Ultrafast and scalable variant annotation and prioritization with big functional genomics data

Area of Science:

• Genomics
• Bioinformatics

Background:

• Large-scale genomics studies generate vast amounts of functional annotation data and sequencing variants.
• Existing variant annotation algorithms struggle with the efficiency and scalability required for big genomic data, especially whole-genome sequencing variants.

Purpose of the Study:

• To develop a novel algorithm, VarNote, for rapid and scalable annotation of genome-scale variants.
• To enhance the processing of large and complex functional annotation resources.

Main Methods:

• Development of VarNote featuring a novel index system and a parallel random-sweep searching algorithm.
• Implementation of region-based and allele-specific annotation capabilities.
• Integration of base-wise and context-dependent annotations.

Main Results:

• VarNote demonstrates substantial performance improvements (two to three orders of magnitude) over existing algorithms.
• The tool supports flexible extraction of annotations.
• Development of three pipelines for prioritizing causal regulatory variants for various diseases.

Conclusions:

• VarNote provides an efficient and scalable solution for annotating genome-scale variants in large functional annotation resources.
• The developed pipelines facilitate the prioritization of causal regulatory variants for common diseases, Mendelian disorders, and cancers.