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Search Publications by: Sarah Riman (Assoc)

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Displaying 1 - 12 of 12

Development of Publicly Available Forensic DNA Sequence Mixture Data

March 12, 2025
Author(s)
Erica Romsos, Kevin Kiesler, Carolyn Steffen, Lisa Borsuk, Sarah Riman, Lauren Mullen, Jodi Irwin, Peter Vallone, Katherine Gettings
Background: In 2018, the Next-Generation Sequencing Committee of SWGDAM queried bioinformatic and statistical interpretation method developers regarding data needs for the development of sequence-based probabilistic genotyping software. Methods: Based on

Development of a Forensic DNA Research Grade Test Material

October 16, 2024
Author(s)
Erica Romsos, Lisa Borsuk, Carolyn Steffen, Sarah Riman, Kevin Kiesler, Peter Vallone
Advancements in forensic DNA typing technology and methods have resulted in increased sensitivity and, while beneficial, carry the weight of more challenging profile interpretation. In response, the forensic DNA community has often requested more complex

A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation

June 19, 2024
Author(s)
Sarah Riman, Jo-Anne Bright, Kaitlin Huffman, Lilliana Moreno, Sicen Liu, Asmitha Sathya, Peter Vallone
Several fully continuous probabilistic genotyping software (PGS) use Markov chain Monte Carlo algorithms (MCMC) to assign weights to different proposed genotype combinations at a locus. Replicate interpretations of the same profile in these software are

Understanding the characteristics of sequence-based single-source DNA profiles

November 9, 2019
Author(s)
Sarah Riman, Hariharan K. Iyer, Lisa A. Borsuk, Peter M. Vallone
The sequencing of STR markers provides additional information present in the underlying sequence variation that is typically masked by traditional fragment-based genotyping. However, the interpretation of STR profiles generated by targeted sequencing

Are Reported Likelihood Ratios Well Calibrated?

October 8, 2019
Author(s)
Peter Vallone, Sarah Riman, Jan Hannig
In this work we introduce a new statistical methodology for empirically examining the validity of model-based Likelihood Ratio (LR) systems by applying a general statistical inference approach called generalized fiducial inference [1]. LR systems are