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High-performance computing for SARS-CoV-2 RNAs clustering: a data science‒based genomics approach.

Anas Oujja1,2, Mohamed Riduan Abid1, Jaouad Boumhidi2

  • 1School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco.

Genomics & Informatics
|January 11, 2022
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Summary
This summary is machine-generated.

This study introduces a high-performance computing (HPC) tool for clustering SARS-CoV-2 RNA sequences using the longest common subsequence (LCS) algorithm. This approach aids in tracking and predicting virus mutations.

Keywords:
RNASARS-COV-2bioinformaticsdata sciencehigh-performance computinglongest common subsequence

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic data is rapidly expanding, necessitating robust high-performance computing (HPC) platforms.
  • Emerging SARS-CoV-2 mutations require advanced ICT tools for RNA data analysis, including classification and mutation tracking.

Purpose of the Study:

  • To present an HPC-based tool for clustering SARS-CoV-2 RNA sequences.
  • To leverage data science methodologies for efficient genomic data processing.
  • To assist in tracking and predicting SARS-CoV-2 mutations.

Main Methods:

  • Utilized a data science approach encompassing data collection, analysis, and visualization.
  • Employed the Hadoop MapReduce programming paradigm and adapted the longest common subsequence (LCS) algorithm for efficient pairwise RNA sequence comparison.
  • Extracted SARS-CoV-2 RNA sequences from the U.S. National Center for Biotechnology Information (NCBI) Virus repository.

Main Results:

  • Developed an HPC-based clustering tool for SARS-CoV-2 RNA.
  • Successfully computed LCS lengths to quantify RNA sequence dissimilarities for clustering.
  • Conducted a comparative performance study of the LCS algorithm under varying workloads and Hadoop configurations.

Conclusions:

  • The HPC-based LCS clustering approach provides an efficient method for analyzing SARS-CoV-2 RNA data.
  • The tool facilitates the classification and potential prediction of virus mutations.
  • Performance analysis offers insights into optimizing HPC resource utilization for genomic data processing.