Results of STRmix™ NGS Developmental Validation Will Also Be Presented

STRmix co-developer Dr John Buckleton will be the recipient of the prestigious Adelaide Medal for scientific achievement that has a marked influence on forensic sciences from an international perspective. 

 

Introduced in 1990, the Adelaide Medal will be awarded to Dr. Buckleton when the International Association of Forensic Sciences (IAFS) – the largest forensic association in the world – holds its 23rd Triennial Meeting in conjunction with the 26th Symposium of the Australian and New Zealand Forensic Science Society from November 20-24 in Sydney, Australia.  

 

In addition to his role as co-developer of STRmix™ – sophisticated forensic software capable of resolving mixed DNA profiles previously regarded as too complex or degraded to interpret – Dr. Buckleton’s casework experience covers 33 years in the U.S., Australia, the Netherlands, the United Kingdom, and New Zealand. During that time, he has examined more than 2,000 cases, testified more than 200 times, and co-authored more than 250 publications in the forensic field. 

 

In addition to receiving the Adelaide Medal, Dr Buckleton will present the 2020 Adelaide Medal Lecture at 3:15 pm on Tuesday, November 21 and participate in a subsequent panel discussion.

 

Dr. Buckleton will not be the only individual representing STRmix during the IAFS Triennial Meeting. On Thursday, November 23, STRmix Scientist Developer Kevin Cheng will present results from the developmental validation of STRmix™ NGS, a probabilistic genotyping tool for the interpretation of autosomal STRs from forensic profiles generated using next generation sequencing (NGS), as part of a panel discussion on Biological Criminalistics.

 

NGS, also known as MPS, is a new DNA profiling technology offering ultra-high throughput, scalability, and speed. Used to determine the order of nucleotides in entire genomes or targeted regions of DNA, the technology has the potential to assist forensic laboratories in cases involving human identification, kinship, and ancestral origin at a faster pace and a cheaper cost.

 

Other panel members include: Kate Stevenson (ESR), who will present a snapshot of trends for mixed DNA profiles associated with new technologies; Lauren Elborough (Adelaide University), discussing the use of explainable artificial intelligence techniques to better understand a DNA-profile-reading neural network; Penny Cooper (Forensic Biology Department, PathWest Laboratory Medicine), speaking on interpretation and reporting considerations following DNA profile enhancement techniques; and Suhua Zhang (School of Basic Medical Sciences, Fudan University), presenting a MNP panel for forensic DNA mixture deconvolution.

 

STRmix will also be present in the exhibition, demonstrating the latest updates of FaSTR™ DNA, STRmix™, and DBLR™ which complete the full workflow(external link) from analysis to interpretation and database matching.

 

Since 1957, the IAFS has held an international meeting every three years to allow both academics and practitioners across all forensic science disciplines to meet and exchange ideas. Expected to attract 1,500 attendees from around the world, the program will strategically cover critical issues in order to identify possible solutions for stronger and more reliable forensic science and medicine in the future.

 

The theme of the 23rd Triennial Meeting is “Where to From Here?”. The theme asks the forensic science community to review contemporary practices and consider strategies to improve operational contributions and effectiveness in the light of both current and future challenges.

 

A multidisciplinary professional organization that provides guidance for advances in science and its application in the legal system, the IAFS supports professionalism, integrity, competencies, education, support for research, improvement of practice, and support for cooperation in forensic sciences.

 

For more information about IAFS and its 23rd Triennial Meeting, visit https://iafs2023.com.au/(external link).

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