RACS：基于重叠群的基因组的ChIP-Seq数据的快速分析
RACS: Rapid Analysis of ChIP-Seq data for contig based genomes
摘要：
背景资料：染色质免疫沉淀结合下一代测序（ChIP-Seq）是一种广泛使用的技术，用于以全基因组方式研究染色质相关蛋白的功能。ChIP-Seq生成大量数据，这些数据可能难以处理和分析，特别是对于具有基于重叠群的基因组的生物体。基于重叠群的基因组通常对顺式元件（例如增强子）具有较差的注释，所述顺式元件对于基因表达是重要的。注释不佳的基因组使得ChIP-Seq数据的综合分析变得困难，因此缺乏标准化的分析管道。研究方法：我们报告了一个计算管道，它利用传统的高性能计算技术和开源工具来处理和分析从ChIP-Seq获得的数据。我们将我们的计算管道“ChIP-Seq数据快速分析”（RACS）应用于在模式生物嗜热四膜虫中生成的ChIP-Seq数据，这是具有重叠群中可用的基因组的生物体的一个例子。结果如下：为了测试RAC的性能和效率，我们进行了对照ChIP-Seq实验，使我们能够在分析之前发表的数据集时快速消除假阳性。我们的流水线分离基因和基因间区域之间发现的读取积累，并且对于快速下游分析是高效的。结论：总而言之，本报告中提出的计算管道是一种高效且高度可靠的工具，可用于分析在具有基于重叠群的基因组的模式生物中生成的全基因组ChIP-Seq数据。RACS是一个开源计算管道，可从以下网站下载：https://bitbucket.org/mjponce/racs--或--https://gitrepos.scinet.utoronto.ca/public/? a=摘要&p=RACS
英文摘要：
Background: Chromatin immunoprecipitation coupled to next generation sequencing (ChIP-Seq) is a widely used technique to investigate the function of chromatin-related proteins in a genome-wide manner. ChIP-Seq generates large quantities of data which can be difficult to process and analyse, particularly for organisms with contig based genomes. Contig-based genomes often have poor annotations for cis-elements, for example enhancers, that are important for gene expression. Poorly annotated genomes make a comprehensive analysis of ChIP-Seq data difficult and as such standardized analysis pipelines are lacking. Methods: We report a computational pipeline that utilizes traditional High-Performance Computing techniques and open source tools for processing and analysing data obtained from ChIP-Seq. We applied our computational pipeline "Rapid Analysis of ChIP-Seq data" (RACS) to ChIP-Seq data that was generated in the model organism Tetrahymena thermophila, an example of an organism with a genome that is available in contigs. Results: To test the performance and efficiency of RACs, we performed control ChIP-Seq experiments allowing us to rapidly eliminate false positives when analyzing our previously published data set. Our pipeline segregates the found read accumulations between genic and intergenic regions and is highly efficient for rapid downstream analyses. Conclusions: Altogether, the computational pipeline presented in this report is an efficient and highly reliable tool to analyze genome-wide ChIP-Seq data generated in model organisms with contig-based genomes. RACS is an open source computational pipeline available to download from: https://bitbucket.org/mjponce/racs --or-- https://gitrepos.scinet.utoronto.ca/public/?a=summary&p=RACS
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