title: Probabilistic peak detection in CE-LIF for STR DNA typing.
abstract: In this work, we present a novel probabilistic peak detection algorithm based on a Bayesian framework for forensic DNA analysis. The proposed method aims at an exhaustive use of raw electropherogram data from a laser-induced fluorescence multi-CE system. As the raw data are informative up to a single data point, the conventional threshold-based approaches discard relevant forensic information early in the data analysis pipeline. Our proposed method assigns a posterior probability reflecting the data point's relevance with respect to peak detection criteria. Peaks of low intensity generated from a truly existing allele can thus constitute evidential value instead of fully discarding them and contemplating a potential allele drop-out. This way of working utilizes the information available within each individual data point and thus avoids making early (binary) decisions on the data analysis that can lead to error propagation. The proposed method was tested and compared to the application of a set threshold as is current practice in forensic STR DNA profiling. The new method was found to yield a significant improvement in the number of alleles identified, regardless of the peak heights and deviation from Gaussian shape.
authors.affiliation: Analytical Chemistry, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Michael
authors.initials: M
authors.lastname: Woldegebriel
authors.affiliation: Analytical Chemistry, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.|Netherlands Forensic Institute, The Hague, The Netherlands.|CLHC, Amsterdam Center for Forensic Science and Medicine, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Arian
authors.initials: A
authors.lastname: van Asten
authors.affiliation: Netherlands Forensic Institute, The Hague, The Netherlands.|CLHC, Amsterdam Center for Forensic Science and Medicine, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Ate
authors.initials: A
authors.lastname: Kloosterman
authors.affiliation: Analytical Chemistry, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Vivó-Truyols
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016172:DNA Fingerprinting / Q000379:methods*; D019075:Electrophoresis, Capillary / Q000379:methods*; D053803:Forensic Genetics / Q000379:methods*; D006801:Humans; D018895:Microsatellite Repeats / Q000235:genetics*; D015233:Models, Statistical
keywords: Bayesian statistics; Forensic; Peak detection; Probabilistic; STR analysis