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The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
Open accessAbstract
Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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“These results indicate that a combination of appropriate sample preparation and long reads can readily create cDNA profiles that approach the full-length sequences of mRNAs from complex samples, underlining the utility of long read platforms, despite the lower read depths they may produce37.”
From Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study (2014) · cites this paper as [37]
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Metadata
- Authors
- McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., & DePristo, M. A.
- Affiliations
- Broad Institute (US)
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2010-07-19
- Volume
- 20
- Issue
- 9
- Pages
- 1297-1303
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC2928508
- OpenAlex
- W2119180969
- Cited by
- 27,895
- References
- 10
- Citations by year
- 2026: 1950 · 2025: 2502 · 2024: 2891 · 2023: 2502 · 2022: 2637 · 2021: 2797 · 2020: 2482 · 2019: 2106 · 2018: 1874 · 2017: 1655 · 2016: 1458 · 2015: 1111 · 2014: 871 · 2013: 578 · 2012: 294
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Broad Institute
- National Human Genome Research Institute (U54HG003067, HG003067, 54 HG003067, U01HG005208)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- 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
- GATK, MapReduce, next-generation sequencing, functional programming, shared-memory parallelism, 1000 Genomes Project, TCGA, genetic variation
- MeSH terms
- Base Sequence, Software, Genome, Sequence Analysis, DNA, Genomics
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