OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing enables variant discovery for research and disease studies.
• Variant calling, a computationally intensive process, typically uses complex software chains like the Genome Analysis Toolkit (GATK).
• Current GATK Best Practices primarily focus on human data and lack adaptability to high-throughput sequencing advancements, hindering reproducibility.

Purpose of the Study:

• To develop an automated, reproducible, and computationally optimized workflow for variant calling.
• To address the limitations of existing variant calling recommendations for diverse organisms and high-throughput sequencing.
• To enhance the efficiency and accessibility of genomic sequence annotation.

Main Methods:

• Developed an open-source workflow (OVarFlow) building upon GATK Best Practices.
• Implemented parallelized data evaluation and optimized GATK application performance.
• Tuned Java garbage collection and heap size for key GATK tools (SortSam, MarkDuplicates, HaplotypeCaller, GatherVcfs).

Main Results:

• Achieved automated detection of single nucleotide polymorphisms and insertion-deletions for model and non-model organisms.
• Reduced overall analysis time by 50% through optimized computational tasks.
• Created a generalized computational architecture enabling reproducibility and parallel processing.

Conclusions:

• The developed workflow (OVarFlow) provides automation and reproducibility for variant calling.
• Optimized computational processing reduces resource demands, lowering entry barriers to variant calling.
• Enables standardized variant calling across diverse genomic applications.