Parallelization of the trinity pipeline for de Novo transcriptome assembly

Abstract

This paper details a distributed-memory implementation of Chrysalis, part of the popular Trinity workflow used for de novo transcripto me assembly. We have implemented changes to Chrysalis, which was previously multi-threaded for shared-memory architectures, to change it to a hybrid implementation which uses both MPI and OpenMP. With the new hybrid implementation, we report speedups of about a factor of twenty for both Graph From Fasta and Reads To Transcripts on an iDataPlex cluster for a sugar beet dataset containing around 130 million reads. Along with the hybrid implementation, we also use PyFasta to speed up Bowtie execution by a factor of three which is also part of the Trinity workflow. Overall, we reduce the runtime of the Chrysalis step of the Trinity workflow from over 50 hours to less than 5 hours for the sugar beet dataset. By enabling the use of multi-node clusters, this implementation is a significant step towards making de novo transcripto me assembly feasible for ever bigger transcripto me datasets.