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

作者：Sze Sing Hoi^*; Pimsler Meaghan L; Tomberlin Jeffery K; Jones Corbin D; Tarone Aaron M

来源：BMC Genomics, 2017, 18(S4): 387.

DOI：10.1186/s12864-017-3735-1

• RNA-Seq
• Transcriptome assembly
• Alternative splicing
• Gene expression

Background: With 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.
Results: We 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.
Conclusions: Our 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.

• 出版日期2017-5-24