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DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
08:04

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Published on: October 8, 2019

A bit-parallel dynamic programming algorithm suitable for DNA sequence alignment.

Kouichi Kimura1, Asako Koike, Kenta Nakai

  • 1Central Research Laboratory, Hitachi Ltd., 1-280 Higashi-Koigakubo, Kokubunji Tokyo, 185-8601, Japan. kouichi.kimura.hh@hitachi.com

Journal of Bioinformatics and Computational Biology
|July 20, 2012
PubMed
Summary
This summary is machine-generated.

This study modifies Myers' algorithm for approximate string matching, enabling longer queries by limiting mismatches instead of query length. This enhances DNA sequence analysis and seed alignment for high-throughput sequencing data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Myers' bit-parallel dynamic programming algorithm is efficient for approximate string matching but limited by query length (typically 64 characters).
  • Approximate string matching is crucial for analyzing biological sequences, especially with the advent of high-throughput sequencing.

Purpose of the Study:

  • To modify Myers' algorithm to overcome the query length restriction.
  • To develop a more flexible approximate string matching algorithm suitable for biological sequence analysis.

Main Methods:

  • Modified Myers' algorithm with a restriction on the maximum number of mismatches (less than half the word size).
  • Analyzed the algorithm's time complexity as O(m log |Σ|), where m is query length and |Σ| is alphabet size.

Main Results:

  • The modified algorithm removes the strict query length limitation of the original Myers' algorithm.
  • Achieved a time complexity of O(m log |Σ|), efficient for small alphabets like DNA.
  • Demonstrated utility in extending seed alignments for high-throughput short-read DNA sequencing data.

Conclusions:

  • The modified algorithm offers a more adaptable approach to approximate string matching for biological sequences.
  • Particularly effective for DNA sequences and large-scale genomic analyses using next-generation sequencing data.