Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Whole genome sequencing (WGS) is crucial for identifying genetic variants linked to human diseases and traits.
• Existing platforms lack comprehensive downstream analysis capabilities for WGS data.
• Efficient processing and annotation of large-scale WGS datasets remain a significant challenge.

Purpose of the Study:

• To develop novel algorithms and a robust parallel computing framework for WGS data analysis.
• To improve the efficiency and comprehensiveness of variant annotation and genotype analysis.
• To provide integrated tools for quality control, filtration, annotation, pathogenic prediction, and statistical testing.

Main Methods:

• Proposed three novel algorithms: sequence gap-filled gene feature annotation, bit-block encoded genotypes, and sectional fast access to text lines.
• Integrated these algorithms into a parallel computing framework named KGGSeq.
• Utilized WGS data from the 1000 Genomes Project for performance evaluation.

Main Results:

• KGGSeq annotated several thousand more reliable non-synonymous variants compared to widely used tools like ANNOVAR and SNPEff.
• KGGSeq processed genotypes for ~60 million variants across 2504 subjects in approximately 30 minutes on a small server, significantly faster than alternatives.
• The bit-block genotype format achieved over 1000x speedup in genotypic correlation calculations and reduced storage space by at least 98.5%.

Conclusions:

• KGGSeq provides a robust and efficient framework for comprehensive downstream analysis of whole genome sequencing data.
• The novel algorithms and data structures significantly improve variant annotation accuracy and genotype processing speed.
• KGGSeq offers a valuable tool for accelerating genetic research in human diseases and traits.