A Comprehensive Analysis Workflow for Genome-Wide Screening Data from ChIP-Sequencing Experiments

被引:0
|
作者
Ozer, Hatice Gulcin [1 ,2 ]
Bozdag, Doruk [1 ,3 ]
Camerlengo, Terry [1 ]
Wu, Jiejun [4 ]
Huang, Yi-Wen [4 ]
Hartley, Tim [1 ]
Parvin, Jeffrey D. [1 ,2 ]
Huang, Tim [4 ]
Catalyurek, Umit V. [1 ]
Huang, Kun [1 ,2 ]
机构
[1] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[2] Ohio State Univ, Ctr Comprehens Canc, Biomed Informat Shared Resource, Columbus, OH 43210 USA
[3] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA
[4] Ohio State Univ, Dept Mol Virol, Columbus, OH 43210 USA
关键词
ChIP-seq; workflow; short sequence mapping; parallelization; normalization; visualization; OLIGONUCLEOTIDES;
D O I
暂无
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
ChIP-sequencing is a new technique for generating short DNA sequences useful in analyzing DNA-protein interactions and carrying out genome-wide studies. Although there are some studies to process and analyze ChIP-sequencing data, a complete workflow has not been reported yet. The size of the data and broad range of biological questions are the main challenges to establish a data analysis workflow for ChIP-sequencing data. In this paper, we present the ChIP-sequencing data analysis workflow that we developed at the Ohio State University Comprehensive Cancer Center Bioinformatics Shared Resources. This pipeline utilizes 1) use of different mapping algorithms such as Eland, MapReads, SeqMap, RMAP to align short sequence reads to the reference genome 2) a novel normalization algorithm to detect significant binding densities and to compare binding densities of different experiments 3) gene database mapping and 3D binding density visualization 4) distributed computing and high performance computing (HPC) support.
引用
收藏
页码:320 / +
页数:2
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