Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

12.3K
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...
12.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

PHYFUM: Phylogenetic Reconstruction of Normal and Pre-malignant Tissue Evolution Using Fluctuating Methylation.

bioRxiv : the preprint server for biology·2026
Same author

Disease-specific U1 spliceosomal RNA mutations in mature B-cell neoplasms.

Leukemia·2025
Same author

Continuous Risk Assessment of Late and Term Preeclampsia Throughout Pregnancy: A Retrospective Cohort Study.

Medicina (Kaunas, Lithuania)·2025
Same author

Large B-cell lymphomas with CCND1 rearrangement have different immunoglobulin gene breakpoints and genomic profile than mantle cell lymphoma.

Blood cancer journal·2024
Same author

Featuring BRCA1 and BRCA2 germline mutational landscape from Asturias (North Spain).

Clinical genetics·2024
Same author

Backtracking NOM1::ETV6 fusion to neonatal pathogenesis of t(7;12) (q36;p13) infant AML.

Leukemia·2024

Related Experiment Video

Updated: Jul 28, 2025

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

19.5K

RFcaller: a machine learning approach combined with read-level features to detect somatic mutations.

Ander Díaz-Navarro1, Pablo Bousquets-Muñoz1, Ferran Nadeu2,3

  • 1Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, 33006 Oviedo, Spain.

NAR Genomics and Bioinformatics
|June 1, 2023
PubMed
Summary

RFcaller is a machine learning pipeline for accurate somatic mutation detection in cancer. It efficiently identifies driver gene mutations missed by other methods, aiding clinical diagnosis.

More Related Videos

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

24.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Jul 28, 2025

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

19.5K
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

24.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning in Oncology

Background:

  • Decreasing sequencing costs and comprehensive cancer genomics are driving wider adoption of tumor sequencing in research and clinics.
  • Existing somatic mutation identification pipelines show variable results, necessitating multi-caller approaches that are computationally intensive and imperfect.
  • Expert review of mutation calls is crucial for clinical applications but is time-consuming, highlighting the need for automated, accurate solutions.

Purpose of the Study:

  • To introduce RFcaller, a machine learning-based pipeline for somatic mutation detection in tumor-normal paired samples.
  • To provide an efficient and accurate alternative to existing, resource-intensive mutation callers.
  • To improve the reliability of somatic mutation identification for research and clinical use.

Main Methods:

  • Development of RFcaller, a pipeline utilizing machine learning algorithms for somatic mutation detection.
  • Application of RFcaller to tumor-normal paired samples for identifying substitutions and insertions/deletions (indels).
  • Validation of RFcaller's performance against deep sequencing and Sanger sequencing.

Main Results:

  • RFcaller demonstrates high accuracy in detecting substitutions and indels from whole genome and exome data.
  • The pipeline successfully identifies mutations in driver genes that are often missed by other methods.
  • RFcaller operates without requiring substantial computational resources.

Conclusions:

  • RFcaller offers a computationally efficient and accurate machine learning approach for somatic mutation detection.
  • The pipeline enhances the ability to identify critical driver gene mutations, supporting clinical diagnosis.
  • RFcaller represents a significant advancement in the reliable genomic characterization of cancers.