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

Abstract

Abstract Background With 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. Results To 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. Conclusions Our 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.

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Affiliated Institutions

• Shanghai Institutes for Biological Sciences CN
• Chinese Academy of Sciences CN
• Sun Yat-sen University CN
• East China Normal University CN
• Shanghai Center For Bioinformation Technology CN
• Center for Excellence in Molecular Plant Sciences CN

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