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Updated: Oct 8, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

AMC: accurate mutation clustering from single-cell DNA sequencing data.

Zhenhua Yu^1,2, Fang Du^1,2

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

Bioinformatics (Oxford, England)

|December 24, 2021

Summary

Accurate Mutation Clustering (AMC) software improves phylogenetic inference from large single-cell DNA sequencing datasets by efficiently clustering mutations. This enhances computational efficiency and accuracy for analyzing intra-tumor heterogeneity.

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Last Updated: Oct 8, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Characterizing Mutational Load and Clonal Composition of Human Blood

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Published on: July 11, 2019

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
Computational Biology
Cancer Research

Background:

Single-cell DNA sequencing (scDNA-seq) provides high-resolution intra-tumor heterogeneity profiles.
Current phylogenetic inference methods struggle with computational efficiency and accuracy on large scDNA-seq datasets.

Purpose of the Study:

To introduce Accurate Mutation Clustering (AMC), a novel software for efficient phylogenetic inference from scDNA-seq data.
To improve the accuracy and computational efficiency of analyzing large-scale single-cell genomic data.

Main Methods:

AMC utilizes Principal Component Analysis (PCA) and K-means clustering to group mutations.
It infers maximum likelihood estimates for genotypes within mutation clusters.
Phylogenetic trees are reconstructed using these inferred genotypes.

Main Results:

AMC demonstrates high computational efficiency in clustering mutations on large scDNA-seq datasets.
The method improves the accuracy of phylogenetic inference compared to existing approaches.
Evaluations on simulated datasets confirm AMC's utility for large-scale analyses.

Conclusions:

AMC offers a significant advancement in analyzing intra-tumor heterogeneity using scDNA-seq data.
The software enhances the efficiency and accuracy of phylogenetic tree reconstruction.
AMC is a valuable tool for researchers working with large single-cell genomic datasets.