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KIRMES: kernel-based identification of regulatory modules in euchromatic sequences
Motivation: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-ba...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722996/ https://www.ncbi.nlm.nih.gov/pubmed/19389732 http://dx.doi.org/10.1093/bioinformatics/btp278 |
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author | Schultheiss, Sebastian J. Busch, Wolfgang Lohmann, Jan U. Kohlbacher, Oliver Rätsch, Gunnar |
author_facet | Schultheiss, Sebastian J. Busch, Wolfgang Lohmann, Jan U. Kohlbacher, Oliver Rätsch, Gunnar |
author_sort | Schultheiss, Sebastian J. |
collection | PubMed |
description | Motivation: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules. Results: We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets. Availability: The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/ Contact: sebi@tuebingen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Text |
id | pubmed-2722996 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-27229962009-08-07 KIRMES: kernel-based identification of regulatory modules in euchromatic sequences Schultheiss, Sebastian J. Busch, Wolfgang Lohmann, Jan U. Kohlbacher, Oliver Rätsch, Gunnar Bioinformatics German Conference on Bioinformatics Motivation: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules. Results: We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets. Availability: The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/ Contact: sebi@tuebingen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2009-08-15 2009-04-23 /pmc/articles/PMC2722996/ /pubmed/19389732 http://dx.doi.org/10.1093/bioinformatics/btp278 Text en http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | German Conference on Bioinformatics Schultheiss, Sebastian J. Busch, Wolfgang Lohmann, Jan U. Kohlbacher, Oliver Rätsch, Gunnar KIRMES: kernel-based identification of regulatory modules in euchromatic sequences |
title | KIRMES: kernel-based identification of regulatory modules in euchromatic sequences |
title_full | KIRMES: kernel-based identification of regulatory modules in euchromatic sequences |
title_fullStr | KIRMES: kernel-based identification of regulatory modules in euchromatic sequences |
title_full_unstemmed | KIRMES: kernel-based identification of regulatory modules in euchromatic sequences |
title_short | KIRMES: kernel-based identification of regulatory modules in euchromatic sequences |
title_sort | kirmes: kernel-based identification of regulatory modules in euchromatic sequences |
topic | German Conference on Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722996/ https://www.ncbi.nlm.nih.gov/pubmed/19389732 http://dx.doi.org/10.1093/bioinformatics/btp278 |
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