A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms

BMC Genomics

Research

Corbin D. Jones¹  Sing-Hoi Sze²  Aaron M. Tarone³  Meaghan L. Pimsler³  Jeffery K. Tomberlin³ 

[1] Department of Biology, University of North Carolina at Chapel Hill, NC 27599, Chapel Hill, USA;Department of Computer Science and Engineering, Texas A&M University, College Station, 77843, TX, USA;Department of Biochemistry & Biophysics, Texas A&M University, College Station, 77843, TX, USA;Department of Entomology, Texas A&M University, College Station, 77843, TX, USA;

关键词: RNA-Seq;    Transcriptome assembly;    Alternative splicing;    Gene expression;   

DOI  :  10.1186/s12864-017-3735-1

来源: Springer

【 摘 要 】

BackgroundWith increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries.ResultsWe develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory.ConclusionsOur strategy minimizes memory consumption while simultaneously obtaining comparable or improved accuracy over existing algorithms. It provides support for incremental updates of assemblies when new libraries become available.

【 授权许可】

CC BY   
© The Author(s) 2017

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