Recommendations for performance optimizations when using GATK3.8 and GATK4

BMC Bioinformatics

Matthew E Hudson¹  Ravishankar K Iyer²  Derek E Wildman³  Eric D Wieben⁴  Eric W Klee⁵  Steven N Hart⁵  Nathan R Mattson⁶  Michael T Kalmbach⁶  Mathieu Wiepert⁶  Travis M Drucker⁶  Matthew A Bockol⁶  Saurabh Baheti⁷  Katherine I Kendig⁸  Jacob R Heldenbrand⁸  Liudmila S Mainzer⁸ 

[1] Department of Crop Sciences, University of Illinois at Urbana-Champaign;Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign;Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign;Mayo Clinic, Department of Biochemistry and Molecular Biology;Mayo Clinic, Department of Health Sciences Research;Mayo Clinic, Department of IT Executive Administration;Mayo Clinic, Department of Research Services;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign;

关键词: GATK;    Genomic variant calling;    Best practices;    Computational performance;    Cluster computing;    Parallelization;   

DOI  :  10.1186/s12859-019-3169-7

来源: DOAJ

【 摘 要 】

Abstract Background Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. Results We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. Conclusions In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

【 授权许可】

Unknown