by Flinders University
Promising new inroads into critical DNA testing has been forecast by Flinders University experts who have applied machine learning to DNA profiling.
From medical diagnostics to forensic tests and national security, PCR (polymerase chain reaction) DNA profiling has revolutionized high-throughput sampling this century—but little has changed since it was developed in the 1980s.
"Even a small improvement in PCR performance could have a huge impact on the hundreds of thousands of forensic and intelligence DNA samples amplified every year—notably when samples are degraded," say experts, including Flinders University academic Dr. Duncan Taylor, from Forensic Science SA.
The new research, published in two articles in Genes, discovered significant improvements both in the quality of DNA profiling and more efficient PCR cycling conditions with the use of artificial intelligence methods, says College of Science and Engineering Ph.D. candidate Caitlin McDonald, who led the study.
"Our system has the potential to overcome challenges that have hindered forensic scientists for decades, especially with trace, inhibited or degraded samples," says McDonald, who recently presented on the study at the International Society of Forensic Genetics conference (ISFG 2024).
"By intelligently optimizing PCR for a wide variety of sample types, it can dramatically enhance amplification success, delivering more reliable results in even the most complex cases.
"Beyond forensics, this system has the capacity to revolutionize other fields that depend on PCR, such as clinical diagnostics and environmental monitoring, by boosting efficiency, reducing errors, and enabling high-throughput analysis across diverse applications."
PCR is a common laboratory technique used to amplify or copy small segments of genetic material, for example in DNA fingerprinting, diagnosing genetic disorders or detecting bacteria or viruses such as COVID-19.
Backed by other Flinders University's College of Science and Engineering experts, including Professor Adrian Linacre and AI computer scientist Associate Professor Russell Brinkworth, the study used machine learning to create new "smart PCR" systems—targeting large-scale potential alterations and faster cycling conditions for rapid and more accurate results.
The first article in Genes presents the theoretical framework, and the second article describes the large-scale testing of the new "smart" PCR system.
Flinders University Professor Linacre, who focuses on DNA forensic technologies, says PCR is widely used across various fields and applications, including forensic science, animal research, medicine, and national security.
"AI and machine learning are so new, yet when harnessed correctly, have the possibility to greatly increase the sensitivity of PCR testing," says Professor Linacre.
He says research on non-coding sections of DNA has been carried out in forensic testing since 1994.
"With further research, these AI-ML methods have the potential to improve the quality of DNA evidence used in criminal investigations, and to increase the quality of trace DNA samples, enhancing the criminal justice process."
Associate Professor of Autonomous Systems Russell Brinkworth says improving existing processes will further define AI applications in the future.
"Traditionally, DNA amplification required all settings to be in place before the process commenced. This did not take into account the many possible differences between samples and conditions," adds Associate Professor Brinkworth.
"By utilizing advances in machine learning and sensors, we have changed the process of PCR from a one-size-fits-all to a customized and optimized individual experience. Producing higher quality and quantity DNA faster than previously possible."
More information: Caitlin McDonald et al, Developing a Machine-Learning 'Smart' PCR Thermocycler, Part 1: Construction of a Theoretical Framework, Genes (2024). DOI: 10.3390/genes15091196
Caitlin McDonald et al, Developing a Machine Learning 'Smart' Polymerase Chain Reaction Thermocycler Part 2: Putting the Theoretical Framework into Practice, Genes (2024). DOI: 10.3390/genes15091199
Journal information: Genes
Provided by Flinders University
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