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	Generate gene expression profile from high-throughput sequencing data
	Liu, Hui ; Jiang, Zhichao ; Fang, Xiangzhong ; Fu, Hanjiang ; Zheng, Xiaofei ; Cha, Lei ; Li, Wuju
	2011
关键词	Next-generation sequencing multiple mapping Gibbs sampler least-square Bayesian RNA-SEQ DISCOVERY
英文摘要	This work presents two methods, the Least-square and Bayesian method, to solve the multiple mapping problem in extracting gene expression profiles through the next-generation sequencing. We parallel the tag sequences to genome, and partition them to improving the methods' efficiency. The essential feature of these methods is that they can solve the multiple mapping problem between genes and short-reads, while generating almost the same estimation in single-mapping situation as the traditional approaches. These two methods are compared by simulation and a real example, which was generated from radiation-induced lung cancer cells (A549), through mapping short-reads to human ncRNA database. The results show that the Bayesian method, as realized by Gibbs sampler, is more efficient and robust than the Least-square method.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000297647000008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Mathematics; SCI(E); 中国科学引文数据库(CSCD); 0; ARTICLE; 6; 1131-1145; 6
语种	英语
出处	SCI
出版者	frontiers of mathematics in china
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/314427]
专题	数学科学学院
推荐引用方式 GB/T 7714	Liu, Hui,Jiang, Zhichao,Fang, Xiangzhong,et al. Generate gene expression profile from high-throughput sequencing data. 2011-01-01.

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