The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

TLDR

Next‑generation DNA sequencing projects such as the 1000 Genomes Project are transforming our understanding of genetic variation, but the enormous data volumes—nearly five terabases for the pilot—make efficient, robust analysis tools hard to develop. The authors present the Genome Analysis Toolkit (GATK), a structured programming framework that applies MapReduce‑style functional programming to simplify the creation of efficient, robust NGS analysis tools. GATK supplies a compact set of data‑access patterns, decouples analysis logic from data‑management infrastructure, and is optimized for correctness, stability, CPU and memory efficiency, enabling distributed and shared‑memory parallelization and supporting tools such as coverage calculators and SNP callers. The framework has already facilitated rapid development of efficient, robust NGS tools that are deployed in large‑scale projects like the 1000 Genomes Project and The Cancer Genome Atlas.

Abstract

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.