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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
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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Single Nucleotide Polymorphisms-SNPs01:05

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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,...
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Cancers Originate from Somatic Mutations in a Single Cell02:21

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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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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...
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Related Experiment Video

Updated: Aug 27, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes.

Teng Gao1, Ruslan Soldatov1, Hirak Sarkar1

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Nature Biotechnology
|September 27, 2022
PubMed
Summary

Numbat, a new computational method, enhances the detection of copy number variations in single-cell RNA sequencing data. It reconstructs tumor profiles and identifies malignant cells, aiding cancer research.

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Characterizing Mutational Load and Clonal Composition of Human Blood
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Area of Science:

  • Genomics and Computational Biology
  • Cancer Research and Molecular Oncology

Background:

  • Genome instability and altered transcriptional programs are key drivers of cancer.
  • Single-cell RNA sequencing (scRNA-seq) offers a powerful approach to study tumor heterogeneity from both genetic and non-genetic sources.

Purpose of the Study:

  • To introduce Numbat, a novel computational method for enhancing copy number variation detection in scRNA-seq data.
  • To leverage haplotype information, allele, and expression signals for improved accuracy in inferring single-cell copy number profiles and tumor clonal phylogeny.

Main Methods:

  • Numbat integrates population-based phasing, allele signals, and expression data from scRNA-seq.
  • The method iteratively infers single-cell copy number profiles and tumor clonal phylogeny by exploiting evolutionary relationships between subclones.
  • Numbat does not require matched DNA data or prior genotyping.

Main Results:

  • Numbat successfully reconstructed tumor copy number profiles across 22 diverse cancer samples (multiple myeloma, gastric, breast, thyroid).
  • The method accurately identified malignant cells within the tumor microenvironment.
  • Genetic subpopulations with transcriptional signatures linked to tumor progression and therapy resistance were identified.

Conclusions:

  • Numbat is an effective computational tool for precise copy number variation analysis in scRNA-seq data.
  • The method facilitates the identification of distinct cancer subpopulations and their evolutionary trajectories.
  • Numbat's applicability across various cancer types and experimental settings makes it a valuable resource for cancer research.