Computation for ChIP-seq and RNA-seq studies Export

@article{citeulike:6043685, abstract = {Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.}, author = {Pepke, Shirley and Wold, Barbara and Mortazavi, Ali}, booktitle = {Nat Meth}, citeulike-article-id = {6043685}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nmeth.1371}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nmeth.1371}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19844228}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19844228}, day = {15}, doi = {10.1038/nmeth.1371}, issn = {1548-7091}, journal = {Nature Methods}, keywords = {chip-seq}, month = {October}, number = {11s}, pages = {S22--S32}, posted-at = {2009-11-04 17:44:07}, priority = {2}, publisher = {Nature Publishing Group}, title = {Computation for ChIP-seq and RNA-seq studies}, url = {http://dx.doi.org/10.1038/nmeth.1371}, volume = {6}, year = {2009} }

TY - JOUR ID - citeulike:6043685 L3 - citeulike-article-id:6043685 N2 - Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification. IS - 11s JF - Nature Methods SN - 1548-7091 EP - S32 TI - Computation for ChIP-seq and RNA-seq studies PB - Nature Publishing Group VL - 6 T2 - Nat Meth SP - S22 KW - chip-seq AU - Pepke, Shirley AU - Wold, Barbara AU - Mortazavi, Ali PY - 2009/10/15/ UR - http://dx.doi.org/10.1038/nmeth.1371 ER -