Forensic Labs Get a Roadmap for Next-Generation DNA Testing
Researchers have benchmarked the best software tools for analyzing shotgun sequencing data from degraded DNA samples—a critical technique for solving cold cases and identifying remains. The findings show which tool combinations work best, helping forensic laboratories adopt more reliable genetic analysis methods that could accelerate criminal investigations and missing-person cases.
Originaltitel: Preparing for shotgun sequencing in forensic genetics - Benchmarking of tools for read mapping, genotype calling, and imputation
Shotgun-sekvensering öppnar nya möjligheter för forensisk genetik, men val av bioinformatiska verktyg är avgörande för resultatets tillförlitlighet. Forskare från Köpenhamns universitet, svenska Rättsmedicinalverket och norska institutioner jämförde fem aligneringsalgoritmer, fyra genotypanropningsmetoder och tre imputationsmetoder på forensiska prover av varierande kvalitet. Vid analys av högt kvalitativt material (blod och munslemhinna) fungerade alla verktyg väl. Däremot visade lågt kvalitativt material (hår) betydande skillnader i prestanda. Kombinationen BWA-MEM tillsammans med ANGSD eller GATK HaplotypeCaller plus GLIMPSE2-imputering gav lägst felrate. Resultaten är relevanta för aktörer inom forensisk genetik och personidentifikation: rätt verktygsval minskar risken för felidentifikation och sätter standard för DNA-underrättelsearbete och genealogisk utredning.
<p>Shotgun sequencing has emerged as a powerful tool in forensic genetics, as it allows for comprehensive genetic profiles to be generated from highly degraded DNA. The method enables simultaneous access to a wide range of markers, thereby supporting applications such as human identification (HID), DNA intelligence, and forensic investigative genetic genealogy (FIGG). However, the accuracy and utility of shotgun sequencing data are highly dependent on the bioinformatic analysis. In this study, we benchmarked widely used bioinformatic tools using shotgun sequencing data from both high-quality (blood and buccal) and low-quality (hair) forensic samples. Specifically, we evaluated five alignment algorithms (Bowtie2, BWA-ALN, BWA-MEM, CLC, and CLC LightSpeed), four genotype calling methods (ANGSD, ATLAS, GATK HaplotypeCaller, and a custom rule-based approach), and three imputation methods (Beagle4.1, Beagle5.4, and GLIMPSE2). All investigated tools were found to be suitable for analysing high-quality reference samples. However, their performance varied significantly when applied to low-quality (hair) forensic samples. The combination of BWA-MEM, ANGSD, or GATK HaplotypeCaller, and imputation with GLIMPSE2 produced the lowest degree of discordance. The work presented here emphasises the importance of informed bioinformatic tool selection and optimisation, and it provides practical recommendations for analysing shotgun sequencing data in forensic genetics.</p>