Predicting the origin of stains from whole miRNome massively parallel sequencing data |
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| Affiliation: | 1. Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland;2. National Center for Forensic Science, University of Central Florida, Orlando, USA;3. Functional Genomics Center Zurich (FGCZ), University of Zurich/ETH Zurich, Zurich, Switzerland;4. Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Aas, Norway;1. Department of Forensic Molecular Biology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands;2. Thermo Fisher Scientific/Life Technologies, South San Francisco, CA, USA;1. Department of Forensic Genetics, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610041, Sichuan, China;2. Department of Microbiology, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610041, Sichuan, China;1. Department of Forensic Genetics, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610041, Sichuan, China;2. Institute of Forensic Science, Chengdu Public Security Bureau, Chengdu 610081, Sichuan, China;3. Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410000, Hunan, China;4. Department of Criminal Science and Technology, Sichuan Police College, Luzhou 646000, Sichuan, China;5. Institute of Criminal Science and Technology, Shenzhen Public Security Bureau, Shenzhen518000, Shenzhen, China;6. Department of Biochemistry and Molecular Biology, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610041, Sichuan, China;1. Zurich Institute of Forensic Medicine, University of Zurich, Switzerland;2. National Center for Forensic Science, University of Central Florida, Orlando, USA;3. Department of Chemistry, University of Central Florida, PO Box 162366, Orlando, FL, 32816-2366, USA;4. Carabinieri Scientific Department of Rome- Genetic Unit, Rome, Italy;5. Malopolska Centre of Biotechnology of the Jagiellonian University, Gronostajowa st. 7A, 30-387, Krakow, Poland;6. National Institute of Legal Medicine and Forensic Sciences, Portugal;7. Orchid Cellmark Ltd., Abingdon, UK;8. National Institute of Standards and Technology, Material Measurement Laboratory, Gaithersburg, MD, United States;9. Department of Pharmacy and Forensic Science, King’s College London, Franklin-Wilkins Building, 150 Stamford Street, London, UK;10. Institute of Legal Medicine, Medical Faculty, University of Cologne, Germany;11. Department of Forensic Biology, Oslo University Hospital, Oslo, Norway;12. Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark;13. Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands;14. Laboratoire de Police Scientifique de Lyon, Institut National de Police Scientifique, F-69134, Ecully, France;15. Forensic Genetics Unit, Institute of Forensic Sciences, University of Santiago de Compostela, Spain;p. Institute of Legal Medicine, Medical University of Innsbruck, Austria;q. Forensic Science Program, Pennsylvania State University, PA, USA;r. Institute of Legal Medicine, University of Münster, Germany;s. Gerhard-Domagk-Institute of Pathology, University Hospital Münster, Germany;t. Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA, The Hague, The Netherlands;1. Shanghai Key Laboratory of Forensic Medicine, Institute of Forensic Science, Ministry of Justice, P.R. China, Shanghai 200063, China;2. Thermo Fisher Scientific, Shanghai, China;3. Department of Forensic Genetics, West China School of Preclinical and Forensic Medicine, Sichuan University (West China University of Medical Sciences), Chengdu, China |
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| Abstract: | In this study, we have screened the six most relevant forensic body fluids / tissues, namely blood, semen, saliva, vaginal secretion, menstrual blood and skin, for miRNAs using a whole miRNome massively parallel sequencing approach. We applied partial least squares (PLS) and linear discriminant analysis (LDA) to predict body fluids based on the expression of the miRNA markers. We estimated the prediction accuracy for models including different subsets of miRNA markers to identify the minimum number of markers needed for sufficient prediction performance. For one selected model consisting of 9 miRNA markers we calculated their importance for prediction of each of the six different body fluid categories. |
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| Keywords: | Forensic science Body fluid identification miRNA Massively parallel sequencing MPS Probabilistic method Partial least squares PLS Linear discriminant analysis LDA |
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