archive-com.com » COM » C » CYBGEN.COM

Total: 359

Choose link from "Titles, links and description words view":

Or switch to "Titles and links view".
  • Cybergenetics :: Examination of DNA mixture proportion variability using multiple STR typing kits and NIST standard reference material® 2391c component D
    Transfer Sending Data Downloads Back to Presentations Examination of DNA Mixture Proportion Variability Using Multiple STR Typing Kits and NIST Standard Reference Material 174 2391c Component D M C Kline C R Hill E L R Butts D L Duewer M D Coble and J M Butler Examination of DNA mixture proportion variability using multiple STR typing kits and NIST standard reference material 174 2391c component D Promega s Twenty

    Original URL path: http://www.cybgen.com/information/presentations/2011/ISHI/Kline-Examination-of-DNA-mixture-proportion-variability-using-multiple-STR-typing-kits-and-NIST-standard-reference-material-2391c-component-D/page.shtml (2016-02-12)
    Open archived version from archive


  • Cybergenetics :: Combining DNA evidence for greater match information
    Symposium on Human Identification National Harbor MD 4 Oct 2011 Poster Download Poster Abstract Most fields of scientific enquiry routinely combine data from multiple experiments These experiments can be repetitions drawn from one item or involve different items entirely The motivation is to elicit maximal information from an experimental design The statistical mechanism is the joint likelihood function A likelihood function mathematically quantifies how well alternative hypotheses explain a fixed data result A joint likelihood function assesses these hypotheses on multiple data items simultaneously Typically the data are drawn from independent experiments Therefore the joint likelihood simply multiplies together the likelihoods from separate experiments jointly conditioned on a particular explanatory hypothesis In forensic DNA science human data interpretation is usually performed on data derived from only a single item This practice is a consequence of thresholding quantitative peak height data into all or none qualitative allele possibilities in order to simplify human review Combining profiles after interpretation for consensus has little statistical foundation Quantitative computer interpretation however does not share these artificial limitations It is therefore natural to mathematically preserve identification information by inferring a genotype using a joint likelihood function examining all the independent data simultaneously This talk describes

    Original URL path: http://www.cybgen.com/information/presentations/2011/ISHI/Perlin-Combining-DNA-evidence-for-greater-match-information/page.shtml (2016-02-12)
    Open archived version from archive

  • Cybergenetics :: An On-Demand DNA Information Community
    lab then notifies DNA information consumers such as police prosecutors and defense With the advent of lab automation though machines are generating ever greater quantities of more challenging data Painstaking human review of difficult data is slow and expensive and loses considerable identification information 1 This information loss discarding informative data as inconclusive or reducing match strength a million fold devalues the DNA information currency Computer based probabilistic genotyping SWGDAM 2010 3 2 2 can eliminate this interpretation bottleneck Allegheny County in Pennsylvania has been pioneering an approach to on demand DNA interpretation that serves its criminal justice community In this new DNA processing paradigm the crime lab identifies challenging data and forwards it electronically to their interpretation partner Cybergenetics for computer processing Within days the company sends a TrueAllele 174 match report to the prosecutor or other DNA information consumer The laboratory is thus relieved of a challenging DNA interpretation burden and the information needs of the county s criminal justice community are met with great speed at low cost In a recent serial rape case the key evidence was a DNA mixture having a minor contributor that matched the suspect with a CPI of 10 5 More could be done So the lab gave the data to Cybergenetics for TrueAllele processing and within two days the prosecutor received a LR match score of 10 15 DNA evidence from a second victim with a CPI of 10 6 was later delivered to Cybergenetics who found a 10 12 TrueAllele LR match from a 10 minor component to the same suspect The police and prosecutor received this second match information dispatch within two days of submission The case went to trial 10 days later Another state recently adopted this lab generates computer interprets information model The state lab had processed

    Original URL path: http://www.cybgen.com/information/presentations/2011/ISHI/David-An-on-demand-DNA-information-community/page.shtml (2016-02-12)
    Open archived version from archive

  • Cybergenetics :: The New Standard Reference Material® 2391c:PCR-based DNA Profiling Standard
    Newsletters Newsroom Press Releases Trials Contact Directions Information Visiting Support FAQ File Transfer Sending Data Downloads Back to Presentations The New Standard Reference Material 174 2391c PCR based DNA Profiling Standard M C Kline E L R Butts C R Hill M D Coble D L Duewer and J M Butler The new standard reference material 174 2391c PCR based DNA profiling standard International Society for Forensic Genetics Vienna Austria

    Original URL path: http://www.cybgen.com/information/presentations/2011/ISFG/Kline-The-new-standard-reference-material-2391c-PCR-based-DNA-profiling-standard/page.shtml (2016-02-12)
    Open archived version from archive

  • Cybergenetics :: Combining DNA Evidence for Greater Match Information
    Society for Forensic Genetics Vienna Austria 1 Sep 2011 Downloads Download Poster Abstract Most fields of scientific enquiry routinely combine data from multiple experiments These experiments can be repetitions drawn from one item or involve different items entirely The motivation is to elicit maximal information from an experimental design The statistical mechanism is the joint likelihood function A likelihood function mathematically quantifies how well alternative hypotheses explain a fixed data result A joint likelihood function assesses these hypotheses on multiple data items simultaneously Typically the data are drawn from independent experiments Therefore the joint likelihood simply multiplies together the likelihoods from separate experiments jointly conditioned on a particular explanatory hypothesis In forensic DNA science human data interpretation is usually performed on data derived from only a single item This practice is a consequence of thresholding quantitative peak height data into all or none qualitative allele possibilities in order to simplify human review Combining profiles after interpretation for consensus has little statistical foundation Quantitative computer interpretation however does not share these artificial limitations It is therefore natural to mathematically preserve identification information by inferring a genotype using a joint likelihood function examining all the independent data simultaneously This talk describes the

    Original URL path: http://www.cybgen.com/information/presentations/2011/ISFG/Perlin-Combining-DNA-evidence-for-greater-match-information/page.shtml (2016-02-12)
    Open archived version from archive

  • Cybergenetics :: Investigative DNA Databases the Preserve Identification Information
    for Forensic Genetics Vienna Austria 1 Sep 2011 Downloads Download Poster Abstract A DNA database can link crime scenes to suspects providing investigative leads These DNA associations can solve cold cases track terrorists and stop criminals before they inflict further harm However current government databases do not fully preserve DNA identification information and cannot maximize public safety DNA data is summarized in a genotype The genotype can be stored on a database and compared with other genotypes to form a likelihood ratio LR match statistic Data uncertainty present in most evidence translates into genotype probability Highly informative interpretation uses all the quantitative DNA data placing higher probability on more likely genotype values Most evidence though is interpreted by qualitative human review which diffuses probability across infeasible solutions Since the LR is proportional to the true genotype probability weaker interpretation methods lead to weaker or nonexistent DNA matches The weakest DNA interpretation method is RMNE which thresholds quantitative data into all or none qualitative allele events The current DNA databases including CODIS use an RMNE allele representation that discards considerable genotype information losing sensitivity and specificity The probabilistic genotype representation is part of the new ANSI NIST ITL data exchange standard

    Original URL path: http://www.cybgen.com/information/presentations/2011/ISFG/Perlin-Investigative-DNA-databases-that-preserve-identification-information/page.shtml (2016-02-12)
    Open archived version from archive

  • Cybergenetics :: Inclusion probability is a likelihood ratio: implications for DNA mixtures
    more informative method that preserves more of the identification information present in the DNA data The debate implicitly assumes that there is some essential difference between PI and LR suggesting that each perspective should be understood and evaluated on its own merits In fact there are many different LR statistics for DNA mixture interpretation And PI happens to be just one of them However amongst all currently used LRs the PI version does have a special distinction it is the least informative Recognizing that PI is just another LR has important consequences for forensic science practice The current PI vs LR controversy can be finally put to rest Inclusion efficacy can be measured in terms of how well it preserves the data s identification information The logarithm of the LR is a standard information measure and PI is a LR so this assessment is easily accomplished The inclusion method can be supported in court based on its scientific status as a valid LR The PI statistic can be better understood through the inclusion likelihood function used in its LR construction The relevance of PI can be challenged on particular DNA evidence by examining the appropriateness of its inclusion likelihood modeling

    Original URL path: http://www.cybgen.com/information/presentations/2010/ISHI/Perlin_Inclusion_probability_is_a_likelihood_ratio_implications_for_DNA_mixtures/page.shtml (2016-02-12)
    Open archived version from archive

  • Cybergenetics :: Inclusion probability for DNA mixtures is a subjective one-sided match statistic unrelated to identification information
    defendant is usually needed for court Jurors rely on this strength of match to help decide guilt or innocence However the reliability of unsophisticated match statistics for DNA mixtures has been questioned Materials and Methods The most prevalent match statistic for DNA mixtures is the combined probability of inclusion CPI used by crime labs for over 15 years When testing 13 short tandem repeat STR genetic loci the 1 CPI value is typically around a million regardless of DNA mixture composition However actual identification information as measured by a likelihood ratio LR spans a much broader range This study examined probability of inclusion PI mixture statistics for 517 locus experiments drawn from 16 reported cases and compared them with LR locus information calculated independently on the same data The log 1 PI values were examined and compared with corresponding log LR values Results The LR and CPI methods were compared in case examples of false inclusion false exclusion a homicide and criminal justice outcomes Statistical analysis of crime laboratory STR data shows that inclusion match statistics exhibit a truncated normal distribution having zero center with little correlation to actual identification information By the law of large numbers LLN 1 CPI

    Original URL path: http://www.cybgen.com/information/publication/2015/JPI/Perlin-Inclusion-probability-for-DNA-mixtures-is-a-subjective-one-sided-match-statistic-unrelated-to-identification-information/page.shtml (2016-02-12)
    Open archived version from archive



  •