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Related Concept Videos

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,...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
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%...
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...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...

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Related Experiment Video

Updated: May 9, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

SNVHMM: predicting single nucleotide variants from next generation sequencing.

Jiawen Bian, Chenglin Liu, Hongyan Wang

    BMC Bioinformatics
    |July 17, 2013
    PubMed
    Summary

    A new tool, SNVHMM, efficiently detects single nucleotide variants (SNVs) in cancer genomics data, even with low sequencing depth. This hidden Markov model (HMM) improves upon existing methods by incorporating read quality for accurate genotype inference.

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    Published on: October 18, 2013

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Next-generation sequencing (NGS) enables genomic exploration and cancer mutation detection.
    • NGS faces challenges in accurately detecting single nucleotide variants (SNVs) in low-coverage genomic regions.
    • Existing probabilistic methods struggle with low-depth sequencing data for SNV detection.

    Purpose of the Study:

    • To develop an improved computational tool for accurate SNV detection in low-depth NGS data.
    • To enhance SNV calling by leveraging read mapping and base quality information.
    • To provide a robust method for cancer genomics research with limited sequencing depth.

    Main Methods:

    • Developed SNVHMM, a tool based on a discrete hidden Markov model (HMM).
    • Integrated read mapping quality and base quality into the HMM's emission probability.
    • Utilized contextual information and confidence measures for genotype inference at each genomic position.

    Main Results:

    • SNVHMM demonstrated superior performance in SNV detection for low-depth sequencing data compared to Bayes-based methods.
    • The model achieved significant performance improvements over SNVMix2, especially at low sequencing depths.
    • SNVHMM outperformed SNVMix2 even when the latter was trained on large datasets.

    Conclusions:

    • SNVHMM efficiently detects SNVs in NGS cancer data, even with very low sequence depth.
    • The method requires minimal training data for effective SNV prediction.
    • SNVHMM incorporates base and mapping quality, offering user-selectable confidence levels for SNV prediction.