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Updated: Feb 20, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

A universal indel filtering workflow for both long-read and short-read NGS data.

Md Shariful Islam Bhuyan¹, M Sohel Rahman²

¹CSE Department, ECE Building, Bangladesh University of Engineering and Technology, West Palashi, Dhaka, Bangladesh.

BMC Research Notes

|February 19, 2026

Summary

This study introduces a machine learning tool to improve insertion and deletion (indel) detection in genomic data. The workflow enhances accuracy for both long-read and short-read sequencing, aiding disease genomics and personalized healthcare.

Keywords:

Gradient-boosting Indel detection Long-read sequencing Short-read sequencing Variant filtering

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Last Updated: Feb 20, 2026

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate detection of insertions and deletions (indels) is crucial for genomic applications like disease research and personalized medicine.
Current indel detection methods face challenges, especially in complex genomic regions and with diverse sequencing data.
Existing tools often require specific sequencing workflow details, limiting their universal applicability.

Purpose of the Study:

To develop a universal, machine learning-based filtering workflow to enhance indel detection accuracy.
To create a method that is independent of specific sequencing workflow parameters, such as read depth.
To improve the reliability of indel calling across both long-read and short-read sequencing technologies.

Main Methods:

Developed a machine learning workflow using a gradient-boosting classifier (XGBoost).
Utilized publicly available genomic annotation datasets for training and validation.
The workflow was designed to be workflow-agnostic, not requiring sequencing-specific information.

Main Results:

Achieved a significant improvement in indel detection precision: approximately 26% for long-read and 24% for short-read data.
Maintained high recall rates, around 90%, across both data types.
Validated the approach using the Genome in a Bottle (GIAB) dataset and precisionFDA Truth Challenge V2 data.

Conclusions:

The presented machine learning workflow effectively enhances indel detection accuracy for diverse sequencing data.
The tool is open-access and workflow-agnostic, offering a broadly applicable solution for improving genomic analyses.
This method provides a valuable resource for advancing disease genomics, population genetics, and personalized healthcare through more reliable indel calling.