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MSuPDA: A Memory Efficient Algorithm for Sequence Alignment.

Mohammad Ibrahim Khan1, Md Sarwar Kamal1, Linkon Chowdhury2

  • 1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Cuet Road, Chittagong, 4349, Bangladesh.

Interdisciplinary Sciences, Computational Life Sciences
|August 9, 2015
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Summary

This study introduces a novel method for DNA sequence alignment using pushdown automata (PDA) to significantly reduce memory usage. By employing anchor seeds and PDA stack operations, it efficiently frees memory during alignment.

Keywords:
Anchor seedMSuPDAPOPQuick splitting

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Computer Science

Background:

  • Space complexity is a critical challenge in DNA sequence alignment.
  • Existing methods often require substantial memory resources.
  • Pushdown automata offer a theoretical framework for memory-efficient computation.

Purpose of the Study:

  • To develop a memory-saving approach for local DNA sequence alignment.
  • To leverage pushdown automata for reducing computational space complexity.
  • To optimize memory utilization in bioinformatics algorithms.

Main Methods:

  • Selection of anchor seeds (AS) from nucleotide base pair datasets.
  • Utilizing quick splitting techniques to isolate anchor seeds.
  • Employing pushdown automata (PDA) with anchor seeds in the input unit and genome segments in the stack.
  • Matching anchor seeds with genome segments via stack POP operations controlled by the PDA.

Main Results:

  • The proposed method effectively reduces memory usage during DNA sequence alignment.
  • Pushdown automata operations successfully free memory cells as nucleotides are processed.
  • Anchor seed matching within the PDA framework demonstrates efficient space utilization.

Conclusions:

  • Pushdown automata provide a viable solution for memory efficiency in DNA sequence alignment.
  • The anchor seed-based approach with PDA significantly mitigates space complexity issues.
  • This method offers a promising direction for developing more memory-efficient bioinformatics tools.