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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 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]
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“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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Cites
(1979) · Cited by 43,841
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandić, I. (2008) · Future Generation Computer Systems · doi:10.1016/j.future.2008.12.001 · Cited by 5,946
Above the Clouds: A Berkeley View of Cloud Computing
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I. G., et al. (2009) · Cited by 5,838
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Real-Time DNA Sequencing from Single Polymerase Molecules
Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., et al. (2009) · Science · doi:10.1126/science.1162986 · Cited by 3,732
Self-citationBacterial Community Variation in Human Body Habitats Across Space and Time
Costello, E. K., Lauber, C. L., Hamady, M., Fierer, N., Gordon, J. I., & Knight, R. (2009) · Science · doi:10.1126/science.1177486 · Cited by 3,231
BLAST: at the core of a powerful and diverse set of sequence analysis tools
McGinnis, S., & Madden, T. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh435 · Cited by 2,173
Snir, M., Otto, S. W., WALKER, D. W., Dongarra, J. J., & Huss‐Lederman, S. (1996) · Cited by 2,152
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- Matsunaga, A., Tsugawa, M. & Fortes, J. in 4th IEEE International Conference on eScience. 222–229 (IEEE, Indianapolis, Indiana, 2008).
- Vance, A. Training to climb an Everest of digital data. New York Times B1 (11 Oct 2009).
- Bell, G. & Gray, J. Petascale computational systems: balanced cyberinfrastructure in a data-centric world Microsoft Research [online] , (2005).
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Kramer, A. A. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003477 · Cited by 1
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卜, 德., 焦, 飞., 赵, 国., 赵, 屹., 廖, 奇., 余, 鲲., 罗, 海., 孙, 亮., 李, 慧., & 陈, 润. (2013) · Scientia Sinica Vitae · doi:10.1360/zc2013-43-5-376 · Cited by 1
Metadata
- 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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