Integrating genomic data to predict transcription factor binding.
Identifieur interne : 002292 ( PubMed/Curation ); précédent : 002291; suivant : 002293Integrating genomic data to predict transcription factor binding.
Auteurs : Dustin T. Holloway [États-Unis] ; Mark Kon ; Charles DelisiSource :
- Genome informatics. International Conference on Genome Informatics [ 0919-9454 ] ; 2005.
Descripteurs français
- KwdFr :
- Algorithmes, Analyse de profil d'expression de gènes, Analyse de regroupements, Biologie informatique, Facteurs de transcription (génétique), Facteurs de transcription (métabolisme), Gènes fongiques, Génome fongique, Immunoprécipitation de la chromatine, Liaison aux protéines, Phylogénie, Régions promotrices (génétique), Régulation de l'expression des gènes fongiques, Saccharomyces cerevisiae (génétique), Sites de fixation, Séquence nucléotidique, Théorème de Bayes, Évolution moléculaire.
- MESH :
- génétique : Facteurs de transcription, Saccharomyces cerevisiae.
- métabolisme : Facteurs de transcription.
- Algorithmes, Analyse de profil d'expression de gènes, Analyse de regroupements, Biologie informatique, Gènes fongiques, Génome fongique, Immunoprécipitation de la chromatine, Liaison aux protéines, Phylogénie, Régions promotrices (génétique), Régulation de l'expression des gènes fongiques, Sites de fixation, Séquence nucléotidique, Théorème de Bayes, Évolution moléculaire.
English descriptors
- KwdEn :
- Algorithms, Base Sequence, Bayes Theorem, Binding Sites, Chromatin Immunoprecipitation, Cluster Analysis, Computational Biology, Evolution, Molecular, Gene Expression Profiling, Gene Expression Regulation, Fungal, Genes, Fungal, Genome, Fungal, Phylogeny, Promoter Regions, Genetic, Protein Binding, Saccharomyces cerevisiae (genetics), Transcription Factors (genetics), Transcription Factors (metabolism).
- MESH :
- chemical , genetics : Transcription Factors.
- genetics : Saccharomyces cerevisiae.
- chemical , metabolism : Transcription Factors.
- Algorithms, Base Sequence, Bayes Theorem, Binding Sites, Chromatin Immunoprecipitation, Cluster Analysis, Computational Biology, Evolution, Molecular, Gene Expression Profiling, Gene Expression Regulation, Fungal, Genes, Fungal, Genome, Fungal, Phylogeny, Promoter Regions, Genetic, Protein Binding.
Abstract
Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor's binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes. Binding site clusters are statistically significant clusters of TF binding sites detected by the algorithm ClusterBuster. Gene expression takes into account the idea that when the gene expression profiles of a transcription factor and a potential target gene are correlated, then it is more likely that the gene is a genuine target. Also, genes with highly correlated expression profiles are often regulated by the same TF(s). The GO annotation data takes advantage of the idea that common transcription targets often have related function. Finally, the distribution of the counts of all k-mers of length 4, 5, and 6 in gene's promoter region were examined as means to predict TF binding. In each case the data are compared to known true positives taken from ChIP-chip data, Transfac, and the Saccharomyces Genome Database. First, degeneracy, conservation, expression, and binding site clusters were examined independently and in combination via Bayesian allocation. Then, binding sites were predicted with a support vector machine (SVM) using all methods alone and in combination. The SVM works best when all genomic data are combined, but can also identify which methods contribute the most to accurate classification. On average, a support vector machine can classify binding sites with high sensitivity and an accuracy of almost 80%.
PubMed: 16362910
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<term>Promoter Regions, Genetic</term>
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<term>Biologie informatique</term>
<term>Facteurs de transcription (génétique)</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Gènes fongiques</term>
<term>Génome fongique</term>
<term>Immunoprécipitation de la chromatine</term>
<term>Liaison aux protéines</term>
<term>Phylogénie</term>
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<term>Base Sequence</term>
<term>Bayes Theorem</term>
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<term>Biologie informatique</term>
<term>Gènes fongiques</term>
<term>Génome fongique</term>
<term>Immunoprécipitation de la chromatine</term>
<term>Liaison aux protéines</term>
<term>Phylogénie</term>
<term>Régions promotrices (génétique)</term>
<term>Régulation de l'expression des gènes fongiques</term>
<term>Sites de fixation</term>
<term>Séquence nucléotidique</term>
<term>Théorème de Bayes</term>
<term>Évolution moléculaire</term>
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<front><div type="abstract" xml:lang="en">Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor's binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes. Binding site clusters are statistically significant clusters of TF binding sites detected by the algorithm ClusterBuster. Gene expression takes into account the idea that when the gene expression profiles of a transcription factor and a potential target gene are correlated, then it is more likely that the gene is a genuine target. Also, genes with highly correlated expression profiles are often regulated by the same TF(s). The GO annotation data takes advantage of the idea that common transcription targets often have related function. Finally, the distribution of the counts of all k-mers of length 4, 5, and 6 in gene's promoter region were examined as means to predict TF binding. In each case the data are compared to known true positives taken from ChIP-chip data, Transfac, and the Saccharomyces Genome Database. First, degeneracy, conservation, expression, and binding site clusters were examined independently and in combination via Bayesian allocation. Then, binding sites were predicted with a support vector machine (SVM) using all methods alone and in combination. The SVM works best when all genomic data are combined, but can also identify which methods contribute the most to accurate classification. On average, a support vector machine can classify binding sites with high sensitivity and an accuracy of almost 80%.</div>
</front>
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<Abstract><AbstractText>Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor's binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes. Binding site clusters are statistically significant clusters of TF binding sites detected by the algorithm ClusterBuster. Gene expression takes into account the idea that when the gene expression profiles of a transcription factor and a potential target gene are correlated, then it is more likely that the gene is a genuine target. Also, genes with highly correlated expression profiles are often regulated by the same TF(s). The GO annotation data takes advantage of the idea that common transcription targets often have related function. Finally, the distribution of the counts of all k-mers of length 4, 5, and 6 in gene's promoter region were examined as means to predict TF binding. In each case the data are compared to known true positives taken from ChIP-chip data, Transfac, and the Saccharomyces Genome Database. First, degeneracy, conservation, expression, and binding site clusters were examined independently and in combination via Bayesian allocation. Then, binding sites were predicted with a support vector machine (SVM) using all methods alone and in combination. The SVM works best when all genomic data are combined, but can also identify which methods contribute the most to accurate classification. On average, a support vector machine can classify binding sites with high sensitivity and an accuracy of almost 80%.</AbstractText>
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