Hash functions in nucleotide sequence analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Randomization is crucial for algorithms and data structures in nucleotide sequence analysis.
• Hash functions are widely used to randomize nucleotide sequences in genomics.
• A comprehensive review of hash function types and applications in this domain is lacking.

Purpose of the Study:

• To provide a comprehensive survey of hash functions used in nucleotide sequence analysis.
• To categorize hash functions into four main types for bioinformatic method developers.
• To highlight the properties, applications, and differences of various hash functions.

Main Methods:

• Categorization of hash functions into four types: scattering, permutations, minimum perfect, and locality-sensitive.
• Inclusion of both general-use and specialized hash functions for nucleotide sequence analysis.
• Analysis of salient properties, commonalities, differences, and application areas for each category.

Main Results:

• Hash functions are classified into scattering, permutations, minimum perfect, and locality-sensitive categories.
• Examples of both general and specialized hash functions are provided for nucleotide sequence analysis.
• Key characteristics and applications of each hash function category are detailed.

Conclusions:

• This survey offers a structured overview of hash functions for nucleotide sequence analysis.
• It serves as a valuable resource for bioinformatic method developers.
• Understanding hash function categories aids in selecting optimal tools for genomic data analysis.