GATK

The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. 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.


References in zbMATH (referenced in 15 articles )

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  1. Kamm, Jack; Terhorst, Jonathan; Durbin, Richard; Song, Yun S.: Efficiently inferring the demographic history of many populations with allele count data (2020)
  2. Zhou, Tianjian; Sengupta, Subhajit; Müller, Peter; Ji, Yuan: TreeClone: reconstruction of tumor subclone phylogeny based on mutation pairs using next generation sequencing data (2019)
  3. Robinson, Peter N.; Piro, Rosario Michael; Jäger, Marten: Computational exome and genome analysis (2018)
  4. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  5. Barturen, Guillermo; Oliver, José L.; Hackenberg, Michael: Error correction in methylation profiling from NGS bisulfite protocols (2017)
  6. Bonizzoni, Paola; Della Vedova, Gianluca; Pirola, Yuri; Previtali, Marco; Rizzi, Raffaella: An external-memory algorithm for string graph construction (2017)
  7. Fu, Rong; Wang, Pei; Ma, Weiping; Taguchi, Ayumu; Wong, Chee-Hong; Zhang, Qing; Gazdar, Adi; Hanash, Samir M.; Zhou, Qinghua; Zhong, Hua; Feng, Ziding: A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data (2017)
  8. Liu, Yongchao; Schmidt, Bertil: CUSHAW suite: parallel and efficient algorithms for NGS read alignment (2017)
  9. Shpak, Max; Ni, Yang; Lu, Jie; Müller, Peter: Variance in estimated pairwise genetic distance under high versus low coverage sequencing: the contribution of linkage disequilibrium (2017)
  10. Ji, Yuan; Sengupta, Subhajit; Lee, Juhee; Müller, Peter; Gulukota, Kamalakar: Estimating latent cell subpopulations with Bayesian feature allocation models (2015)
  11. Lee, Juhee; Müller, Peter; Gulukota, Kamalakar; Ji, Yuan: A Bayesian feature allocation model for tumor heterogeneity (2015)
  12. McCallum, Kenneth J.; Ionita-Laza, Iuliana: Empirical Bayes scan statistics for detecting clusters of disease risk variants in genetic studies (2015)
  13. Daugelaite, Jurate; O’Driscoll, Aisling; Sleator, Roy D.: An overview of multiple sequence alignments and cloud computing in bioinformatics (2013)
  14. Zhao, Zhigen; Wang, Wei; Wei, Zhi: An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data (2013)
  15. Muralidharan, Omkar; Natsoulis, Georges; Bell, John; Ji, Hanlee; Zhang, Nancy R.: Detecting mutations in mixed sample sequencing data using empirical Bayes (2012)