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Updated: Jan 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Proof-of-concept study on an integrated multiplex pyrosequencing system and CLPSQ-Net algorithm for multiplex

Yueyan Cao¹, Qiang Zhu², Yuguo Huang²

¹College of Basic Medicine & Forensic Medicine, Henan University of Science and Technology, Luoyang, China; West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China.

Forensic Science International

|November 30, 2025

Summary

This study introduces a novel multiplex microhaplotype (MH) detection system using pyrosequencing (PSQ) and deep learning. The system achieves high accuracy for forensic DNA analysis, improving upon traditional methods.

Keywords:

Contrastive learning framework Microhaplotype genotyping Multiplex pyrosequencing Nucleotide dispensation optimization Peak-height simulation

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Last Updated: Jan 9, 2026

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Pyrosequencing: A Simple Method for Accurate Genotyping

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Published on: May 25, 2015

Area of Science:

Forensic Science
Genetics
Bioinformatics

Background:

Microhaplotypes (MHs) offer high polymorphism and short fragment lengths, making them valuable for forensic applications.
Existing methods for MH detection can be limited by data scarcity and complex signal deconvolution.

Purpose of the Study:

To develop a streamlined multiplex MH detection system using pyrosequencing (PSQ) and deep contrastive learning.
To enhance accuracy and efficiency in MH genotyping for forensic use.

Main Methods:

Selection of polymorphic short-fragment MHs from the 1000 Genomes Project (1KGP).
Algorithmic optimization of nucleotide dispensation orders and peak-height simulation for machine learning.
Development of CLPSQ-Net, a deep contrastive learning framework for deconvoluting multiplex PSQ signals.

Main Results:

Simultaneous genotyping of four MH loci with high concordance (>0.98 for uniplex, >0.99 for multiplex).
CLPSQ-Net achieved 89.7% classification accuracy and 88.4% F1-score on experimental data, significantly outperforming regression methods.
Demonstrated input sensitivity of 100 pg DNA.

Conclusions:

Established a scalable framework for multiplex MH genotyping via PSQ.
The developed system offers superior base-calling accuracy, streamlined efficiency, and expanded utility for complex loci profiling in forensic applications.