Computational solutions to large‐scale data management and analysis
Open accessAbstract
Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist - such as cloud and heterogeneous computing - to successfully tackle our big data problems.
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The exponential increase in the volume of genomic data due to sequencing technology advancements necessitates WGA algorithms that can handle large datasets efficiently and accurately.
“The exponential increase in the volume of genomic data due to advancements in sequencing technologies necessitates WGA algorithms that can handle large datasets efficiently and accurately [76].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [76]
The Abstract and Perspectives describe rapidly growing genomic data from sequencing advances and the need for large-scale computation. The MapReduce section discusses scaling sequence-read alignment, but the paper does not specifically establish a need for whole-genome alignment (WGA) algorithms that are both efficient and accurate. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTraining modules and review papers that describe typical workflows or common challenges and solutions for life scientists working with large, complex datasets fall short in that they do not simultaneously offer step-wise, reproducible, and practical guidance.
“While training modules and review papers exist that describe typical workflows or common challenges and solutions for life scientists working with large, complex datasets (Schadt et al., 2010), these resources fall short in that they do not simultaneously offer step-wise, reproducible, and practical guidance.”
From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Schadt et al., 2010
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- Authors
- Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P.
- Affiliations
- Pacific Biosciences (United States) (US)
- Stanford University (US)
- Corresponding author
- Schadt, Eric E.
- Venue
- Nature Reviews Genetics
- Published
- 2010-09
- Volume
- 11
- Issue
- 9
- Pages
- 647-657
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3124937
- OpenAlex
- W2023116290
- Cited by
- 631
- References
- 42
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 22 · 2023: 21 · 2022: 22 · 2021: 29 · 2020: 26 · 2019: 27 · 2018: 60 · 2017: 43 · 2016: 62 · 2015: 70 · 2014: 77 · 2013: 63 · 2012: 42
- FWCI
- 18.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (HHSN268201000034C)
- National Cancer Institute (R01CA130826)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- large-scale data management, high-dimensional data analysis, cloud computing, heterogeneous computing, genomic data, sequence data, bioinformatics, computational environment
- MeSH terms
- Animals, Humans, Sequence Analysis, DNA, Computational Biology, Genomics
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