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

19.2K
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%...
19.2K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

19.7K
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,...
19.7K
RNA-seq03:21

RNA-seq

12.4K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
12.4K
Sanger Sequencing01:57

Sanger Sequencing

777.9K
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...
777.9K

You might also read

Related Articles

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

Sort by
Same author

Somatic variant detection in normal tissues from single-cell sequencing data.

bioRxiv : the preprint server for biology·2026
Same author

Hospital Environment-Associated Sources of Mycobacterium abscessus Infection in Transplant Recipients.

JAMA network open·2026
Same author

Long-term follow-up: blinatumomab maintenance after allogeneic hematopoietic cell transplantation for B-lineage acute lymphoblastic leukemia.

Haematologica·2026
Same author

Temporal single-cell transcriptional dynamics of murine pancreatic islet remodeling during hyperglycaemia progression.

Molecular metabolism·2026
Same author

Charting spatial ligand-target activity using Renoir.

Nature communications·2026
Same author

Multi-component, multi-target actions of Wuzi Yanzong Pill in male infertility: An evidence map linking clinical signals to mechanistic networks.

Journal of ethnopharmacology·2026

Related Experiment Video

Updated: Mar 22, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

12.2K

Monovar: single-nucleotide variant detection in single cells.

Hamim Zafar1,2, Yong Wang3, Luay Nakhleh1

  • 1Department of Computer Science, Rice University, Houston, Texas, USA.

Nature Methods
|April 19, 2016
PubMed
Summary

Monovar is a new statistical method for analyzing single-cell DNA sequencing data. It accurately detects genetic variants, outperforming existing tools in tumor analysis.

More Related Videos

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

1.4K
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

15.8K

Related Experiment Videos

Last Updated: Mar 22, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

12.2K
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

1.4K
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

15.8K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Standard variant callers struggle with single-cell DNA sequencing challenges.
  • Allelic dropout, false positives, and uneven coverage complicate variant detection.

Purpose of the Study:

  • To develop a robust statistical method for variant detection in single-cell DNA data.
  • To address the limitations of current variant callers in single-cell genomics.

Main Methods:

  • Developed Monovar, a novel statistical approach for variant calling.
  • Applied Monovar to single-cell DNA sequencing datasets.

Main Results:

  • Monovar demonstrated superior performance compared to standard algorithms.
  • Successfully identified driver mutations and mapped clonal substructure in human tumors.

Conclusions:

  • Monovar is a suitable and effective tool for single-nucleotide variant detection in single-cell genomics.
  • The method aids in understanding tumor heterogeneity and evolution.