KmerKeys: a web resource for searching indexed genome assemblies and variants

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• K-mers (short DNA sequences) are crucial for genome analysis, but their large scale presents computational challenges.
• Analyzing billions of k-mers in human genome assemblies requires significant computational resources.

Purpose of the Study:

• To develop a performant and scalable solution for analyzing large-scale k-mer data in genome assemblies.
• To introduce KmerKeys, a web application enabling rapid and flexible k-mer searching.

Main Methods:

• Developed a novel indexing data structure using a hash table optimized for short sequence keys.
• Implemented cache-friendly hash tables, memory mapping, and massive parallel processing for speed and efficiency.
• Enabled both exact and fuzzy sequence searches within genome assemblies.

Main Results:

• KmerKeys provides rapid query speeds for cloud-based genome assembly analysis.
• The system efficiently handles and searches large collections of human genome assembly information.
• Allows integration of variant databases and metadata, such as gnomAD, for enhanced analysis.

Conclusions:

• KmerKeys offers a scalable and efficient solution to the computational challenges of k-mer analysis in genomics.
• The application facilitates advanced genomic data analysis by enabling flexible and fast querying.
• KmerKeys supports the incorporation of diverse genomic information, paving the way for future sequencing analysis advancements.