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.
More Information
Genomics Codes
BWA-ALN* 0.5.10
A popular software package for mapping low-divergent sequences against a large-reference genome, such as the human genome.
MPI-HMMER* v2.3
An open-source implementation of the HMMER* protein sequence analysis suite.
BLASTn*/BLASTp*
An algorithm for comparing primary biological sequence information.
GATK*
A software package developed at the Broad Institute to analyze next-generation sequencing data.
QIAGEN
QIAGEN Bioinformatics* solutions deliver faster time to insight by combining powerful analytics that are able to interpret complex biological processes.
Halvade*
Halvade* is a MapReduce implementation of the best-practice DNA sequencing pipeline as recommended by Broad Institute.
ABySS*
ABySS* is an open-source de novo genome assembler for short paired-end reads.
DIDA*
DIDA* performs large-scale alignment tasks by distributing the indexing and alignment stages into smaller subtasks over a cluster of compute nodes.
elPrep*
elPrep* is a high-performance tool for preparing SAM/BAM/CRAM files for variant calling in genomic sequencing pipelines.
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Информация о продукте и производительности
Результаты эталонных тестов получены до применения недавних пакетов исправлений ПО и обновлений встроенного ПО, предназначенных для устранения уязвимостей под названием «Spectre» и «Meltdown». После установки этих обновлений данные результаты могут быть неприменимы к вашему устройству или системе.
Программное обеспечение и рабочие задачи, используемые в тестах оценки производительности, оптимизированы для обеспечения высокой производительности только с микропроцессорами Intel®. Тесты производительности, такие как SYSmark* и MobileMark*, проводятся для конкретных конфигураций вычислительных систем, компонентов, программного обеспечения, операций и функций. Любые изменения этих параметров могут привести к изменению конечных результатов. При принятии решения о покупке следует обращаться к другим источникам информации и данным тестирования производительности, в том числе к информации о производительности данного продукта в сочетании с другими продуктами. Подробная информация: http://www.intel.com/benchmarks.