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

Abstract

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.