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Related Concept Videos

Multi-species Conserved Sequences02:51

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Updated: Sep 25, 2025

Primer Extension Capture: Targeted Sequence Retrieval from Heavily Degraded DNA Sources
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Efficient privacy-preserving variable-length substring match for genome sequence.

Yoshiki Nakagawa1, Satsuya Ohata2, Kana Shimizu3,4

  • 1Department of Computer Science and Engineering, Waseda University, Tokyo, Japan.

Algorithms for Molecular Biology : AMB
|April 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for secure genome data sharing, enabling fast substring matching using FM-index and secret sharing. The privacy-preserving technology significantly accelerates genomic data analysis.

Keywords:
FM-indexLCP arrayMaximal exact matchPrivate genome sequence searchSecret sharingSecure multiparty computationSuffix array

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Data Security

Background:

  • Genome data sharing is crucial for research but faces privacy challenges.
  • Existing methods for searching genomic databases lack efficiency and privacy preservation.

Purpose of the Study:

  • To develop a privacy-preserving algorithm for secure substring matching in large genomic databases.
  • To improve the efficiency of genomic data searching while maintaining data confidentiality.

Main Methods:

  • Developed a secure table lookup algorithm utilizing FM-index and secret sharing.
  • Implemented a privacy-preserving technique for variable-length substring matching.
  • Evaluated the algorithm's performance on a human genome sequence.

Main Results:

  • The proposed algorithm achieves secure substring matching with complexities independent of table length after query input.
  • Experimental results show a speed improvement of at least three orders of magnitude compared to non-indexed searches.
  • The protocol demonstrated high efficiency in a realistic computational and network environment.

Conclusions:

  • The developed algorithm significantly enhances the speed and security of genomic data searching.
  • This privacy-preserving technology facilitates accelerated and secure genome data sharing for research.
  • The approach offers a scalable solution for analyzing large-scale genomic datasets.