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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.9K
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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RNA-seq03:21

RNA-seq

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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...
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Related Experiment Video

Updated: Mar 8, 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

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DNA copy number profiling using single-cell sequencing.

Xuefeng Wang1, Hao Chen2, Nancy R Zhang3

  • 1H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Briefings in Bioinformatics
|February 5, 2017
PubMed
Summary
This summary is machine-generated.

A new toolkit called SCNV addresses the need for analyzing single-cell DNA sequencing data. It accurately detects copy number variations and profiles whole genomes, even with noisy, low-coverage data.

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell DNA sequencing data often suffers from low coverage and high technical noise.
  • Existing software for detecting copy number variations (CNVs) and constructing whole-genome copy number profiles from such data is limited.

Purpose of the Study:

  • To introduce SCNV, a novel software toolkit designed for accurate CNV detection and whole-genome copy number profiling from single-cell DNA sequencing data.
  • To provide a high-resolution solution for breakpoint detection and copy number calling in challenging single-cell genomics datasets.

Main Methods:

  • Developed an efficient bin-free segmentation approach for copy number analysis.
  • Implemented an auto-tuning parameter feature that utilizes normal cells from the same batch to adjust for technical noise.

Main Results:

  • SCNV offers the highest possible resolution for breakpoint detection and subsequent copy number calling.
  • The auto-tuning capability allows SCNV to adapt to varying noise levels across different datasets and platforms.

Conclusions:

  • SCNV provides a robust and high-resolution solution for analyzing copy number variations in noisy, low-coverage single-cell DNA sequencing data.
  • Its adaptive nature facilitates broader application across diverse single-cell genomics studies and platforms.