Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles

BACKGROUND: Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. RESULTS: To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. CONCLUSIONS: Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0740-8) contains supplementary material, which is available to authorized users

Ancient dental calculus preserves signatures of biofilm succession and interindividual variation independent of dental pathology

Dental calculus preserves oral microbes, enabling comparative studies of the oral microbiome and health through time. However, small sample sizes and limited dental health metadata have hindered health-focused investigations to date. Here, we investigate the relationship between tobacco pipe smoking and dental calculus microbiomes. Dental calculus from 75 individuals fromthe 19th century Middenbeemster skeletal collection (Netherlands) were analyzed by metagenomics. Demographic and dental health parameters were systematically recorded, including the presence/number of pipe notches. Comparative data sets fromEuropean populations before and after the introduction of tobaccowere also analyzed. Calculus species profileswere comparedwith oral pathology to examine associations between microbiome community, smoking behavior, and oral health status. The Middenbeemster individuals exhibited relatively poor oral health,with a high prevalence of periodontal disease, caries, heavy calculus deposits, and antemortem tooth loss. No associations between pipe notches and dental pathologies, or microbial species composition,were found. Calculus samples before and after the introduction of tobacco showed highly similar species profiles. Observed interindividual microbiome differences were consistent with previously described variation in human populations from the Upper Paleolithic to the present. Dental calculus may not preserve microbial indicators of health and disease status as distinctly as dental plaque

Ancient Plasmodium genomes shed light on the history of human malaria

Malaria-causing protozoa of the genus Plasmodium have exerted one of the strongest selective pressures on the human genome, and resistance alleles provide biomolecular footprints that outline the historical reach of these species1. Nevertheless, debate persists over when and how malaria parasites emerged as human pathogens and spread around the globe1,2. To address these questions, we generated high-coverage ancient mitochondrial and nuclear genome-wide data from P. falciparum, P. vivax and P. malariae from 16 countries spanning around 5,500 years of human history. We identified P. vivax and P. falciparum across geographically disparate regions of Eurasia from as early as the fourth and first millennia bce, respectively; for P. vivax, this evidence pre-dates textual references by several millennia3. Genomic analysis supports distinct disease histories for P. falciparum and P. vivax in the Americas: similarities between now-eliminated European and peri-contact South American strains indicate that European colonizers were the source of American P. vivax, whereas the trans-Atlantic slave trade probably introduced P. falciparum into the Americas. Our data underscore the role of cross-cultural contacts in the dissemination of malaria, laying the biomolecular foundation for future palaeo-epidemiological research into the impact of Plasmodium parasites on human history. Finally, our unexpected discovery of P. falciparum in the high-altitude Himalayas provides a rare case study in which individual mobility can be inferred from infection status, adding to our knowledge of cross-cultural connectivity in the region nearly three millennia ago.This project was funded by the National Science Foundation, grants BCS-2141896 and BCS-1528698; the European Research Council (ERC) under the European Union’s Horizon 2020 programme, grants 851511-MICROSCOPE (to S. Schiffels), 771234-PALEoRIDER (to W.H.) and starting grant 805268-CoDisEASe (to K.I.B.); and the ERC starting grant Waves ERC758967 (supporting K. Nägele and S.C.). We thank the Max Planck-Harvard Research Center for the Archaeoscience of the Ancient Mediterranean for supporting M. Michel, E. Skourtanioti, A.M., R.A.B., L.C.B., G.U.N., N.S., V.V.-M., M. McCormick, P.W.S., C.W. and J.K.; the Kone Foundation for supporting E.K.G. and A.S.; and the Faculty of Medicine and the Faculty of Biological and Environmental Sciences at the University of Helsinki for grants to E.K.G. A.S. thanks the Magnus Ehrnrooth Foundation, the Sigrid Jusélius Foundation, the Finnish Cultural Foundation, the Academy of Finland, the Life and Health Medical Foundation and the Finnish Society of Sciences and Letters. M.C.B. acknowledges funding from: research project PID2020-116196GB-I00 funded by MCIN/AEI/10.13039/501100011033; the Spanish Ministry of Culture; the Chiang Ching Kuo Foundation; Fundación Palarq; the EU FP7 Marie Curie Zukunftskolleg Incoming Fellowship Programme, University of Konstanz (grant 291784); STAR2-Santander Universidades and Ministry of Education, Culture and Sports; and CEI 2015 project Cantabria Campus Internacional. M.E. received support from the Czech Academy of Sciences award Praemium Academiae and project RVO 67985912 of the Institute of Archaeology of the Czech Academy of Sciences, Prague. This work has been funded within project PID2020-115956GB-I00 ‘Origen y conformación del Bronce Valenciano’, granted by the Ministry of Science and Innovation of the Government of Spain, and grants from the Canadian Institutes for Health Research (MZI187236), Research Nova Scotia (RNS 2023-2565) and The Center for Health Research in Developing Countries. D.K. is the Canada research chair in translational vaccinology and inflammation. R.L.K. acknowledges support from a 2019 University of Otago research grant (Human health and adaptation along Silk Roads, a bioarchaeological investigation of a medieval Uzbek cemetery). P.O. thanks the Jane and Aatos Erkko Foundation, the Finnish Cultural Foundation and the Academy of Finland. S. Peltola received support from the Emil Aaltonen Foundation and the Ella and Georg Ehrnrooth Foundation. D.C.S.-G. thanks the Generalitat Valenciana (CIDEGENT/2019/061). E.W.K. acknowledges support from the DEEPDEAD project, HERA-UP, CRP (15.055) and the Horizon 2020 programme (grant 649307). M. Spyrou thanks the Elite program for postdocs of the Baden-Württemberg Stiftung. Open access funding provided by Max Planck Society

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Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems