Commentary: The Future of Whole-Genome Sequencing for Public Health and the Clinic

Allard, Marc W.

doi:10.1128/jcm.01082-16

Cited by 31 publications

(24 citation statements)

References 7 publications

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“…The evolution of sequencing technologies [1, 2] encouraged many applications of Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) in genomic research and the clinic [3, 4]. One of these applications is genomic variant calling, commonly performed using the Genome Analysis Toolkit (GATK), maintained by the Broad Institute [5–8].…”

Section: Introductionmentioning

confidence: 99%

Recommendations for performance optimizations when using GATK3.8 and GATK4

et al. 2019

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BackgroundUse 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.ResultsWe 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.ConclusionsIn 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.

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Section: Introductionmentioning

confidence: 99%

Recommendations for performance optimizations when using GATK3.8 and GATK4

et al. 2019

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“…Whole-genome sequence (WGS) data is increasingly important in public health microbiology (1–4). The data can be used to replicate many of the basic bacterial sub-typing approaches, as well as support epidemiological investigations, such as surveillance and outbreak investigation (5–7).…”

Section: Announcementmentioning

confidence: 99%

Complete microbial genomes for public health in Australia and Southwest Pacific

Baines

Silva

Carter

et al. 2019

Preprint

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“…Currently routine WGS in clinical microbiology is most suited to public health and reference laboratories and is unlikely to be adopted on a widespread basis by individual hospitals in the short-term and many factors need to be considered, including cost, infrastructure, sequencing capacity and data analysis capabilities [ 78 ]. Further cost reductions, technological developments and perhaps most importantly, overcoming challenges associated with bioinformatic analysis [ 78 , 79 , 80 ] will all aid the more widespread adoption of WGS in clinical settings. Finally, a recent study has shown that significant monetary savings (in excess of €200,000 in this case) can be achieved by adopting WGS to inform more targeted infection control policies [ 81 ].…”

Section: Whole-genome Sequencingmentioning

confidence: 99%

Advances in the Microbiome: Applications to Clostridium difficile Infection

Culligan

Sleator

2016

JCM

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Clostridium difficile is a major cause of morbidity and mortality worldwide, causing over 400,000 infections and approximately 29,000 deaths in the United States alone each year. C. difficile is the most common cause of nosocomial diarrhoea in the developed world, and, in recent years, the emergence of hyper-virulent (mainly ribotypes 027 and 078, sometimes characterised by increased toxin production), epidemic strains and an increase in the number of community-acquired infections has caused further concern. Antibiotic therapy with metronidazole, vancomycin or fidaxomicin is the primary treatment for C. difficile infection (CDI). However, CDI is unique, in that, antibiotic use is also a major risk factor for acquiring CDI or recurrent CDI due to disruption of the normal gut microbiota. Therefore, there is an urgent need for alternative, non-antibiotic therapeutics to treat or prevent CDI. Here, we review a number of such potential treatments which have emerged from advances in the field of microbiome research.

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Commentary: The Future of Whole-Genome Sequencing for Public Health and the Clinic

Cited by 31 publications

References 7 publications

Recommendations for performance optimizations when using GATK3.8 and GATK4

Recommendations for performance optimizations when using GATK3.8 and GATK4

Complete microbial genomes for public health in Australia and Southwest Pacific

Advances in the Microbiome: Applications to Clostridium difficile Infection

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