Next Generation Sequencing (NGS) represents a transformative leap in forensic science. It generates massively parallel DNA data from degraded or low-level samples, opening new paths to solve previously unsolvable cases. When paired with advanced probabilistic genotyping, forensic experts can separate complex mixtures, extract meaningful profiles from challenging evidence, and quantify results with unparalleled precision. These innovations redefine what forensic science can achieve in casework, victim identification, and criminal investigations.
This talk features a case study inspired by Ohio v. Slater Howell, alongside STR data from a NGS validation study. On April 7, 2014, gas station clerk Babul Kumer Saha was fatally shot during a Cleveland robbery. Investigators collected DNA from the gas pump button, a plastic bag, and a lottery register. These items yielded complex mixtures that the Cuyahoga County crime laboratory could not interpret using traditional methods.
The crime lab’s TrueAllele® analysts fully resolved these capillary electrophoresis (CE) mixtures. Their probabilistic genotyping (PG) linked Slater Howell to the crime. But how would their analysis have looked had they used NGS data?
Our presentation retraces the case’s PG analysis steps using NGS mixture validation data in place of the original CE data. We add a five-person mixture from a fictitious gun swab to better demonstrate the technologies. This retracing shows how to couple NGS with PG to solve complex DNA evidence.
NGS becomes cost-effective when labs process many samples or use multiplexed panels. By sequencing millions of fragments at once, NGS outpaces CE in speed and efficiency, especially with large-scale projects. Multiplexing allows scientists to gather more comprehensive data from a single sequencing run.
Unlike CE, which detects STR alleles by fragment length, NGS simultaneously analyzes both STRs and SNPs—capturing identity, ancestry, phenotype, and lineage markers. It provides sequence-level resolution, allowing scientists to distinguish “isoalleles” that are identical in size but differ in sequence.
NGS handles low-template and degraded DNA with greater sensitivity, making it ideal for cold cases, touch DNA, and complex mixtures. It detects minor contributors that CE might overlook and provides more accurate sequence context, enhancing the ability to differentiate between similar DNA profiles.
By reading exact nucleotide sequences, NGS distinguishes contributors more clearly than CE’s peak-height approach. NGS also supports SNP panels that predict biogeographical ancestry, physical traits, and Y/mtDNA lineages—valuable tools in investigative genetic genealogy and the identification of unknown remains.
Despite all these advanced detection features, NGS has not yet gained widespread adoption for DNA analysis in the forensic science community. A key reason has been the lack of powerful NGS data interpretation software that can extract useful information from the complex DNA mixtures seen in everyday casework. The NGS may be better than CE, but probabilistic genotyping of CE data is at least a viable solution.
Until now. Combining better NGS data with PG software gives the best of both worlds. NGS improves on CE, and PG improves on CPI. Together, NGS plus PG provides a synergistic solution. As demonstrated in this presentation, coupling NGS with powerful software that unmixes DNA mixtures offers new solutions to challenging DNA evidence problems.
Next Generation Sequencing (NGS) represents a transformative leap in forensic science. It generates massively parallel DNA data from degraded or low-level samples, opening new paths to solve previously unsolvable cases. When paired with advanced probabilistic genotyping, forensic experts can separate complex mixtures, extract meaningful profiles from challenging evidence, and quantify results with unparalleled precision. These innovations redefine what forensic science can achieve in casework, victim identification, and criminal investigations.
This talk features a case study inspired by Ohio v. Slater Howell, alongside STR data from a NGS validation study. On April 7, 2014, gas station clerk Babul Kumer Saha was fatally shot during a Cleveland robbery. Investigators collected DNA from the gas pump button, a plastic bag, and a lottery register. These items yielded complex mixtures that the Cuyahoga County crime laboratory could not interpret using traditional methods.
The crime lab’s TrueAllele® analysts fully resolved these capillary electrophoresis (CE) mixtures. Their probabilistic genotyping (PG) linked Slater Howell to the crime. But how would their analysis have looked had they used NGS data?
Our presentation retraces the case’s PG analysis steps using NGS mixture validation data in place of the original CE data. We add a five-person mixture from a fictitious gun swab to better demonstrate the technologies. This retracing shows how to couple NGS with PG to solve complex DNA evidence.
NGS becomes cost-effective when labs process many samples or use multiplexed panels. By sequencing millions of fragments at once, NGS outpaces CE in speed and efficiency, especially with large-scale projects. Multiplexing allows scientists to gather more comprehensive data from a single sequencing run.
Unlike CE, which detects STR alleles by fragment length, NGS simultaneously analyzes both STRs and SNPs—capturing identity, ancestry, phenotype, and lineage markers. It provides sequence-level resolution, allowing scientists to distinguish “isoalleles” that are identical in size but differ in sequence.
NGS handles low-template and degraded DNA with greater sensitivity, making it ideal for cold cases, touch DNA, and complex mixtures. It detects minor contributors that CE might overlook and provides more accurate sequence context, enhancing the ability to differentiate between similar DNA profiles.
By reading exact nucleotide sequences, NGS distinguishes contributors more clearly than CE’s peak-height approach. NGS also supports SNP panels that predict biogeographical ancestry, physical traits, and Y/mtDNA lineages—valuable tools in investigative genetic genealogy and the identification of unknown remains.
Despite all these advanced detection features, NGS has not yet gained widespread adoption for DNA analysis in the forensic science community. A key reason has been the lack of powerful NGS data interpretation software that can extract useful information from the complex DNA mixtures seen in everyday casework. The NGS may be better than CE, but probabilistic genotyping of CE data is at least a viable solution.
Until now. Combining better NGS data with PG software gives the best of both worlds. NGS improves on CE, and PG improves on CPI. Together, NGS plus PG provides a synergistic solution. As demonstrated in this presentation, coupling NGS with powerful software that unmixes DNA mixtures offers new solutions to challenging DNA evidence problems.
Workshop currently at capacity. A waitlist is available to join on our registration page.
Forensic Analyst, Cybergenetics
Kari Danser is a forensic analyst at Cybergenetics (Pittsburgh, PA).She has two years of experience operating the TrueAllele® Casework system on casework data and has testified in court multiple times. She trains various laboratory analysts as well as students about the system. She has also presented research using the TrueAllele Casework system at conferences for the forensic science community.
