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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.4K
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%...
18.4K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.7K
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,...
17.7K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.1K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.1K

You might also read

Related Articles

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

Sort by
Same author

Fibroblast growth factor receptor inhibition for succinate dehydrogenase-deficient gastrointestinal stromal tumors: a phase 2 trial.

Nature medicine·2026
Same author

Multiplexed measurements of protein-protein interactions and protein abundance across cellular conditions using Prod&PQ-seq.

bioRxiv : the preprint server for biology·2026
Same author

An expanded registry of candidate cis-regulatory elements.

Nature·2026
Same author

The <i>9p21.3</i> Coronary Artery Disease Risk Locus Modulates Vascular Cell-State Transitions via Enhancer-Driven Regulation of <i>MTAP</i>.

bioRxiv : the preprint server for biology·2025
Same author

ID2 Suppresses Multiple Myeloma Cell Proliferation by Repressing the Activity of the Transcription Factor TCF3.

Blood cancer discovery·2025
Same author

The Somatic Mosaicism across Human Tissues Network.

Nature·2025

Related Experiment Video

Updated: Dec 13, 2025

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

15.6K

Detecting sample swaps in diverse NGS data types using linkage disequilibrium.

Nauman Javed1,2, Yossi Farjoun2, Tim J Fennell2

  • 1Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.

Nature Communications
|July 31, 2020
PubMed
Summary
This summary is machine-generated.

Genomics sample mislabeling is a major problem. CrosscheckFingerprints (Crosscheck) is a new tool that detects incorrectly paired sequencing datasets, improving data accuracy for researchers.

More Related Videos

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.4K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Related Experiment Videos

Last Updated: Dec 13, 2025

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

15.6K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.4K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The rapid expansion of genomics datasets presents challenges in data integrity.
  • Sample mislabeling in genomics studies can lead to erroneous conclusions and wasted resources.
  • Accurate sample attribution is critical for reproducible and reliable scientific findings.

Purpose of the Study:

  • To introduce CrosscheckFingerprints (Crosscheck), a novel computational tool.
  • To quantify sample-relatedness and detect incorrectly paired sequencing datasets from different donors.
  • To provide a robust method for identifying sample mix-ups in large-scale genomics projects.

Main Methods:

  • Development of CrosscheckFingerprints (Crosscheck) algorithm for quantifying sample relatedness.
  • Utilizing sequence data to generate unique sample identifiers (fingerprints).
  • Comparison of Crosscheck performance against existing methods for sample mislabeling detection.

Main Results:

  • Crosscheck demonstrates superior performance compared to existing methods for detecting sample mislabeling.
  • The tool is effective even with sparse data or data from different experimental assays.
  • Application to 8851 ENCODE datasets identified and corrected dozens of mislabeled samples, approximately 1% of the data.

Conclusions:

  • CrosscheckFingerprints is a highly effective tool for ensuring the accuracy of genomics datasets.
  • The tool can identify and correct sample mislabeling and ambiguous metadata, enhancing data quality.
  • Implementing Crosscheck is crucial for maintaining the integrity of large genomics databases and research findings.