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iMotifs: an integrated sequence motif visualization and analysis environment
Motivation: Short sequence motifs are an important class of models in molecular biology, used most commonly for describing transcription factor binding site specificity patterns. High-throughput methods have been recently developed for detecting regulatory factor binding sites in vivo and in vitro a...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2832821/ https://www.ncbi.nlm.nih.gov/pubmed/20106815 http://dx.doi.org/10.1093/bioinformatics/btq026 |
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author | Piipari, Matias Down, Thomas A. Saini, Harpreet Enright, Anton Hubbard, Tim J.P. |
author_facet | Piipari, Matias Down, Thomas A. Saini, Harpreet Enright, Anton Hubbard, Tim J.P. |
author_sort | Piipari, Matias |
collection | PubMed |
description | Motivation: Short sequence motifs are an important class of models in molecular biology, used most commonly for describing transcription factor binding site specificity patterns. High-throughput methods have been recently developed for detecting regulatory factor binding sites in vivo and in vitro and consequently high-quality binding site motif data are becoming available for increasing number of organisms and regulatory factors. Development of intuitive tools for the study of sequence motifs is therefore important. iMotifs is a graphical motif analysis environment that allows visualization of annotated sequence motifs and scored motif hits in sequences. It also offers motif inference with the sensitive NestedMICA algorithm, as well as overrepresentation and pairwise motif matching capabilities. All of the analysis functionality is provided without the need to convert between file formats or learn different command line interfaces. The application includes a bundled and graphically integrated version of the NestedMICA motif inference suite that has no outside dependencies. Problems associated with local deployment of software are therefore avoided. Availability: iMotifs is licensed with the GNU Lesser General Public License v2.0 (LGPL 2.0). The software and its source is available at http://wiki.github.com/mz2/imotifs and can be run on Mac OS X Leopard (Intel/PowerPC). We also provide a cross-platform (Linux, OS X, Windows) LGPL 2.0 licensed library libxms for the Perl, Ruby, R and Objective-C programming languages for input and output of XMS formatted annotated sequence motif set files. Contact: matias.piipari@gmail.com; imotifs@googlegroups.com |
format | Text |
id | pubmed-2832821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28328212010-03-08 iMotifs: an integrated sequence motif visualization and analysis environment Piipari, Matias Down, Thomas A. Saini, Harpreet Enright, Anton Hubbard, Tim J.P. Bioinformatics Applications Note Motivation: Short sequence motifs are an important class of models in molecular biology, used most commonly for describing transcription factor binding site specificity patterns. High-throughput methods have been recently developed for detecting regulatory factor binding sites in vivo and in vitro and consequently high-quality binding site motif data are becoming available for increasing number of organisms and regulatory factors. Development of intuitive tools for the study of sequence motifs is therefore important. iMotifs is a graphical motif analysis environment that allows visualization of annotated sequence motifs and scored motif hits in sequences. It also offers motif inference with the sensitive NestedMICA algorithm, as well as overrepresentation and pairwise motif matching capabilities. All of the analysis functionality is provided without the need to convert between file formats or learn different command line interfaces. The application includes a bundled and graphically integrated version of the NestedMICA motif inference suite that has no outside dependencies. Problems associated with local deployment of software are therefore avoided. Availability: iMotifs is licensed with the GNU Lesser General Public License v2.0 (LGPL 2.0). The software and its source is available at http://wiki.github.com/mz2/imotifs and can be run on Mac OS X Leopard (Intel/PowerPC). We also provide a cross-platform (Linux, OS X, Windows) LGPL 2.0 licensed library libxms for the Perl, Ruby, R and Objective-C programming languages for input and output of XMS formatted annotated sequence motif set files. Contact: matias.piipari@gmail.com; imotifs@googlegroups.com Oxford University Press 2010-03-15 2010-01-26 /pmc/articles/PMC2832821/ /pubmed/20106815 http://dx.doi.org/10.1093/bioinformatics/btq026 Text en © The Author(s) 2010. Published by Oxford University Press. 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/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Piipari, Matias Down, Thomas A. Saini, Harpreet Enright, Anton Hubbard, Tim J.P. iMotifs: an integrated sequence motif visualization and analysis environment |
title | iMotifs: an integrated sequence motif visualization and analysis environment |
title_full | iMotifs: an integrated sequence motif visualization and analysis environment |
title_fullStr | iMotifs: an integrated sequence motif visualization and analysis environment |
title_full_unstemmed | iMotifs: an integrated sequence motif visualization and analysis environment |
title_short | iMotifs: an integrated sequence motif visualization and analysis environment |
title_sort | imotifs: an integrated sequence motif visualization and analysis environment |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2832821/ https://www.ncbi.nlm.nih.gov/pubmed/20106815 http://dx.doi.org/10.1093/bioinformatics/btq026 |
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