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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

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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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data.

Rosaria Tornisiello^1,2,3, Helene Kretzmer^1,2

¹Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany.

Bioinformatics Advances

|April 15, 2026

Summary

Accurately determining genotypes at the single-cell level is crucial for studying gene expression. scGeno, a new Hidden Markov Model, infers chromosome-level genotypes from single-cell RNA sequencing data, overcoming technical challenges.

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Last Updated: Apr 16, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

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Area of Science:

Genomics
Computational Biology
Single-cell analysis

Background:

Accurate genotype determination is essential for single-cell studies of monoallelic expression and genomic imprinting.
Challenges in single-cell RNA sequencing (scRNA-seq) data include technical noise, allelic dropout, and sparse expression, especially in heterogeneous populations.

Purpose of the Study:

To develop a novel computational method for inferring chromosome-level genotype states from scRNA-seq data.
To enable accurate genotype determination at the single-cell level, facilitating the study of allele-specific expression.

Main Methods:

Introduced scGeno, a categorical Hidden Markov Model (HMM).
Models sequential gene expression ratios from scRNA-seq data to infer genotype states along chromosomes.
Leverages the sequential continuity of genotype states to address limitations of single-cell data.

Main Results:

Generates chromosome-resolved, comprehensive genotype maps for individual samples.
The probabilistic framework effectively accounts for technical noise, ensuring high accuracy in genotype assignment.
Validated on experimental data, demonstrating robust performance in identifying distinct genotypic states.

Conclusions:

scGeno provides a robust solution for genotype inference from scRNA-seq data.
Enables systematic investigation of allele-specific expression patterns at single-cell resolution.
The method overcomes key challenges associated with analyzing genetically heterogeneous single cells.