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                            Library & Multimedia
 Statistical modeling of STR capillary electrophoresis signal
NCJ Number
              302536
Journal

*Bmc Bioinformatics* Volume: 20 Dated: 2019
Author(s)
          S. Karkar;
                        et al
Date Published

2019
Annotation
              This project sought to develop a signal model that can effectively characterize the various components of STR signal independent of a priori knowledge of the quantity or quality of DNA.
Abstract
              In order to isolate an individual’s genotype from a sample of biological material, most laboratories use PCR and Capillary Electrophoresis (CE) to construct a genetic profile based on polymorphic loci known as Short Tandem Repeats (STRs). The resulting profile consists of CE signal which contains information about the length and number of STR units amplified. For samples collected from the environment, interpretation of the signal can be challenging given that information regarding the quality and quantity of the DNA is often limited. The signal can be further compounded by the presence of noise and PCR artifacts such as stutter which can mask or mimic biological alleles. Manual interpretation methods cannot comprehensively account for such nuances. The current study first sought to mathematically characterize the quality of the profile by measuring changes in the signal with respect to amplicon size. Next, it examined the noise, allele, and stutter components of the signal and developed distinct models for each. Using cross-validation and model selection, it identified a model that can be effectively used for downstream interpretation. Finally, the project implemented the model in NOCIt, a software system that calculates the a posteriori probability distribution on the number of contributors. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2014-DN-BX-K026


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Award
        Establishing Exclusion Criteria and the Significance of Inclusion: Developing Tools for the Interpretation of Complex DNA Mixtures
    
  
  
Date Published: January 1, 2019
    
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