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Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework.

Tanveer Ahmad1, Nauman Ahmed2, Zaid Al-Ars3

  • 1Accelerated Big Data Systems Group, Quantum & Computer Engineering Department, Delft University of Technology, Delft, The Netherlands. t.ahmad@tudelft.nl.

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Summary
This summary is machine-generated.

This study enhances genomics data processing by using Apache Arrow for in-memory analytics, significantly speeding up variant calling workflows for whole genome sequencing (WGS) and whole exome sequencing (WES) data. The new method improves system resource utilization and outperforms existing techniques.

Keywords:
Apache ArrowBig DataGATK Best PracticesGenomicsIn-Memory DataWhole Genome/Exome Sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data requiring efficient processing.
  • Traditional disk-based workflows incur significant I/O overheads, slowing down analysis.
  • Advancements in memory technologies enable direct in-memory data processing to overcome I/O bottlenecks.

Purpose of the Study:

  • To integrate Apache Arrow's columnar in-memory format into genomics data processing.
  • To enable efficient in-memory communication between genomics applications, avoiding disk I/O and serialization overheads.
  • To improve the performance of high-throughput genomics data analysis workflows.

Main Methods:

  • Integrated Apache Arrow in-memory format and shared memory objects into BWA-MEM, Picard, and GATK.
  • Enabled in-memory communication between these genomics tools.
  • Compared performance against existing techniques like ramDisk and Unix pipes.

Main Results:

  • Achieved significant speedups in variant calling workflows: 4.85x for whole genome sequencing (WGS) and 4.76x for whole exome sequencing (WES) data.
  • Demonstrated improved system resource utilization, cache locality, and parallel scalability.
  • Observed even greater speedups in individual tools like sorting and duplicate removal.

Conclusions:

  • Apache Arrow-based in-memory processing offers a substantial performance improvement for genomics data analysis.
  • This approach effectively reduces I/O bottlenecks and enhances computational efficiency.
  • The method provides a scalable and efficient solution for handling large-scale genomics datasets.