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http://dx.doi.org/10.9708/jksci.2020.25.08.001

K-mer Based RNA-seq Read Distribution Method For Accelerating De Novo Transcriptome Assembly  

Kwon, Hwijun (School of Computer Science and Engineering, Kyungpook National University)
Jung, Inuk (School of Computer Science and Engineering, Kyungpook National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.8, 2020 , pp. 1-8 More about this Journal
Abstract
In this paper, we propose a gene family based RNA-seq read distribution method in means to accelerate the overal transcriptome assembly computation time. To measure the performance of our transcriptome sequence data distribution method, we evaluated the performance by testing four types of data sets of the Arabidopsis thaliana genome (Whole Unclassified Reads, Family-Classified Reads, Model-Classified Reads, and Randomly Classified Reads). As a result of de novo transcript assembly in distributed nodes using model classification data, the generated gene contigs matched 95% compared to the contig generated by WUR, and the execution time was reduced by 4.2 times compared to a single node environment using the same resources.
Keywords
Gene Family; De novo transcriptome assembly; Distribution; Acceleration; Classification model; K-mer;
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