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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.
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Updated: Sep 30, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A Bayesian method to cluster single-cell RNA sequencing data using copy number alterations.

Salvatore Milite¹, Riccardo Bergamin¹, Lucrezia Patruno²

¹Department of Mathematics and Geosciences, University of Trieste, Trieste 34127, Italy.

Bioinformatics (Oxford, England)

|March 17, 2022

Summary

This summary is machine-generated.

CONGAS is a new Bayesian method that identifies cancer subclones and their RNA expression profiles from DNA and RNA sequencing data. This tool helps map copy number alterations to cancer phenotypes at the single-cell level.

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Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Cancers exhibit heterogeneity due to distinct subpopulations with unique genetic and epigenetic alterations.
Copy number alterations (CNAs) driving tumor aneuploidy are implicated in cancer progression and treatment response.
Precisely defining cancer subclones and their associated phenotypes from sequencing data remains a significant challenge.

Purpose of the Study:

To introduce CONGAS, a novel Bayesian probabilistic method for analyzing cancer subclonal architecture.
To jointly identify single-cell clusters based on subclonal CNAs and RNA expression differences.
To provide a scalable computational tool for dissecting tumor heterogeneity.

Main Methods:

CONGAS employs a Bayesian probabilistic framework to phase bulk DNA and single-cell RNA sequencing data from independent assays.
It utilizes statistical priors from bulk DNA sequencing data and does not require a normal reference.
The method incorporates a GPU backend and variational inference for efficient computation.

Main Results:

CONGAS successfully identifies distinct cancer subclones and their associated RNA expression phenotypes at the single-cell level.
The method accurately determines tumor subclonal composition using data from both 10× and Smart-Seq assays.
CONGAS demonstrates robust performance on both simulated and real cancer genomic data.

Conclusions:

CONGAS offers a powerful approach to unraveling complex cancer subclonal structures and their functional consequences.
This method enhances our understanding of tumor heterogeneity and its impact on cancer phenotypes.
CONGAS provides a valuable tool for cancer research, facilitating the analysis of single-cell and bulk sequencing data.