DoOPSearch: a web-based tool for finding and analysing common conserved motifs in the promoter regions of different chordate and plant genes

摘要

Background The comparative genomic analysis of a large number of orthologous promoter regions of the chordate and plant genes from the DoOP databases shows thousands of conserved motifs. Most of these motifs differ from any known transcription factor binding site (TFBS). To identify common conserved motifs, we need a specific tool to be able to search amongst them. Since conserved motifs from the DoOP databases are linked to genes, the result of such a search can give a list of genes that are potentially regulated by the same transcription factor(s). Results We have developed a new tool called DoOPSearch http://doopsearch.abc.hu for the analysis of the conserved motifs in the promoter regions of chordate or plant genes. We used the orthologous promoters of the DoOP database to extract thousands of conserved motifs from different taxonomic groups. The advantage of this approach is that different sets of conserved motifs might be found depending on how broad the taxonomic coverage of the underlying orthologous promoter sequence collection is (consider e.g. primates vs. mammals or Brassicaceae vs. Viridiplantae ). The DoOPSearch tool allows the users to search these motif collections or the promoter regions of DoOP with user supplied query sequences or any of the conserved motifs from the DoOP database. To find overrepresented gene ontologies, the gene lists obtained can be analysed further using a modified version of the GeneMerge program. Conclusion We present here a comparative genomics based promoter analysis tool. Our system is based on a unique collection of conserved promoter motifs characteristic of different taxonomic groups. We offer both a command line and a web-based tool for searching in these motif collections using user specified queries. These can be either short promoter sequences or consensus sequences of known transcription factor binding sites. The GeneMerge analysis of the search results allows the user to identify statistically overrepresented Gene Ontology terms that might provide a clue on the function of the motifs and genes.