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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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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....
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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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Single Nucleotide Polymorphisms-SNPs01:05

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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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Related Experiment Video

Updated: Mar 12, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.

Jean-François Spinella1, Pamela Mehanna1, Ramon Vidal1

  • 1CHU Sainte-Justine Research Center, Université de Montréal, Montreal, QC, Canada.

BMC Genomics
|November 16, 2016
PubMed
Summary

SNooPer, a machine learning tool, accurately calls somatic variants from low-depth cancer sequencing data. This approach maintains high specificity and sensitivity, outperforming existing methods and reducing sequencing costs.

Keywords:
Low-pass next-generation sequencingMachine learningRandom ForestSomatic variant

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Next-generation sequencing (NGS) enables deep cancer genome analysis, but accurate somatic variant calling remains challenging due to varying caller performance and low concordance.
  • Existing statistical methods struggle with sub-optimal data like low-pass sequencing, low coverage, or low variant allele frequency (VAF).
  • Technical sequencing and alignment issues further complicate accurate somatic variant identification in complex cancer genomes.

Purpose of the Study:

  • To develop a versatile machine learning approach, SNooPer, for accurate somatic variant calling in low-depth sequencing data.
  • To demonstrate SNooPer's effectiveness in handling low coverage and low VAFs, thereby reducing sequencing costs while maintaining high accuracy.
  • To provide a robust alternative to existing callers that are sensitive to data quality.

Main Methods:

  • Developed SNooPer, a machine learning tool utilizing Random Forest classification models.
  • Trained data-specific models using a subset of known true variations and sequencing errors from the sequencing output.
  • Applied SNooPer to a dataset of 40 childhood acute lymphoblastic leukemia patients with low-depth sequencing data.

Main Results:

  • SNooPer accurately calls somatic variants in low-depth sequencing data, unaffected by low coverage or low VAFs.
  • The algorithm demonstrated superior overall performance compared to three benchmarked somatic callers.
  • SNooPer effectively reduces overall sequencing costs while maintaining high specificity and sensitivity.

Conclusions:

  • SNooPer offers a flexible and robust solution for somatic variant calling, particularly in challenging low-depth sequencing scenarios.
  • Its machine learning approach, based on Random Forest, mitigates technical bias and systematic errors without requiring user-defined parameters.
  • The SNooPer tool and user guide are publicly available for broader application in cancer genomics research.