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Perlin M.W.,Cybergenetics Corporation | Legler M.M.,Cybergenetics Corporation | Spencer C.E.,Cybergenetics Corporation | Smith J.L.,Cybergenetics Corporation | And 3 more authors.
Journal of Forensic Sciences | Year: 2011

DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all-or-none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two-person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min=2.32, max=10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min=1.00, max=11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in-depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele ® DNA mixture interpretation and establish a significant information improvement over human review. © 2011 American Academy of Forensic Sciences.


Perlin M.W.,Cybergenetics Corporation | Belrose J.L.,Northeast Regional Forensic Institute | Duceman B.W.,New York State Police Forensic Investigation Center
Journal of Forensic Sciences | Year: 2013

DNA evidence can pose interpretation challenges, particularly with low-level or mixed samples. It would be desirable to make full use of the quantitative data, consider every genotype possibility, and objectively produce accurate and reproducible DNA match results. Probabilistic genotype computing is designed to achieve these goals. This validation study assessed TrueAllele® probabilistic computer interpretation on 368 evidence items in 41 test cases and compared the results with human review of the same data. Whenever there was a human result, the computer's genotype was concordant. Further, the computer produced a match statistic on 81 mixture items (for 87 inferred matching genotypes) in the test cases, while human review reported a statistic on 25 of these items (30.9%). Using match statistics to quantify information, probabilistic genotyping was shown to be sensitive, specific, and reproducible. These results demonstrate that objective probabilistic genotyping of biological evidence can reliably preserve DNA identification information. © 2013 The Authors. Journal of Forensic Sciences published by Wiley Periodicals, Inc. on behalf of American Academy of Forensic Sciences.


PubMed | Cybergenetics Corporation
Type: Journal Article | Journal: Journal of forensic sciences | Year: 2011

DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all-or-none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two-person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min = 2.32, max = 10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min = 1.00, max = 11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in-depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele() DNA mixture interpretation and establish a significant information improvement over human review.


Patent
Cybergenetics Inc. | Date: 2014-11-20

The present invention pertains to a process for automatically analyzing mixed DNA samples. Specifically, the process comprises the steps of obtaining a mixed DNA sample; amplifying the DNA sample to produce a product; detecting the product to produce a signal; and analyzing the signal to determine information about the composition of the mixed DNA sample. This DNA mixture analysis is useful for finding criminals and convicting them. This mixture analysis provides high quality estimates, and can determine genotypes, mixture weights, and likelihood ratios. This analysis provides confidence measures in the results it computes, and generates reports and intuitive visualizations. The process automates a tedious manual procedure, thereby reducing the cost, time, and effort involved in DNA forensic analysis. The system can greatly accelerate the rate of DNA crime analysis, and be used to exonerate innocent people.

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