一种针对RNA-Seq 数据的基因异构体表达水平计算方法

doi:10-3969/j.issn.0258.8021. 2013.04.011

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摘要 RNA-Seq是基于高通量测序技术对转录组进行研究的实验技术，被大量用来进行基因的选择性剪切研究。针对RNA-Seq数据中读段对基因异构体的多源映射以及读段在基因参考序列上呈非均匀分布的问题，基于文本数据分析领域流的LDA（latent dirichlet allocation）模型，提出了一个新的基因异构体表达值计算方法LDAseq。利用已知的基因异构体注释信息对模型参数进行约束，解决读段对基因异构体的多源映射问题；通过引入固定长度的“探针”将基因参考序列进行分段，解决读段在整个基因参考序列上呈非均匀分布的问题。将LDAseq应用到一个小鼠数据集和一个人类乳腺癌数据集，并和目前流行的方法Cufflinks和RSEM进行对比。结果表明，所提出的LDAseq方法相比Cufflinks和RSEM准确率分别提高了75.5%和62.8%，从而获得了较为准确的基因异构体表达水平计算结果。

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	刘学军^*李蒙张礼

关键词 ： RNA-Seq, 异构体表达, 多源映射, LDA, 概率模型

Abstract：RNA-Seq is a new experimental technique for trancriptome research based on highthroughput sequencing. It is increasingly used in the research of alternative splicing variation. There are two difficulties in the analysis of RNA-Seq data. One is readisoform multimapping, the other is nonuniform distribution of reads along the gene reference sequence. This paper proposed a new method, so called LDAseq, to calculate isoform expression level based on LDA (Latent Dirichlet Allocation) commonly used to model text corpora. LDAseq utilized the known geneisoform annotation to constrain the hyperparameters for dealing with readisoform multimapping. To modeling the non-uniform distribution of reads along reference sequence, LDAseq introduced “probes” with fixed length to break up the long reference sequence. We applied LDAseq to a mouse dataset and a human breast cancer dataset, and compared the performance of LDAseq with currently used alternatives, such as Cufflinks and RSEM. Results showed that the computation accuracy of LDAseq was increased 75.5% and 62.8% compared with that of Cufflinks and RSEM, respectively.

Key words： RNA-Seq isoform expression multi-mapping LDA probabilistic model

基金资助: 国家自然科学基金（61170152）；中央高校基本科研业务费专项（CXZZ11_0217）

引用本文:

刘学军^*李蒙张礼. 一种针对RNA-Seq 数据的基因异构体表达水平计算方法[J]. 中国生物医学工程学报, 2013, 32(4): 454-463.
LIU Xue Jun^* LI Meng ZHANG Li. A Method of Isoform Expression Calculation for RNA-Seq Data. journal1, 2013, 32(4): 454-463.

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http://cjbme.csbme.org/CN/10-3969/j.issn.0258.8021. 2013.04.011 或 http://cjbme.csbme.org/CN/Y2013/V32/I4/454

［1］Pan Qun, Shai Ofer, Lee LJ, et al. Blencowe BJ. Deep surveying of alternative splicing complexity in the human transcriptome by highthroughput sequencing ［J］. Nature Genetices, 2008, 40(12): 1413-1415.
［2］Skotheim RI, Nees M. Alternative splicing in cancer: noise, functional, or systematic? ［J］. The International Journal of Biochemistry and Cell Biology, 2007, 39: 1432-1449.
［3］Wang Zhong, Gerstein M, Snyder M. RNASeq: a revolutionary tool for transcriptomics ［J］. Nature Reviews Genetics, 2009, 10(1): 57-63.
［4］王曦, 汪小我, 王立坤, et al. 新一代高通量RNA测序数据的处理与分析［J］. 生物化学与生物物理进展, 2010, 37(8): 837-846.
［5］Turro E, Su ShuYi, Goncalves , et al. Haplotype and isoform specific expression estimation using multimapping RNAseq reads ［J］. Genome biology, 2011, 12: R13.
［6］Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNASeq ［J］. Nature Methods, 2008, 5(7): 621-628.
［7］Jiang Hui, Wong Wing Hung. Statistical inferences for isoform expression in RNASeq ［J］. Bioinformatics, 2009, 25(8): 1026-1032.
［8］Kim H, Bi Yingtao, Pal S, et al. IsoformEx: isoform level gene expression estimation using weighted nonnegative least squares from mRNASeq data ［J］. BMC Bioinformatics, 2011, 12: 305.
［9］Li Bo, Ruotti V, Stewart R.M, et al. RNASeq gene expression estimation with read mapping uncertainty ［J］. Bioinformatics, 2010, 26(4): 493-500.
［10］Li Bo, Dewey CN. RSEM: accurate transcript quantification from RNASeq data with or without a reference genome ［J］. BMC Bioinformatics, 2011, 12: 323.
［11］Katz Y, Wang Eric T, Airoldi EM, et al. Analysis and design of RNA sequencing experiments for identifying isoform regulation ［J］. Nature Methods, 2010, 7: 1009-1015.
［12］Glaus P, Honkela A, Rattray M. Identifying differentially expressed transcripts from RNASeq data with biological variation ［J］. Bioinformatics, 2012, 28(3): 1721-1728.
［13］Pepke S, Wold B, Mortazavi A. Computation for ChIPseq and RNASeq studies ［J］. Nature Methods Supplement, 2009, 6:S22-S32.
［14］Li Jun, Jiang Hui, Wong Wing Hung. Modeling nonuniformity in shortread rates in RNASeq data ［J］. Genome Biology, 2010, 11: R50.
［15］Srivastava S, Chen Liang. A twoparameter generalized Poisson model to improve the analysis of RNASeq data ［J］. Nucleic Acids Research, 2010, 38(17): e170.
［16］Wu Zhengpeng, Wang Xi, Zhang Xuegong. Using nonuniform read distribution models to improve isoform expression inference in RNASeq ［J］. Bioinformatics, 2011, 27(4): 502-508.
［17］Roberts A, Trapnell C, Donaghey J, et al. Improving RNASeq expression estimates by correcting for fragment bias ［J］. Genome Biology, 2011, 12: R22.
［18］Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation ［J］. Journal of Machine Learning Research, 2003, 3:993-1022.
［19］赵志兰, 刘学军, 张礼. 一种基于概率模型的RNASeq数据分析方法［C］// 2011中国生物医学工程联合学术年会论文集（光盘版）. 武汉: 中国生物医学工程学会,2011.
［20］Langmead B, Trapnell C, Pop M, et al. Ultrafast and memoryefficient alignment of short DNA sequences to the human genome［J］. Genome Biology, 2009, 10(R25).
［21］Li Ruiqiang, Yu Chang, Li Yingrui, et al. SOAP2: an improved ultrafast tool for short read alignment ［J］. Bioinformatics, 2009, 25(15): 1966-1967.
［22］Wang ET, Sandberg R, Luo Shujun, et al. Alternative isoform regulation in human tissue transcriptomes ［J］. Nature, 2008, 456(7221): 470-476.