1. Novel Method for Accurately Assessing Pull-up Artifacts in STR Analysis
- Author
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George R. Riley, Douglas W. Hoffman, and Robert M. Goor
- Subjects
0301 basic medicine ,Forensic Genetics ,Computer science ,Sample (statistics) ,Article ,Pathology and Forensic Medicine ,Set (abstract data type) ,03 medical and health sciences ,0302 clinical medicine ,Fragment (logic) ,Genetics ,Humans ,030216 legal & forensic medicine ,Artifact (error) ,Training set ,biology ,business.industry ,Pattern recognition ,Models, Theoretical ,biology.organism_classification ,DNA Fingerprinting ,030104 developmental biology ,STR analysis ,Deconvolution ,Artificial intelligence ,Osiris ,business ,Artifacts ,Software ,Microsatellite Repeats - Abstract
OSIRIS is a mathematically-based software tool for Short Tandem Repeat (STR) and DNA fragment analysis (https://www.ncbi.nlm.nih.gov/osiris/). As part of its routine sample analyses, OSIRIS computes unique quality metrics that can be used for sample quality assessment. A common artifact of STR analysis is cross-channel pull-up or pull-down (negative pull-up). This occurs because of the spectral overlap between the dyes used with the marker set, and the failure of the color deconvolution matrix to isolate the colors in the dye set adequately. This paper describes a mathematical method for analyzing and quantifying the pull-up patterns across sample channels and effectively identifying and correcting the pull-up artifacts, as implemented in OSIRIS. Unlike approaches to pull-up that require a training set composed of previous samples, the algorithm described here uses a mathematical model of the underlying causes of pull-up. It is based solely on the information intrinsic to the sample it is analyzing and therefore incorporates the effects of the ambient conditions and the specific procedures used in creating the sample. These conditions are the physical determinants of the level of pull-up in the sample and are not likely to be represented in a training set consisting of past samples.
- Published
- 2020