Year: 2026
Type: preprint
Abstract: Randomness is a powerful tool in the design and analysis of algorithms and data structures for nucleotide sequence data. Nucleotide sequences are not themselves random but are often randomized using hash functions. Despite their widespread use in genomics, there is no comprehensive review of the types of hash functions used and their various applications. In this survey intended for bioinformatic methods developers, we divide hash functions into four categories: scattering hash functions, permutations, minimum perfect hash functions, and locality-sensitive hash functions. For each category, we provide examples of both general-use hash functions that have been applied in nucleotide sequence analysis and hash functions that have been designed specifically for nucleotide sequence analysis. We highlight their salient properties, commonalities, differences, and application areas.
Source: Genome Research
Authors: Ke Chen, Xiang Li, Qian Shi, Mingfu Shao, Paul Medvedev
Institution: Pennsylvania State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: Frameworks: Re-Engineering Galaxy for Performance, Scalability and Energy Efficiency, CAREER: Algorithms and Tools for Allele-Specific Transcript Assembly, A k-mer-based search engine for sequencing databases, Leveraging k-mer sketching statistics to enhance metagenomic methods and alignment algorithms, Computational Methods for Assembling Multiple RNA-seq Samples
OpenAlex ID: W7160103684