DoBSeqWF: a framework for sensitive detection of individual genetic variation in pooled sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Genetic Medicine

Background:

• Population screening for rare genetic diseases can improve early diagnosis and treatment.
• High costs of next-generation sequencing hinder widespread implementation.
• Double-batched sequencing (DoBSeq) is a cost-effective approach for rare variant detection using pooled sequencing data.

Purpose of the Study:

• To develop and validate a specialized bioinformatics workflow for analyzing complex DoBSeq data.
• To enable efficient, sensitive, and reproducible rare variant detection from pooled whole genome sequencing data.

Main Methods:

• Development of DoBSeqWF, a Nextflow-based pipeline for processing DoBSeq data from alignment to variant assignment.
• Creation of training and validation datasets using a childhood cancer cohort (200 individuals).
• Benchmarking of variant callers and implementation of machine learning filters for enhanced rare variant detection.

Main Results:

• DoBSeqWF demonstrated accurate and scalable rare variant detection in pooled sequencing data.
• Machine learning filters improved rare variant detection sensitivity.
• The workflow provides a robust method for analyzing complex DoBSeq data.

Conclusions:

• DoBSeqWF is an effective pipeline for rare variant detection using DoBSeq.
• This workflow supports the expansion of cost-effective genetic screening programs for rare diseases.