Manipulating base quality scores enables variant calling from bisulfite sequencing alignments using conventional...

Area of Science:

• Genomics
• Bioinformatics
• Epigenetics

Background:

• Bisulfite sequencing is crucial for DNA methylation analysis but complicates accurate single nucleotide polymorphism (SNP) variant calling.
• Conventional software struggles to distinguish true genetic variations from bisulfite-induced artifacts.
• SNP data is valuable for genotyping and understanding the methylome within genetic contexts.

Purpose of the Study:

• To develop a computational method for accurate SNP variant calling from bisulfite-converted sequencing data.
• To enable the use of conventional variant calling software for analyzing SNP data alongside methylation information.
• To improve the precision and sensitivity of variant detection in bisulfite sequencing experiments.

Main Methods:

• A computational pre-processing approach termed "double-masking" was developed.
• The method adapts sequence alignment data for per-strand analysis.
• Enables downstream analysis using standard variant callers like GATK and Freebayes.

Main Results:

• The "double-masking" method significantly improves precision and sensitivity compared to specialized tools.
• Performance was validated on high-quality benchmark datasets for human and plant variants.
• The approach effectively dissociates true polymorphisms from bisulfite-induced mutations.

Conclusions:

• The "double-masking" procedure is an open-source, user-friendly method for accurate SNP variant calling.
• It eliminates the need for specialized software and reduces experimental costs by avoiding separate conventional sequencing libraries.
• The method facilitates integrated analysis of genetic variation and DNA methylation.