VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Next-generation sequencing generates large datasets, requiring efficient storage and analysis.
• Variant calling and analysis rely on high-quality sequencing reads.

Purpose of the Study:

• To develop a flexible C++ tool, VariantBam, for filtering and profiling sequencing data.
• To enable efficient data storage by removing irrelevant reads while preserving variant-supporting information.

Main Methods:

• VariantBam utilizes rules based on genomic intervals and variant sites.
• Implemented filters include alignment data, sequence motifs, regional coverage, and base quality.
• The tool processes BAM, CRAM, and SAM sequencing file formats.

Main Results:

• VariantBam achieved a median size reduction ratio of 3.1:1 on lung cancer whole genome BAM files.
• Filtering removed large tags and selected high-quality, variant-supporting reads and reads matching sequence motifs.
• The tool preserves essential information for downstream genomic analyses.

Conclusions:

• VariantBam offers an efficient solution for managing large sequencing datasets.
• The tool facilitates cost-effective data storage and streamlined downstream variant analysis.
• VariantBam enhances the usability of sequencing data for genomic research.