This study examined 266 individuals from various populations including African American, East Asian, South Asian, European, and mixed populations to evaluate the ForenSeq™ Signature Prep Kit Primer Mix B. Focus was placed on phenotypic and biogeographical ancestry predictions by Illumina's Universal Analysis Software (UAS). These outcomes were compared to those obtained through web‐tools developed at the Erasmus Medical Center (EMC) and available from the Forensic Resource/Reference on Genetics‐knowledge base (FROG‐kb), as well as to eye color predictions by the 8‐plex system. Due to drop‐outs, predictions for eye and hair color by UAS failed for various samples in each run. By including reads below thresholds, predictions could be obtained for all samples through the web‐tools. Eye and hair color predictions for African Americans, East Asians, and South Asians showed no errors. Difficulties however, were noted in intermediate (neither blue nor brown) eye color predictions. These were mitigated by the 8‐plex system through exclusion of one eye color (e.g. “not brown”). Additionally, notable discrepancies were observed in hair color predictions, where some black/dark‐brown haired individuals were predicted to have blond hair. Overall, ancestry predictions were more accurate by FROG‐kb compared to UAS, which did not predict South Asian ancestry, particularly Indian individuals.

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

No abstract

Museums displaying artifacts of the human struggle against oppression are often caught in their own internal struggle between presenting factual and unbiased descriptions of their collections, or relying on testament of survivors. Often this quandary is resolved in favor of what can be verified, not what is remembered. However, with improving instrumentation, methods and informatic approaches, science can help uncover evidence able to reconcile memory and facts. Following World War II, thousands of small, cement-like disks with numbers impressed on one side were found at concentration camps throughout Europe. Survivors claimed these disks were made of human cremains; museums erred on the side of caution—without documentation of the claims, was it justifiable to present them as fact? The ability to detect species relevant biological material in these disks could help resolve this question. Proteomic mass spectrometry of five disks revealed all contained proteins, including collagens and hemoglobins, suggesting they were made, at least in part, of animal remains. A new protein/informatics approach to species identification showed that while human was not always identified as the top contributor, human was the most likely explanation for one disk. To our knowledge, this is the first demonstration of protein recovery from cremains. Data are available via ProteomeXchange with identifier PXD035267.