Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

BMC Bioinformatics

Proceedings

Yi Wang¹  Xuan Li²  Yi-Meng Kong²  Qiong-Yi Zhao²  Pei Hao³  Da Luo⁴ 

[1] Institute of Massive Computing, Software Engineering Institute, East China Normal University, 3663 North Zhongshan Road, 200062, Shanghai, China;Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200032, Shanghai, China;Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, 200235, Shanghai, China;State Key Laboratory of Biocontrol, Sun Yat Sen University, 510275, Guangzhou, China;

关键词: Fission Yeast;    Memory Usage;    Transcriptome Assembly;    Coverage Depth;    Read Pair;   

DOI  :  10.1186/1471-2105-12-S14-S2

来源: Springer

【 摘 要 】

BackgroundWith the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data.ResultsTo reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies.ConclusionsOur work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.

【 授权许可】

Unknown   
© Zhao et al; licensee BioMed Central Ltd. 2011. 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.