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Cited by 19Genomics and Phylogenetic StudiesScientific Computing and Data ManagementGenetics, Bioinformatics, and Biomedical Research

Computational Strategies for Scalable Genomics Analysis

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Shi, Lizhen, Wang, Zhong·2019·Genes
DOI 10.3390/genes10121017PMC PMC6947637
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Abstract

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

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  • The cost, resources, and expertise required by parallel and distributed computing approaches limit their adoption in WGA.

    “the potential of parallel and distributed computing in enhancing the efficiency of WGA has been recognized, but the cost, resources, and expertise required by these approaches limit their adoption [85].”

    From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [85]

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Cited by

Sorted by citations

Authors
Shi, L., & Wang, Z.
Affiliations
  • Florida State University (US)
  • Lawrence Berkeley National Laboratory (US)
Corresponding author
Wang, Zhong
ORCID
Venue
Genes
Publisher
MDPI AG
Published
2019-12-06
Volume
10
Issue
12
Pages
1017
ISSN
2073-4425 (ISSN-L)
DOI
10.3390/genes10121017
PMC
PMC6947637
OpenAlex
W2993246561
Cited by
19
References
2
Citations by year
2026: 2 · 2025: 1 · 2024: 3 · 2023: 6 · 2022: 4 · 2021: 1 · 2020: 2
FWCI
0.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Genes (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
Keywords
genomic data analysis, next-generation sequencing, big data, distributed parallel computing, bioinformatics software, hardware acceleration, scalability, high-throughput sequencing data
MeSH terms
Algorithms, Software, Computational Biology, Genomics, High-Throughput Nucleotide Sequencing

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