Accuracy of RNA-Seq and its dependence on sequencing depth

BMC Bioinformatics

Research

Shoudan Liang¹  Guoshuai Cai¹  Xuelin Huang²  Juhee Lee²  Yuan Ji²  Yue Lu³  Peter Müller⁴  Hua Li⁵ 

[1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 77030, Houston, Texas, USA;Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 77030, Houston, Texas, USA;Department of Leukemia, The University of Texas MD Anderson Cancer Center, 77030, Houston, Texas, USA;Department of Mathematics, The University of Texas at Austin, 78712, Austin, Texas, USA;Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, 77030, Houston, Texas, USA;

关键词: False Discovery Rate;    Binomial Distribution;    Receiver Operating Characteristic;    Library Preparation;    Sequencing Depth;   

DOI  :  10.1186/1471-2105-13-S13-S5

来源: Springer

【 摘 要 】

BackgroundThe cost of DNA sequencing has undergone a dramatical reduction in the past decade. As a result, sequencing technologies have been increasingly applied to genomic research. RNA-Seq is becoming a common technique for surveying gene expression based on DNA sequencing. As it is not clear how increased sequencing capacity has affected measurement accuracy of mRNA, we sought to investigate that relationship.ResultWe empirically evaluate the accuracy of repeated gene expression measurements using RNA-Seq. We identify library preparation steps prior to DNA sequencing as the main source of error in this process. Studying three datasets, we show that the accuracy indeed improves with the sequencing depth. However, the rate of improvement as a function of sequence reads is generally slower than predicted by the binomial distribution. We therefore used the beta-binomial distribution to model the overdispersion. The overdispersion parameters we introduced depend explicitly on the number of reads so that the resulting statistical uncertainty is consistent with the empirical data that measurement accuracy increases with the sequencing depth. The overdispersion parameters were determined by maximizing the likelihood. We shown that our modified beta-binomial model had lower false discovery rate than the binomial or the pure beta-binomial models.ConclusionWe proposed a novel form of overdispersion guaranteeing that the accuracy improves with sequencing depth. We demonstrated that the new form provides a better fit to the data.

【 授权许可】

Unknown   
© Cai et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.