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Related Concept Videos

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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Related Experiment Video

Updated: Oct 4, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Published on: June 23, 2012

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Fast, low-memory detection and localization of large, polymorphic inversions from SNPs.

Ronald J Nowling1, Fabian Fallas-Moya2, Amir Sadovnik2

  • 1Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, Wisconsin, United States of America.

Peerj
|February 4, 2022
PubMed
Summary

This study introduces feature hashing to analyze large genomic inversions using single nucleotide polymorphism (SNP) data, significantly reducing memory usage for population genetics research.

Keywords:
Chromosomal inversionsFeature hashingPrincipal component analysisSingle nucleotide polymorphisms

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Large polymorphic inversions impact population structure and genotype maintenance.
  • Single nucleotide polymorphism (SNP) data and principal component analysis (PCA) are used for inversion detection.
  • Analyzing large SNP datasets requires substantial memory, limiting study scope.

Purpose of the Study:

  • To develop a memory-efficient method for analyzing large genomic inversions from SNP data.
  • To enable the analysis of larger datasets on standard computing hardware.

Main Methods:

  • Feature hashing is employed to construct a feature matrix from VCF files containing SNPs.
  • A streaming approach is utilized, avoiding the need to load the entire VCF file into memory.

Main Results:

  • Memory usage was reduced by 97% in evaluations on Anopheles and Drosophila datasets.
  • Inversion detection and localization accuracy remained high with minimal reduction.

Conclusions:

  • The proposed method allows for efficient analysis of large inversion datasets on common computers.
  • The open-source software Asaph implements this approach for public use.