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A common approach is to search for matches to known transcription factor binding sites in the TRANSFAC database (36). Alternatively, if several C. elegans genes are probably regulated by the same transcription factor, one can search for short motifs that are overrepresented in CNSs in the upstream regions of the C. elegans genes and their C. briggsae orthologs (34,35,37). This approach has the advantage that it can identify novel binding sites that are absent from TRANSFAC. For example, Gaudet et al.

Lines 26 and 27 are the end of the loop, with a 3-s delay before the next iteration. org/ and that they include a 3-s delay between requests. html. 4. Programmatic Access to AceDB Using AcePerl Although screen-scraping approaches are fast and easy to implement, they are limited to data that is visible on the web interface. The AcePerl Perl module (19) circumvents these limitations by providing direct access to the AceDB database at WormBase. AcePerl is particularly useful for data-mining tasks seeking textbased annotations or those that need to cross several different database classes.

2). Many other programs exist for identifying potential transcription factor binding sites in CNSs. Tompa et al. (39) recently assessed the accuracy of 13 motif-discovery programs in identifying known transcription factor binding sites. One of the most successful programs was Weeder (40), which counts how often all possible motifs up to a certain length occur in the input sequences, and identifies overrepresented motifs that are well conserved. GuhaThakurta et al. (37) tested the hypothesis that most C.

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