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Related Concept Videos

Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Related Experiment Video

Updated: Jun 9, 2026

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

A fast algorithm for exact sequence search in biological sequences using polyphase decomposition.

Abhilash Srikantha1, Ajit S Bopardikar, Kalyan Kumar Kaipa

  • 1Samsung Advanced Institute of Technology, Bangalore, Karnataka, India. s.abhilash@samsung.com

Bioinformatics (Oxford, England)
|September 9, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient algorithm for fast DNA sequence searching in large databases. By using Q-gram hash tables and downsampling, it significantly improves search speed and memory efficiency for genomic data analysis.

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Last Updated: Jun 9, 2026

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

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Exact sequence search is crucial for DNA subsequence identification in fields like pharmacogenetics, phylogenetics, and personal genomics.
  • The exponential growth of genomic data necessitates faster and more scalable search algorithms.
  • Existing algorithms often struggle with large datasets due to high computational costs.

Purpose of the Study:

  • To present an efficient algorithm for rapid DNA sequence searching within large biological sequences and databases.
  • To address the scalability limitations of current sequence search methods.
  • To optimize both search speed and memory utilization for massive genomic datasets.

Main Methods:

  • The algorithm employs hash tables of Q-grams, constructed from a downsampled database.
  • Beam pruning techniques are utilized to reduce the time complexity of pattern searching.
  • Theoretical complexity calculations and performance benchmarks are used to evaluate the algorithm's efficiency.

Main Results:

  • The proposed algorithm demonstrates efficient search capabilities on large DNA sequences.
  • The use of Q-gram hash tables and downsampling leads to improved memory usage.
  • Beam pruning effectively reduces the computational cost of pattern searches.

Conclusions:

  • The developed algorithm offers a significant improvement in speed and memory efficiency for large-scale DNA sequence searching.
  • This method provides a valuable tool for analyzing the increasing volume of genomic data.
  • The algorithm shows strong potential for applications in various areas of genomics and bioinformatics.