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

Sing-Hoi, Sze, Parrott, Jonathan J and Tarone, Aaron M (2017) A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms. BMC Genomics, 18 (10). ISSN 1471-2164

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Abstract or description

While 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. We 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. Our divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

Item Type: Article
Faculty: School of Law, Policing and Forensics > Law
Depositing User: Jonathan PARROTT
Date Deposited: 17 Dec 2018 14:12
Last Modified: 17 Dec 2018 14:13
URI: http://eprints.staffs.ac.uk/id/eprint/5020

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