Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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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)
  • Received : 2020.07.28
  • Accepted : 2020.08.13
  • Published : 2020.08.31
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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.

본 논문에서는 드노보 전사체 어셈블리의 수행시간을 단축하기 위해 RNA-seq 서열을 유전자계 정보를 활용하여 여러 노드로 분산이 가능한 방법을 제시한다. 제안하는 전사체 서열 데이터 분산기법의 성능을 측정하기 위해 애기장대의 리드를 4개의 데이터 셋(전체 비분류 리드, 완전 분류 리드, 모델 분류 리드, 무작위 분류 리드)으로 구성하여 실험을 수행하였다. 전체 비분류 데이터와 비교하여 생성된 유전자 콘티그(Contig)는 95% 일치하였고 동일한 리소스들을 사용하는 단일 노드에 비해 본 연구에서 제시하는 분산환경분산 환경 기반의 어셈블리 수행시간은 4.2배 단축되었다.

Keywords

References

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