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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.

Jiarui Ding1, Ali Bashashati, Andrew Roth

  • 1Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada.

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

Accurate identification of somatic mutations from next-generation sequencing (NGS) data is crucial for cancer genomics. Machine learning methods significantly improve somatic single nucleotide variant (SNV) prediction accuracy in tumor/normal sequencing data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) is standard for cancer genome profiling.
  • Existing bioinformatics tools for somatic mutation detection have high false prediction rates.
  • Accurate somatic mutation inference from paired tumor/normal NGS data remains a challenge.

Purpose of the Study:

  • To compare supervised machine learning algorithms for somatic single nucleotide variant (SNV) prediction.
  • To evaluate the accuracy of random forest, Bayesian additive regression tree, support vector machine, and logistic regression models.
  • To identify improved computational approaches for somatic mutation detection in cancer genomes.

Main Methods:

  • Developed 106 features for 3369 candidate somatic SNVs from 48 breast cancer genomes.
  • Trained and evaluated four machine learning classifiers using cross-validation and hold-out test data.
  • Utilized both exome capture and whole genome shotgun sequencing platforms for data generation.

Main Results:

  • Supervised machine learning algorithms with feature selection significantly improved somatic SNV prediction accuracy.
  • All evaluated learning algorithms demonstrated statistically significant improvements over standard methods.
  • Unsupervised clustering revealed distinct classes of false positive predictions, suggesting sources of technical artifacts.

Conclusions:

  • Machine learning approaches enhance the accuracy of somatic mutation detection in cancer NGS data.
  • Feature selection is critical for improving the performance of predictive models.
  • Further research into technical artifacts can refine future somatic mutation calling pipelines.