K-mer Based RNA-seq Read Distribution Method For Accelerating De Novo Transcriptome Assembly (전사체 어셈블리 가속화를 위한 K-mer 기반 RNA-seq 데이터 분산 방법)

kwon hwi jun (권휘준); Inuk Jung (정인욱)

doi:10.9708/jksci.2020.25.08.001

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

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(8), pp.1-8
DOI : 10.9708/jksci.2020.25.08.001
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 28, 2020
Accepted : August 13, 2020
Published : August 31, 2020

kwon hwi jun ¹, Inuk Jung ¹

¹경북대학교

Accredited

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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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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* References for papers published after 2023 are currently being built.