discoverer) is running on a Linux-based workstation. Please follow the link to the main interface of the program

Since the computer where TFCD is running is in personal use, it might be that from time to time the site is not up. There might be reasons to use Windows on the same computer etc. If the site is down, you should contact Jaak Vilo to make sure that computer is up and running the WWW software properly.

Version 2 and additional links to used data

Jaak Vilo, Jaak.Vilo@cs.Helsinki.FI
Alvis Brazma, Alvis.Brazma@cs.Helsinki.FI