Although 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. In the proposed data set with 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. This divide-and-conquer strategy enables memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies. (publisher abstract modified)
A Divide-and-Conquer Algorithm for Large-scale de Novo Transcriptome Assembly Through Combining Small Assemblies From Existing Algorithms
NCJ Number
253425
Journal
BMC Genomics Volume: 18 Dated: 2018 Pages: 43-50
Date Published
2018
Length
8 pages
Annotation
This project developed a divide-and-conquer strategy that enables algorithms that can assemble a large amount of RNA-Seq data to be utilized by subdividing a large RNA-Seq data set into small libraries.
Abstract
Date Published: January 1, 2018