ArticleScientific reports2017
Accelerating next generation sequencing data analysis with system level optimizations.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 30 citations in OpenAlex.
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- Fifteen Years of the Genome Analysis Toolkit as the De Facto Standard in Short-Read Variant Calling.International journal of molecular sciences · 2026Review
- Cold Plasma Treatment Facilitated the Conversion of Lignin-Derived Aldehyde for Pseudomonas putida.Applied biochemistry and biotechnology · 2025Article
- A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.BMC biology · 2024Article
- Optimized splitting of mixed-species RNA sequencing data.Journal of bioinformatics and computational biology · 2022Article
- Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders.Current issues in molecular biology · 2022Review
- OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow.BMC bioinformatics · 2021Article
- Recommendations for performance optimizations when using GATK3.8 and GATK4.BMC bioinformatics · 2019Article
- Sentieon DNASeq Variant Calling Workflow Demonstrates Strong Computational Performance and Accuracy.Frontiers in genetics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next generation sequencing (NGS) data analysis is highly compute intensive. In-memory computing, vectorization, bulk data transfer, CPU frequency scaling are some of the hardware features in the modern computing architectures. To get the best execution time and utilize these hardware features, it is necessary to tune the system level parameters before running the application. We studied the GATK-HaplotypeCaller which is part of common NGS workflows, that consume more than 43% of the total execution time. Multiple GATK 3.x versions were benchmarked and the execution time of HaplotypeCaller was optimized by various system level parameters which included: (i) tuning the parallel garbage collection and kernel shared memory to simulate in-memory computing, (ii) architecture-specific tuning in the PairHMM library for vectorization, (iii) including Java 1.8 features through GATK source code compilation and building a runtime environment for parallel sorting and bulk data transfer (iv) the default 'on-demand' mode of CPU frequency is over-clocked by using 'performance-mode' to accelerate the Java multi-threads. As a result, the HaplotypeCaller execution time was reduced by 82.66% in GATK 3.3 and 42.61% in GATK 3.7. Overall, the execution time of NGS pipeline was reduced to 70.60% and 34.14% for GATK 3.3 and GATK 3.7 respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.