Ab initio identification of putative human transcription factor binding sites by comparative genomics
1 Dipartimento di Fisica Teorica dell'Università degli Studi di Torino and INFN, Via P. Giuria 1 – I 10125 Torino, Italy
2 LGPD-IBDM, Université de la Méditerranée / CNRS, Campus de Luminy Case 907 – F-13288 Marseille Cedex 9, France
3 Max-Planck-Institute for Molecular Genetics, Ihnestrasse 73 – D-14195 Berlin, Germany
4 Dipartimento di Genetica, Biologia e Biochimica dell'Università di Torino, Via Santena 5 bis – I-10126 Torino, Italy
BMC Bioinformatics 2005, 6:110 doi:10.1186/1471-2105-6-110Published: 2 May 2005
Understanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors, which typically bind to specific, short DNA sequence motifs usually located in the upstream region of the regulated genes. We discuss here a simple and powerful approach for the ab initio identification of these cis-regulatory motifs. The method we present integrates several elements: human-mouse comparison, statistical analysis of genomic sequences and the concept of coregulation. We apply it to a complete scan of the human genome.
By using the catalogue of conserved upstream sequences collected in the CORG database we construct sets of genes sharing the same overrepresented motif (short DNA sequence) in their upstream regions both in human and in mouse. We perform this construction for all possible motifs from 5 to 8 nucleotides in length and then filter the resulting sets looking for two types of evidence of coregulation: first, we analyze the Gene Ontology annotation of the genes in the set, searching for statistically significant common annotations; second, we analyze the expression profiles of the genes in the set as measured by microarray experiments, searching for evidence of coexpression. The sets which pass one or both filters are conjectured to contain a significant fraction of coregulated genes, and the upstream motifs characterizing the sets are thus good candidates to be the binding sites of the TF's involved in such regulation.
In this way we find various known motifs and also some new candidate binding sites.
We have discussed a new integrated algorithm for the "ab initio" identification of transcription factor binding sites in the human genome. The method is based on three ingredients: comparative genomics, overrepresentation, different types of coregulation. The method is applied to a full-scan of the human genome, giving satisfactory results.