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Updated: Jun 29, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Read-level DNA methylation deconvolution enhances circulating tumor DNA detection.

Ting Qi^1,2, Lakshmi Narayanan Lakshmanan², Yuwei Yang¹

¹State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Sipailou No. 2, Nanjing 210096, China.

Briefings in Bioinformatics

|October 20, 2025

Summary

This summary is machine-generated.

A new method, Alpha, enhances the detection of low-frequency DNA methylation signals. This approach improves cell-type deconvolution and enables sensitive identification of circulating tumor DNA (ctDNA) for cancer detection.

Keywords:

DNA methylation cell-free DNA circulating tumor DNA deconvolution

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Epigenetics
Genomics
Computational Biology

Background:

DNA methylation is crucial for cellular identity but conventional methods struggle with low-frequency signals.
Detecting subtle methylation changes is vital for understanding cellular heterogeneity and disease states.

Purpose of the Study:

To introduce Alpha, a novel method for sensitive identification of low-frequency cell-type-specific DNA methylation.
To evaluate Alpha's performance in cell-type deconvolution and circulating tumor DNA (ctDNA) detection.

Main Methods:

Alpha combines unbiased segmentation with read-level identification of methylation signals.
Method performance was assessed using simulated cell-type admixtures and cell-free DNA (cfDNA) from cancer models.
Alpha was integrated with non-negative least squares (Alpha-NNLS) for ctDNA detection.

Main Results:

Alpha identified methylation markers enriched in regulatory genomic elements.
Alpha-NNLS demonstrated superior performance in estimating tumor fraction from cfDNA compared to existing methods.
Alpha-NNLS showed strong concordance with existing approaches in early-stage colon cancer plasma samples.

Conclusions:

Alpha provides a sensitive approach for detecting low-frequency DNA methylation signals.
Alpha-NNLS shows significant potential for non-invasive ctDNA detection in cancer diagnostics.
The method advances epigenetic analysis for cellular identity and cancer biomarker discovery.