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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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Related Experiment Video

Updated: Jun 4, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Improving SNP discovery by base alignment quality.

Heng Li1

  • 1Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA. hengli@broadinstitute.org

Bioinformatics (Oxford, England)
|February 16, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces per-Base Alignment Quality (BAQ) to improve SNP discovery from sequencing data. BAQ reduces false SNP calls caused by alignment errors around insertions and deletions (indels).

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Related Experiment Videos

Last Updated: Jun 4, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Sequencing data analysis presents challenges in accurate SNP discovery.
  • Misalignments around insertions and deletions (indels) are a major source of false SNP calls.

Purpose of the Study:

  • To introduce a novel application of profile Hidden Markov Models for enhanced SNP discovery.
  • To reduce false SNP calls in resequencing data by addressing alignment inaccuracies.

Main Methods:

  • Application of profile Hidden Markov Models.
  • Development and implementation of per-Base Alignment Quality (BAQ).
  • Utilizing BAQ to assess the probability of read base misalignments.

Main Results:

  • Successfully reduced false SNP calls in resequencing data.
  • Demonstrated effectiveness of BAQ in improving SNP discovery accuracy.
  • Validated on large datasets by the 1000 Genomes Project analysis subgroup.

Conclusions:

  • Profile Hidden Markov Models offer a powerful approach for SNP discovery.
  • Per-Base Alignment Quality is an effective metric for improving alignment accuracy.
  • The proposed method significantly enhances the reliability of SNP discovery from resequencing data.