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Background <p>Whole-exome sequencing (WES) is commonly used for identifying single nucleotide polymorphisms (SNPs) in coding regions of the human genome and has a wide range of clinical applications. As an intensive time-consuming task, automation is key to uncomplicate enabling straightforward data analysis.</p> Methods <p>The whole exome analysis pipeline (WEAP) workflow starts with the alignment of FASTQ files to a reference genome, variant calling, and annotation without user intervention. WEAP utilizes the genome analysis toolkit (GATK) workflow, which incorporates popular NGS analysis tools such as bwa-mem2, samtools, GATK, bcftools, and annovars coupled with the GNU parallel.</p> Results <p>WEAP successfully identified and annotated germline and somatic variants. The major steps of aligning to the reference genome, converting files, and removing duplicates in germline variant discovery were performed severalfold (1.5- to 3.6-fold for 4 samples) faster in parallel mode than in serial mode. In tumor analysis, creating a PoN from 40 samples was approximately 3 times faster in parallel mode. Tumor-only analysis was 1.4 to 7.7 times faster in each step. When comparing tumor tissues with matched normal tissues, the time taken was significantly reduced, accelerating the process from 1.8 to 3.6 times.</p> Discussion <p>WEAP enables perform flawless variant calling from WES data in an automated fashion. WEAP uses gnu parallel for multiple sample processing one at a time leveraging native parallel processing of the implemented tools and software to perform the analysis faster. A comparison between the parallel mode and serial mode of the pipeline revealed that WEAP can be one of the best alternative tools for end-to-end analysis of WES data integrating the gold standard GATK best practices workflow.</p>

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WEAP: an automated and accelerated pipeline for analyzing large scale whole exome sequencing data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Ranjan Jyoti Sarma,
  • Nachimuthu Senthil Kumar

摘要

Background

Whole-exome sequencing (WES) is commonly used for identifying single nucleotide polymorphisms (SNPs) in coding regions of the human genome and has a wide range of clinical applications. As an intensive time-consuming task, automation is key to uncomplicate enabling straightforward data analysis.

Methods

The whole exome analysis pipeline (WEAP) workflow starts with the alignment of FASTQ files to a reference genome, variant calling, and annotation without user intervention. WEAP utilizes the genome analysis toolkit (GATK) workflow, which incorporates popular NGS analysis tools such as bwa-mem2, samtools, GATK, bcftools, and annovars coupled with the GNU parallel.

Results

WEAP successfully identified and annotated germline and somatic variants. The major steps of aligning to the reference genome, converting files, and removing duplicates in germline variant discovery were performed severalfold (1.5- to 3.6-fold for 4 samples) faster in parallel mode than in serial mode. In tumor analysis, creating a PoN from 40 samples was approximately 3 times faster in parallel mode. Tumor-only analysis was 1.4 to 7.7 times faster in each step. When comparing tumor tissues with matched normal tissues, the time taken was significantly reduced, accelerating the process from 1.8 to 3.6 times.

Discussion

WEAP enables perform flawless variant calling from WES data in an automated fashion. WEAP uses gnu parallel for multiple sample processing one at a time leveraging native parallel processing of the implemented tools and software to perform the analysis faster. A comparison between the parallel mode and serial mode of the pipeline revealed that WEAP can be one of the best alternative tools for end-to-end analysis of WES data integrating the gold standard GATK best practices workflow.