Genome Analysis Tool Kit* (GATK*)

A software package developed at the Broad Institute to analyze next-generation sequencing data.

Infrastructure for Deploying GATK Best Practices Pipeline

The Broad Institute GATK Best Practices pipeline has helped standardize genomic analysis by providing step-by-step recommendations for performing pre-processing and variant discovery analysis. Pre-processing refers to generating analysis-ready mapped reads from raw reads using tools like BWA*, Picard* tools, and the Genome Analysis Tool Kit. These analysis-ready reads are passed through the Variant Calling step of Variant Discovery analysis to generate variants per-sample. The first part of the GATK Best Practices pipeline takes two FASTQ files, a reference genome, and dbSNP and 1000g_indels VCF files as input and outputs a gVCF file per-sample. These gVCF files are then further analyzed using Joint Genotyping and Variant Filtering steps of the Variant Discovery analysis.

The tools mentioned in the GATK Best Practices Pipeline require enormous computational power and long periods of time to complete. Benchmarking such a pipeline allows users to better determine the recommended hardware and optimize parameters to help reduce execution time. In an effort to advance the standardization and optimization of genomic pipelines, Intel has benchmarked the GATK Best Practices pipeline using Workflow Profiler, an open-source tool that provides insight into system resources (such as CPU/Disk Utilization, Committed Memory, etc.) and helps eliminate resource bottlenecks.

Performance Results

By using the recommended hardware and applying the thread-level and process-level optimizations to the single sample Solexa-272221 WGS* dataset, we achieve different levels of performance. The chart to the right shows how the execution time scales with the number of threads and processes across various pipeline components. For this particular dataset, all components show a decrease in run time going from 1 to 36 threads. Overall, the execution time from BWA-MEM* to Haplotype-Caller went from 227 hours to 36 hours, a 6x speed-up.1 These performance guidelines can be used to size genomics clusters running GATK Best Practices pipelines.

This benchmarking study provides recommendations of Intel® hardware and guidelines on running a set of whole genome sequences through the GATK Best Practices pipeline. Researchers whose aim is to use this pipeline for multiple datasets may use this paper to scale the number of machines to match the number of datasets that require analysis. For example, an institution whose aim is to analyze 100 WGS a month may need about 5 machines (each with 36 cores) running in parallel to achieve this goal.

Download the code ›

Size and scale your infrastructure according to your workloads with the GATK reference architecture ›

免責事項

1

ベンチマーク結果は、「Spectre」および「Meltdown」と呼ばれる脆弱性への対処を目的とした最近のソフトウェア・パッチおよびファームウェア・アップデートの適用前に取得されたものです。パッチやアップデートを適用したデバイスやシステムでは、同様の結果が得られないことがあります。

性能に関するテストに使用されるソフトウェアとワークロードは、性能がインテル® マイクロプロセッサー用に最適化されていることがあります。SYSmark* や MobileMark* などの性能テストは、特定のコンピューター・システム、コンポーネント、ソフトウェア、操作、機能に基づいて行ったものです。結果はこれらの要因によって異なります。製品の購入を検討される場合は、ほかの製品と組み合わせた場合の本製品の性能など、ほかの情報や性能テストも参考にして、パフォーマンスを総合的に評価することをお勧めします。詳細については、http://www.intel.com/benchmarks を参照してください。