Computation for ChIP-seq and RNA-seq studies.

Computation for ChIP-seq and RNA-seq studies.
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DOI:
10.1038/nmeth.1371
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发表时间:
2009-11
期刊:
影响因子:
48
通讯作者:
Mortazavi, Ali
Mortazavi, Ali
中科院分区:
生物学1区
文献类型:
--
作者:
Pepke, Shirley;Wold, Barbara;Mortazavi, Ali

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蛋白质-DNA相互作用和转录组的全基因组测量值越来越多地通过深DNA测序方法(CHIP-SEQ和RNA-SEQ)来完成。这些基于计数的测量值的力量和丰富性是以常规处理数百万读物的代价为代价。虽然早期顾问必然开发了自己的自定义计算机代码来分析第一个CHIP-seq和RNA-Seq数据集,但新一代的更复杂的算法和软件工具正在出现,以协助这些项目的分析阶段。这篇评论描述了芯片序列和RNA-seq数据集的多层分析,讨论了当前可用于在每一层执行任务的软件包,并描述了未来分析工具的一些即将到来的挑战和功能。我们还讨论了软件选择和用途如何受到基础生物学和数据结构的特定方面的影响,包括基因组大小,转录因子结合位点的位置聚类,转录本发现和表达定量。
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. While 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. This review describes the multilayered analyses of ChIP-seq and RNA-seq datasets, discusses the software packages currently available to perform tasks at each layer, and describes 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.
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