Evaluation of genetic markers for forensic identification of human body fluids

Afolabi, O.A.; Roeder, Amy D.; Iyengar, Arati; Hadi, Sibte

doi:10.1016/j.fsigss.2017.09.098

Cited by 1 publication

(1 citation statement)

References 192 publications

(242 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these serological methods have been utilized for decades, there are well-documented flaws associated with each [3][4][5][6]. Therefore, there has been extensive research among the forensic community that addresses molecular-based BFID methods, such as messenger RNA or microRNA analysis, DNA methylation, and microbial DNA analysis [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Design and optimization of a 16S microbial qPCR multiplex for the presumptive identification of feces, saliva, vaginal and menstrual secretions

Lewis

Seashols‐Williams

2022

Journal of Forensic Sciences

View full text Add to dashboard Cite

Molecular methods for body fluid identification have been extensively researched in the forensic community over the last decade, mostly focusing on RNA-based methods. Microbial DNA analysis has long been used for forensic applications, such as postmortem interval estimations, but only recently has it been applied to body fluid identification. High-throughput sequencing of the 16S ribosomal RNA gene by previous research groups revealed that microbial signatures and abundances vary across human body fluids at the genus and/or species taxonomic level. Since quantitative PCR is still the current technique used in forensic DNA analysis, the purpose of this study was to design a qPCR multiplex targeting the 16S gene of Bacteroides uniformis, Streptococcus salivarius, and Lactobacillus crispatus that can distinguish between feces, saliva, and vaginal/menstrual secretions, respectively. Primers and probes were designed at the species level because these bacteria are highly abundant within their respective fluid. The validated 16S triplex was evaluated in DNA extracts from thirty donors of each body fluid. A classification regression tree model resulted in 96.5% classification accuracy of the population data, which demonstrates the ability of this 16S triplex to presumptively identify these fluids with high confidence at the quantification step of the forensic workflow using minimal input volume of DNA extracted from evidentiary samples.

show abstract