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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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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Furui Liu¹, Fangyuan Shi^1,2, Fang Du^1,2

¹School of Information Engineering, Ningxia University, 750021, Ningxia, China.

Briefings in Bioinformatics

|April 26, 2024

Summary

CoT is a novel bioinformatics method that deciphers tumor clonal copy number substructure from noisy single-cell DNA sequencing (scDNA-seq) data. It effectively infers cell subpopulations and estimates copy numbers, outperforming existing methods.

Keywords:

copy number alteration deep learning intra-tumor heterogeneity single-cell sequencing

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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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell DNA sequencing (scDNA-seq) is crucial for understanding intra-tumor heterogeneity.
Jointly inferring tumor clones and their copy number profiles from scDNA-seq data is challenging due to data noise.

Purpose of the Study:

To introduce CoT, a new bioinformatics method for deciphering clonal copy number substructure.
To improve the accuracy and robustness of tumor copy number profiling from scDNA-seq data.

Main Methods:

CoT utilizes a Copy number Transformer autoencoder with a multi-head attention mechanism.
It learns latent cell embeddings by exploring genomic region correlations.
Infers cell subpopulations and estimates single-cell copy numbers via joint analysis within clusters.

Main Results:

CoT effectively infers cell subpopulations and estimates copy numbers.
The method alleviates read count non-uniformity, yielding robust tumor copy number estimations.
Performance evaluations on synthetic and real datasets demonstrate CoT's superiority over state-of-the-art methods.

Conclusions:

CoT is a powerful tool for deciphering clonal copy number substructure in tumors.
The method enhances the analysis of intra-tumor heterogeneity using scDNA-seq data.
CoT offers robust and accurate copy number profiling for cancer research.