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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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Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...

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Updated: May 4, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

scCMP: A Deep Learning Method for Identifying Clonal Mutational Profiles From Single-Cell Genomic Data.

Junlei Zhou, Ruixiang Li, Fangyuan Shi

IEEE Transactions on Computational Biology and Bioinformatics

|August 14, 2025

Summary

This summary is machine-generated.

We developed scCMP, a novel computational method, to accurately identify clonal mutational profiles by integrating single-cell copy number and point mutation data. This approach enhances understanding of tumor evolution and intra-tumor heterogeneity.

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Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Accurate inference of clonal mutational profiles is crucial for understanding tumor evolution and intra-tumor heterogeneity.
Single-cell multi-modal genomic data (copy numbers, point mutations) offer multiple views of clonal patterns.
Existing computational methods for integrating single-cell copy number and point mutation data are limited.

Purpose of the Study:

To introduce scCMP, a deep joint representation learning framework for accurate identification of clonal mutational profiles.
To develop a computational method that effectively integrates single-cell copy number and point mutation data.
To provide a tool for analyzing intra-tumor heterogeneity and clonal selection.

Main Methods:

scCMP utilizes hybrid Transformer-CNN architectures and graph convolutional networks.
It integrates single-cell copy number and point mutation data by fusing individual and commonality information.
The framework generates meaningful cell embeddings for identifying clonal clusters.

Main Results:

scCMP accurately aggregates cells with similar mutational profiles into clusters.
The method was evaluated on five real single-cell DNA sequencing datasets.
scCMP demonstrates good scalability on datasets from other omics technologies and surpasses state-of-the-art methods.

Conclusions:

scCMP is an effective computational framework for integrating single-cell genomic data.
It accurately identifies clonal mutational profiles, advancing the study of tumor evolution.
The method offers an advantage in analyzing multi-modal single-cell genomic data.