The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
The real cost of sequencing: scaling computation to keep pace with data generation
Open accessAbstract
As the cost of sequencing continues to decrease and the amount of sequence data generated grows, new paradigms for data storage and analysis are increasingly important. The relative scaling behavior of these evolving technologies will impact genomics research moving forward.
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
DNA sequencing with chain-terminating inhibitors
Sanger, F., Nicklen, S., & Coulson, A. R. (1977) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.74.12.5463 · Cited by 68,572
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
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 global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationSalmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
Identification of common molecular subsequences
Smith, T. F., & Waterman, M. S. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90087-5 · Cited by 9,940
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Gut Dysbiosis and Muscle Aging: Searching for Novel Targets against Sarcopenia
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Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E., & Blayney, J. K. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby051 · Cited by 173
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Development of Bioinformatics Infrastructure for Genomics Research
Mulder, N. J., Adebiyi, E., Adebiyi, M., Adeyemi, S., Ahmed, A., Ahmed, R., Akanle, B., Alibi, M., Armstrong, D. L., Aron, S., et al. (2017) · Global Heart · doi:10.1016/j.gheart.2017.01.005 · Cited by 85
Metadata
- Authors
- Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D. J., Salichos, L., Zhang, J., Weinstock, G. M., Isaacs, F., Rozowsky, J., & Gerstein, M.
- Venue
- Genome Biology
- Published
- 2016-03-23
- Volume
- 17
- Issue
- 1
- Pages
- 53-53
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W2302703011
- Cited by
- 367
- References
- 57
- FWCI
- 26.94
- Type
- journal article
- Language
- en
- Open access
- gold
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- Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D. J., Salichos, L., Zhang, J., Weinstock, G. M., Isaacs, F., Rozowsky, J., & Gerstein, M. (2016). The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biology. https://doi.org/10.1186/s13059-016-0917-0
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.