The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
A Survey of General‐Purpose Computation on Graphics Hardware
Owens, J. D., Luebke, D. P., Govindaraju, N. K., Harris, M., Krüger, J., Lefohn, A. E., & Purcell, T. J. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01012.x · Cited by 2,107
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Barroso, L. A., & Hölzle, U. (2009) · Synthesis lectures on computer architecture · doi:10.2200/s00193ed1v01y200905cac006 · Cited by 1,711
Scalable Parallel Programming with CUDA
Nickolls, J. R., Buck, I., Garland, M., & Kevin Skadron, Y. (2008) · Queue · doi:10.1145/1365490.1365500 · Cited by 1,666
(2008) · doi:10.1145/1401132.1401152 · Cited by 1,575
Direct detection of DNA methylation during single-molecule, real-time sequencing
Flusberg, B. A., Webster, D. R., Lee, J. H., Travers, K. J., Olivares, E. C., Clark, T. A., Korlach, J., & Turner, S. W. (2010) · Nature Methods · doi:10.1038/nmeth.1459 · Cited by 1,460
Genetic Mapping in Human Disease
Altshuler, D., Daly, M. J., & Lander, E. S. (2008) · Science · doi:10.1126/science.1156409 · Cited by 1,452
Infernal 1.0: inference of RNA alignments
Nawrocki, E. P., Kolbe, D. L., & Eddy, S. R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp157 · Cited by 1,428
Genetics of gene expression and its effect on disease
Emilsson, V., Thorleifsson, G., Zhang, B., Leonardson, A. S., Zink, F., Zhu, J., Carlson, S., Helgason, A., Walters, G. B., Gunnarsdottir, S., et al. (2008) · Nature · doi:10.1038/nature06758 · Cited by 1,296
Self-citationBandura, D. R., Baranov, V. I., Ornatsky, O. I., Antonov, A., Kinach, R., Lou, X., Pavlov, S., Vorobiev, S., Dick, J. E., & Tanner, S. D. (2009) · Analytical Chemistry · doi:10.1021/ac901049w · Cited by 1,295
Human Genome Sequencing Using Unchained Base Reads on Self-Assembling DNA Nanoarrays
Drmanac, R., Sparks, A. B., Callow, M. J., Halpern, A. L., Burns, N. L., Kermani, B. G., Carnevali, P., Nazarenko, I., Nilsen, G. B., Yeung, G., et al. (2010) · Science · doi:10.1126/science.1181498 · Cited by 1,212
Mapping the Genetic Architecture of Gene Expression in Human Liver
Schadt, E. E., Molony, C. M., Chudin, E., Hao, K., Yang, X., Lum, P. Y., Kasarskis, A., Zhang, B., Wang, S. S.-M., Suver, C., et al. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060107 · Cited by 918
Self-citationVariations in DNA elucidate molecular networks that cause disease
Chen, Y., Zhu, J., Lum, P. Y., Yang, X., Pinto, S., MacNeil, D. J., Zhang, C., Lamb, J., Edwards, S., Sieberts, S. K., et al. (2008) · Nature · doi:10.1038/nature06757 · Cited by 887
Self-citationGropp, W., Huss‐Lederman, S., Lumsdaine, A., Lusk, E. L., Nitzberg, B., Saphir, W. C., & Snir, M. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/4789.001.0001 · Cited by 702
CloudBurst: highly sensitive read mapping with MapReduce
Schatz, M. C. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp236 · Cited by 630
Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008) · Nature Genetics · doi:10.1038/ng.167 · Cited by 553
Self-citationAccelerating molecular dynamic simulation on graphics processing units
Friedrichs, M. S., Eastman, P. K., Vaidyanathan, V., Houston, M., LeGrand, S., Beberg, A. L., Ensign, D. L., Bruns, C. M., & Pande, V. S. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21209 · Cited by 529
Searching for SNPs with cloud computing
Langmead, B., Schatz, M. C., Lin, J., Pop, M., & Salzberg, S. L. (2009) · Genome biology · doi:10.1186/gb-2009-10-11-r134 · Cited by 490
Cloud computing: A new business paradigm for biomedical information sharing
Rosenthal, A. S., Mork, P., Li, M. H., Stanford, J., Koester, D., & Reynolds, P. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.08.014 · Cited by 365
High-throughput sequence alignment using Graphics Processing Units
Schatz, M. C., Trapnell, C., Delcher, A. L., & Varshney, A. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-474 · Cited by 310
A network view of disease and compound screening
Schadt, E. E., Friend, S. H., & Shaywitz, D. A. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2826 · Cited by 289
Self-citationLiu, Y., Maskell, D. L., & Schmidt, B. (2009) · BMC Research Notes · doi:10.1186/1756-0500-2-73 · Cited by 265
Barroso, L. A., & Hölzle, U. (2009) · Synthesis lectures on computer architecture · doi:10.1007/978-3-031-01722-3 · Cited by 197
VertNet: A New Model for Biodiversity Data Sharing
Constable, H., Guralnick, R., Wieczorek, J. R., Spencer, C., & Peterson, A. T. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000309 · Cited by 185
Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges
Stein, L. D. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2414 · Cited by 160
Direct sequencing of the human microbiome readily reveals community differences
Kuczynski, J., Costello, E. K., Nemergut, D. R., Zaneveld, J., Lauber, C. L., Knights, D., Koren, O., Fierer, N., Kelley, S. T., Ley, R. E., et al. (2010) · Genome biology · doi:10.1186/gb-2010-11-5-210 · Cited by 156
Third-generation sequencing fireworks at Marco Island
Munroe, D. J., & Harris, T. (2010) · Nature Biotechnology · doi:10.1038/nbt0510-426 · Cited by 79
A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules
Zhang, W., Zhu, J., Schadt, E. E., & Liu, J. S. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000642 · Cited by 69
Self-citationAccelerating molecular dynamic simulation on the cell processor and Playstation 3
Luttmann, E., Ensign, D. L., Vaidyanathan, V., Houston, M., Rimon, N., Øland, J., Jayachandran, G., Friedrichs, M. S., & Pande, V. S. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.21054 · Cited by 44
High-throughput Bayesian network learning using heterogeneous multicore computers
Linderman, M. D., Bruggner, R. V., Athalye, V. R., Meng, T. H. Y., Asadi, N. B., & Nolan, G. P. (2010) · International Conference on Supercomputing · doi:10.1145/1810085.1810101 · Cited by 17
Self-citationSansom, C. (2010) · Nature Biotechnology · doi:10.1038/nbt0110-13 · Cited by 16
Flagged first, then by citations
2 of the 42 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- 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).
Cited by
Sorted by citations
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.031 · Cited by 1,905
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
A window into third-generation sequencing
Schadt, E. E., Turner, S., & Kasarskis, A. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq416 · Cited by 1,026
Self-citationThe Origin of the Haitian Cholera Outbreak Strain
Chin, C.-S., Sorenson, J. M., Harris, J. B., Robins, W. P., Charles, R. C., Jean‐Charles, R. R., Bullard, J., Webster, D. R., Kasarskis, A., Peluso, P. S., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1012928 · Cited by 747
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
