ArticleBMC bioinformatics2004
Computational identification of transcription factor binding sites by functional analysis of sets of genes sharing overrepresented upstream motifs.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 46 citations in OpenAlex.
- DNA Transposons Favor De Novo Transcript Emergence Through Enrichment of Transcription Factor Binding Motifs.Genome biology and evolution · 2024Article
- SIRT1 gene expression upon genotoxic damage is regulated by APE1 through nCaRE-promoter elements.Molecular biology of the cell · 2014Article
- Motif discovery and transcription factor binding sites before and after the next-generation sequencing era.Briefings in bioinformatics · 2013Article
- Article
- TaNF-YC11, one of the light-upregulated NF-YC members in Triticum aestivum, is co-regulated with photosynthesis-related genes.Functional & integrative genomics · 2010Article
- Genome-wide survey of microRNA-transcription factor feed-forward regulatory circuits in human.Molecular bioSystems · 2009Article
- Associating transcription factor-binding site motifs with target GO terms and target genes.Nucleic acids research · 2008Article
- Identification of DNA-binding protein target sequences by physical effective energy functions: free energy analysis of lambda repressor-DNA complexes.BMC structural biology · 2007Article
- Identification of candidate regulatory sequences in mammalian 3' UTRs by statistical analysis of oligonucleotide distributions.BMC bioinformatics · 2007Article
- Machine learning for regulatory analysis and transcription factor target prediction in yeast.Systems and synthetic biology · 2007Article
- The identification of functional motifs in temporal gene expression analysis.Evolutionary bioinformatics online · 2007Article
- Correlated fragile site expression allows the identification of candidate fragile genes involved in immunity and associated with carcinogenesis.BMC bioinformatics · 2006Article
- BLISS: binding site level identification of shared signal-modules in DNA regulatory sequences.BMC bioinformatics · 2006Article
- A survey of motif discovery methods in an integrated framework.Biology direct · 2006Article
- GONOME: measuring correlations between GO terms and genomic positions.BMC bioinformatics · 2006Article
- Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states.Genome research · 2005Article
- Ab initio identification of putative human transcription factor binding sites by comparative genomics.BMC bioinformatics · 2005Article
- Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays.Nucleic acids research · 2005Article
- Comparing functional annotation analyses with Catmap.BMC bioinformatics · 2004Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranscriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance.
resultsTo exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set.
conclusionsThe method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcription factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network.
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