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Hash functions in nucleotide sequence analysis.

Ke Chen1, Xiang Li1, Qian Shi1

  • 1Department of Computer Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.

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Summary
This summary is machine-generated.

Hash functions are essential for randomizing nucleotide sequences in bioinformatics. This survey categorizes hash functions, aiding bioinformatic method developers in selecting appropriate tools for nucleotide sequence analysis.

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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.