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Recommendations for performance optimizations when using GATK3.8 and GATK4.

Jacob R Heldenbrand¹, Saurabh Baheti², Matthew A Bockol³

¹National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W. Clark St., Urbana, IL, USA.

BMC Bioinformatics

|November 10, 2019

Summary

This summary is machine-generated.

The Genome Analysis Toolkit (GATK) 3.8 and GATK4 offer performance improvements for genomic variant calling. GATK3.8 is faster for single samples, while GATK4 is more cost-effective for large-scale analyses.

Keywords:

Best practices Cluster computing Computational performance GATK Genomic variant calling Parallelization

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Area of Science:

Genomics
Bioinformatics

Background:

Genome Analysis Toolkit (GATK) is standard for genomic variant calling.
GATK has undergone significant evolution with GATK3.8 and GATK4 releases.
GATK4 represents a major rewrite with Spark implementation.

Purpose of the Study:

Analyze performance improvements in GATK3.8 and GATK4.
Provide guidance for optimal deployment in productive environments.
Help the community stay updated with GATK performance changes.

Main Methods:

Re-evaluated threading, garbage collection, I/O, and data-level parallelization.
Compared trade-offs between GATK3.8 and GATK4.
Optimized parameter values for variant calling procedures.

Main Results:

Reduced execution time by 29.3% for GATK3.8 and 16.9% for GATK4.
Achieved whole human genome analysis in hours with data splitting.
GATK4 demonstrated greater cost-effectiveness, enabling single-threaded analysis for multiple samples.

Conclusions:

For time-sensitive single-sample analysis, GATK3.8 with data splitting is recommended.
For cost-effective routine or population analyses, GATK4 processing multiple samples on one node is advised.
GATK4 offers significant cost savings per sample for large-scale studies.