A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms

BMC Genomics

Research

Sing-Hoi Sze¹  Aaron M. Tarone²  Jonathan J. Parrott² 

[1] Department of Computer Science and Engineering, Texas A&M University, College Station, 77843, Mexico, TX, USA;Department of Biochemistry & Biophysics, Texas A&M University, College Station, 77843, Mexico, TX, USA;Department of Entomology, Texas A&M University, College Station, 77843, Mexico, TX, USA;

关键词: Divide-and-conquer;    RNA-Seq;    de novo;   

DOI  :  10.1186/s12864-017-4270-9

来源: Springer

【 摘 要 】

BackgroundWhile the continued development of high-throughput sequencing has facilitated studies of entire transcriptomes in non-model organisms, the incorporation of an increasing amount of RNA-Seq libraries has made de novo transcriptome assembly difficult. Although algorithms that can assemble a large amount of RNA-Seq data are available, they are generally very memory-intensive and can only be used to construct small assemblies.ResultsWe develop a divide-and-conquer strategy that allows these algorithms to be utilized, by subdividing a large RNA-Seq data set into small libraries. Each individual library is assembled independently by an existing algorithm, and a merging algorithm is developed to combine these assemblies by picking a subset of high quality transcripts to form a large transcriptome. When compared to existing algorithms that return a single assembly directly, this strategy achieves comparable or increased accuracy as memory-efficient algorithms that can be used to process a large amount of RNA-Seq data, and comparable or decreased accuracy as memory-intensive algorithms that can only be used to construct small assemblies.ConclusionsOur divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

【 授权许可】

CC BY   
© The Author(s) 2017