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A survey on the algorithm and development of multiple sequence alignment.

Yongqing Zhang1,2, Qiang Zhang1, Jiliu Zhou1

  • 1School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China.

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Summary
This summary is machine-generated.

Multiple sequence alignment (MSA) is crucial for uncovering biological sequence information like function and evolution. This survey details MSA algorithms and applications, addressing challenges posed by increasing data scale and accuracy demands.

Keywords:
heuristiciterative algorithmmultiple sequence alignmentprogressive alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple Sequence Alignment (MSA) is fundamental in bioinformatics for analyzing biological sequences.
  • MSA reveals insights into sequence function, evolution, and structure.
  • Increasing sequence data and accuracy demands present challenges for current MSA methods.

Purpose of the Study:

  • To provide a comprehensive survey of Multiple Sequence Alignment (MSA) algorithms and their applications in bioinformatics.
  • To systematically introduce MSA knowledge, including background, databases, metrics, and benchmarks.
  • To categorize and analyze classical and state-of-the-art MSA algorithms.

Main Methods:

  • Systematic review and categorization of MSA algorithms.
  • Analysis of MSA applications across various bioinformatics domains.
  • Discussion of challenges and future opportunities in MSA development.

Main Results:

  • Detailed overview of MSA knowledge components (background, database, metric, benchmark).
  • Enumeration of common MSA applications: database searching, phylogenetic, genomic, metagenomic, and protein analysis.
  • Categorization of algorithms: progressive alignment, iterative, heuristics, machine learning, and divide-and-conquer.

Conclusions:

  • The study offers a structured perspective on MSA, its algorithms, and applications.
  • It highlights the importance of efficient and accurate MSA strategies for modern bioinformatics.
  • Provides valuable insights for researchers contributing to MSA and related fields.