[Paper Review] Ab initio identification of putative human transcription factor binding sites by comparative genomics

Davide Corà, Carl Herrmann+4 more|ArXiv.org|May 3, 2005

Genomics and Chromatin Dynamics33 references6 citations

TL;DR

This study presents a comparative genomics approach to ab initio identify human transcription factor binding sites by scanning conserved upstream regions across human and mouse genomes. By detecting overrepresented 5–8 bp motifs in co-conserved gene sets and filtering for Gene Ontology enrichment and coexpression from microarray data, the method identifies known and novel candidate regulatory motifs, demonstrating its power to prioritize functionally relevant cis-regulatory elements without prior knowledge of transcription factors.

ABSTRACT

We discuss a simple and powerful approach for the ab initio identification of cis-regulatory motifs involved in transcriptional regulation. 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.

Motivation & Objective

• To develop a method for ab initio discovery of human transcription factor binding sites without prior knowledge of the regulators.
• To leverage evolutionary conservation between human and mouse genomes to prioritize functionally relevant cis-regulatory motifs.
• To identify sets of co-regulated genes through shared upstream motifs and validate their biological coherence using functional annotation and expression data.
• To distinguish true regulatory motifs from random sequence patterns by integrating multiple lines of biological evidence.

Proposed method

• Perform whole-genome comparison of human and mouse upstream sequences using the CORG database of conserved upstream regions.
• Scan all possible 5–8 bp DNA motifs in the conserved upstream regions for overrepresentation.
• Group genes sharing the same overrepresented motif in both species into putative regulatory sets.
• Filter motif sets using Gene Ontology (GO) enrichment analysis to detect statistically significant shared biological functions.
• Apply microarray-based coexpression analysis to detect correlated expression patterns across the gene sets.
• Prioritize motifs that pass at least one filter (GO enrichment or coexpression) as high-confidence candidate transcription factor binding sites.

Experimental results

Research questions

• RQ1 Which short DNA motifs in human upstream regions are evolutionarily conserved between human and mouse and show overrepresentation in specific gene sets?
• RQ2 Do genes sharing the same conserved upstream motif exhibit significant functional similarity as annotated by Gene Ontology?
• RQ3 Is there evidence of coexpression among genes sharing the same upstream motif across microarray experiments?
• RQ4 Can combined evidence from GO annotation and expression profiles improve the identification of biologically relevant transcription factor binding sites?
• RQ5 What novel or previously uncharacterized motifs emerge as high-confidence candidates for transcription factor binding?

Key findings

• The method successfully identifies several known transcription factor binding motifs, validating its biological relevance.
• A significant number of motif sets show statistically significant enrichment for shared Gene Ontology terms, indicating functional coherence among the associated genes.
• Many motif sets exhibit coexpression patterns in microarray data, supporting their role in coregulated gene regulation.
• The integration of conservation, overrepresentation, and functional evidence significantly improves the specificity of candidate motif selection over sequence conservation alone.
• The approach identifies a set of novel candidate motifs not previously linked to known transcription factors, suggesting new regulatory elements for functional validation.