Ringo - an R/Bioconductor package for analyzing ChIP-chip readouts

@article{citeulike:1416237, abstract = {Background Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a high-throughput assay for DNA-protein-binding or post-translational chromatin/histone modifications. However, the raw microarray intensity readings themselves are not immediately useful to researchers, but require a number of bioinformatic analysis steps. Identified enriched regions need to be bioinformatically annotated and compared to related datasets by statistical methods. Results We present a free, open-source R package Ringo that facilitates the analysis of ChIP-chip experiments by providing functionality for data import, quality assessment, normalization and visualization of the data, and the detection of ChIP-enriched genomic regions. Conclusion Ringo integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It facilitates the construction of programmed analysis workflows, offers benefits in scalability, reproducibility and methodical scope of the analyses and opens up a broad selection of follow-up statistical and bioinformatic methods.}, author = {Toedling, Joern and Sklyar, Oleg and Huber, Wolfgang }, citeulike-article-id = {1416237}, comment = {Furthermore, several peak finding toolkits have been developed to facilitate routine processing of ChIP-on-chip datasets. TileMap is an easy-to-use peak finder for Affymetrix tiling array data, which has been applied in a number of independent studies (Ji and Wong, 2005); Ringo is a Bioconductor package for the analysis of ChIP-on-chip data from the widely used NimbleGen platform (Toedling et al., 2007); ChIPOTle is a basic peak finding macro for Excel, which does not take platform-specific information into account (Buck et al., 2005); and Tilescope is a fully integrated analysis pipeline that is applicable to data from both the Affymetrix and the NimbleGen platform (Zhang et al., 2007). In spite of the abundance of algorithms published recently, the peak finding problem for ChIP-on-chip data cannot be regarded as solved. In particular, current peak finders have problems with histone modifications that cover extended genomic regions and they seem to miss a substantial number of weak binding sites. In order to select a biologically meaningful cutoff that distinguishes between significant peaks and random fluctuations, experimental validation of a moderate number of detected peaks continues to be crucial. To guide this process, a framework has been proposed that can help identify most informative regions for validation (Du et al., 2006). - Block et al 2007}, doi = {10.1186/1471-2105-8-221}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {software}, month = {June}, pages = {221+}, posted-at = {2008-01-18 21:26:16}, priority = {2}, title = {Ringo - an R/Bioconductor package for analyzing ChIP-chip readouts}, url = {http://dx.doi.org/10.1186/1471-2105-8-221}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1416237 L3 - citeulike-article-id:1416237 TI - Ringo - an R/Bioconductor package for analyzing ChIP-chip readouts JF - BMC Bioinformatics VL - 8 SP - 221 SN - 1471-2105 N2 - Background Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a high-throughput assay for DNA-protein-binding or post-translational chromatin/histone modifications. However, the raw microarray intensity readings themselves are not immediately useful to researchers, but require a number of bioinformatic analysis steps. Identified enriched regions need to be bioinformatically annotated and compared to related datasets by statistical methods. Results We present a free, open-source R package Ringo that facilitates the analysis of ChIP-chip experiments by providing functionality for data import, quality assessment, normalization and visualization of the data, and the detection of ChIP-enriched genomic regions. Conclusion Ringo integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It facilitates the construction of programmed analysis workflows, offers benefits in scalability, reproducibility and methodical scope of the analyses and opens up a broad selection of follow-up statistical and bioinformatic methods. KW - software N1 - Furthermore, several peak finding toolkits have been developed to facilitate routine processing of ChIP-on-chip datasets. TileMap is an easy-to-use peak finder for Affymetrix tiling array data, which has been applied in a number of independent studies (Ji and Wong, 2005); Ringo is a Bioconductor package for the analysis of ChIP-on-chip data from the widely used NimbleGen platform (Toedling et al., 2007); ChIPOTle is a basic peak finding macro for Excel, which does not take platform-specific information into account (Buck et al., 2005); and Tilescope is a fully integrated analysis pipeline that is applicable to data from both the Affymetrix and the NimbleGen platform (Zhang et al., 2007). In spite of the abundance of algorithms published recently, the peak finding problem for ChIP-on-chip data cannot be regarded as solved. In particular, current peak finders have problems with histone modifications that cover extended genomic regions and they seem to miss a substantial number of weak binding sites. In order to select a biologically meaningful cutoff that distinguishes between significant peaks and random fluctuations, experimental validation of a moderate number of detected peaks continues to be crucial. To guide this process, a framework has been proposed that can help identify most informative regions for validation (Du et al., 2006). - Block et al 2007 AU - Toedling, Joern AU - Sklyar, Oleg AU - Huber, Wolfgang PY - 2007/06/26/ UR - http://dx.doi.org/10.1186/1471-2105-8-221 ER -