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Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data

BACKGROUND: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for wh...

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Detalles Bibliográficos
Autores principales: Zacher, Benedikt, Kuan, Pei Fen, Tresch, Achim
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2868012/
https://www.ncbi.nlm.nih.gov/pubmed/20398407
http://dx.doi.org/10.1186/1471-2105-11-194
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author Zacher, Benedikt
Kuan, Pei Fen
Tresch, Achim
author_facet Zacher, Benedikt
Kuan, Pei Fen
Tresch, Achim
author_sort Zacher, Benedikt
collection PubMed
description BACKGROUND: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression. RESULTS: We present a free, open-source R/Bioconductor package Starr that facilitates comparative analysis of ChIP-chip data across experiments and across different microarray platforms. The package provides functions for data import, quality assessment, data visualization and exploration. Starr includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, Starr automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome. CONCLUSION: Starr is an R package that covers the complete ChIP-chip workflow from data processing to binding pattern detection. It focuses on the high-level data analysis, e.g., it provides methods for the integration and combined statistical analysis of binding profiles and complementary functional genomics data. Starr enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features.
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spelling pubmed-28680122010-05-12 Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data Zacher, Benedikt Kuan, Pei Fen Tresch, Achim BMC Bioinformatics Software BACKGROUND: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression. RESULTS: We present a free, open-source R/Bioconductor package Starr that facilitates comparative analysis of ChIP-chip data across experiments and across different microarray platforms. The package provides functions for data import, quality assessment, data visualization and exploration. Starr includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, Starr automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome. CONCLUSION: Starr is an R package that covers the complete ChIP-chip workflow from data processing to binding pattern detection. It focuses on the high-level data analysis, e.g., it provides methods for the integration and combined statistical analysis of binding profiles and complementary functional genomics data. Starr enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features. BioMed Central 2010-04-17 /pmc/articles/PMC2868012/ /pubmed/20398407 http://dx.doi.org/10.1186/1471-2105-11-194 Text en Copyright ©2010 Zacher et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Zacher, Benedikt
Kuan, Pei Fen
Tresch, Achim
Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
title Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
title_full Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
title_fullStr Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
title_full_unstemmed Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
title_short Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data
title_sort starr: simple tiling array analysis of affymetrix chip-chip data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2868012/
https://www.ncbi.nlm.nih.gov/pubmed/20398407
http://dx.doi.org/10.1186/1471-2105-11-194
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