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The ITS2 Database
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Multiple Sequence Alignment Based on a Suffix Tree and Center-Star Strategy: A Linear Method for Multiple Nucleotide

Wenhe Su1, Xiangke Liao1, Yutong Lu1

  • 11 School of Computer Science and Technology, National University of Defense Technology , Changsha, China .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 9, 2017
PubMed
Summary
This summary is machine-generated.

We developed a new Multiple Sequence Alignment (MSA) method, MASC, using a center-star strategy and suffix trees. MASC achieves linear time complexity and significantly outperforms existing tools in speed for large datasets.

Keywords:
Sparkcenter-star strategymultiple sequence alignmentsuffix tree

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple Sequence Alignment (MSA) is crucial for analyzing biological sequence data.
  • Existing MSA methods face challenges with large datasets and computational complexity.

Purpose of the Study:

  • To develop a faster and scalable MSA algorithm.
  • To reduce MSA complexity by leveraging pairwise alignments and suffix trees.

Main Methods:

  • Implemented a center-star strategy to decompose MSA into pairwise alignments.
  • Utilized suffix trees for efficient matching of identical substrings between sequences.
  • Developed the Multiple Sequence Alignment based on a Suffix tree and Center-star strategy (MASC) algorithm.
  • Integrated MASC with the Spark-distributed parallel framework for big data processing.

Main Results:

  • MASC achieves linear time complexity O(mn) for MSA.
  • The method demonstrates significantly improved speed compared to existing tools like MAFFT.
  • MASC aligned 67,200 sequences (>10,000 bps) in 9 minutes, versus MAFFT's >3.5 days.
  • No loss in accuracy was observed for highly similar nucleotide sequences.

Conclusions:

  • MASC offers a highly efficient and scalable solution for Multiple Sequence Alignment.
  • The algorithm is particularly effective for large-scale genomic and sequence analysis.
  • MASC represents a significant advancement in computational biology tools for evolutionary analysis.