pyAmpli: an amplicon-based variant filter pipeline for targeted resequencing data.

Area of Science:

• Genomics
• Bioinformatics

Background:

• Haloplex targeted resequencing is widely used for analyzing germline and somatic variants in gene panels.
• Wet-lab procedures in Haloplex can introduce false positives, complicating data analysis.
• A comprehensive filtering strategy for amplicon enrichment errors is currently lacking.

Purpose of the Study:

• To introduce pyAmpli, a novel software package for filtering variants in Haloplex data.
• To address systematic errors arising from amplicon enrichment during targeted resequencing.
• To improve the accuracy of variant calling in clinical gene panel analysis.

Main Methods:

• Developed pyAmpli, a platform-independent, parallelized Python package.
• Implemented an amplicon-based variant filtering strategy for Haloplex data.
• Enabled filtering of variants based on user-defined criteria for systematic errors.

Main Results:

• pyAmpli effectively filters variants, significantly increasing specificity.
• The tool maintains high sensitivity, crucial for detecting true positive mutations.
• Demonstrated improved accuracy in identifying clinically relevant mutations from targeted resequencing data.

Conclusions:

• pyAmpli is an accessible software tool that enhances the true positive variant call rate.
• The package specifically mitigates errors associated with PCR-based enrichment in targeted regions.
• Facilitates more reliable variant reporting in clinical genomics.