True Positive Matches Research Articles

Evaluation of parentage testing accuracy of child trafficking cases: Combining the exclusion probability and likelihood ratio approaches

The Chinese government established a national anti-trafficking DNA database in 2009 to help reunite trafficked children with their families. The database collects DNA information from missing children's parents, trafficked and homeless children, then conducts parentage testing using 18 or more loci to find matched pairs. This article evaluates the matching accuracy of parentage testing in child-trafficking cases, under both Hardy-Weinberg equilibrium and population substructure. Both one-parent and two-parent scenarios are considered, and mutations are taken into account. The number of random matches is first evaluated using exclusion probability (PE). It is found that there are a large number of single parent–child pairs that match at 18 loci, but the PE approach cannot tell which are the true positive ones. The likelihood ratio (LR) approach can help distinguish the true positive matches. So the second step is to obtain the true positive rate and false positive rate of matched pairs of single parent and child according to the LR approach. Based on the results of the two-step procedure, it is concluded that more than 18 loci should be used to ensure a correct match of single parent and child.

A comparison of absorption inhibition and absorption elution methods for estimation of ABO blood groups in saliva

Background: ABO blood groups are the primary, most common, conspicuous, and easily detectable groups. These blood group specific antigens are not the exclusive domain of the erythrocyte, but abundantly present in many other bodily secretions such as sweat, semen and even saliva. Absorption inhibition and absorption elution are the main methods to detect salivary blood group antigens. The aim of this study is to detect the secretor status of 100 samples of saliva and to compare the diagnostic efficacy of absorption elution method and absorption inhibition methods in detecting the ABO (H) blood group antigens in secretors. Materials and Methods: Totally 100 un-stimulated whole saliva samples were analyzed for the presence of ABO (H) blood group specific antigens by absorption inhibition and absorption elution methods. Initially, secretor status was ascertained by detecting the H antigen in saliva, and both methods were used to detect the blood groups from saliva and the results were compared with that of blood which were statistically analyzed. Results: Of the 100 samples, 77 secretors and 23 non-secretors. Using absorption inhibition method 62 out of 77 samples gave results that matched with blood. Using absorption elution 70 out of 77 samples gave matching results. Absorption elution depicted better sensitivity, more true positive matches with blood as well as a higher Kappa coefficient. Conclusion: Absorption elution gave better results than absorption inhibition.

Morphometric Comparison of Clavicle Outlines from 3D Bone Scans and 2D Chest Radiographs: A Shortlisting Tool to Assist Radiographic Identification of Human Skeletons

This paper describes a computerized clavicle identification system primarily designed to resolve the identities of unaccounted-for U.S. soldiers who fought in the Korean War. Elliptical Fourier analysis is used to quantify the clavicle outline shape from skeletons and postero-anterior antemortem chest radiographs to rank individuals in terms of metric distance. Similar to leading fingerprint identification systems, shortlists of the top matching candidates are extracted for subsequent human visual assessment. Two independent tests of the computerized system using 17 field-recovered skeletons and 409 chest radiographs demonstrate that true-positive matches are captured within the top 5% of the sample 75% of the time. These results are outstanding given the eroded state of some field-recovered skeletons and the faintness of the 1950's photofluorographs. These methods enhance the capability to resolve several hundred cold cases for which little circumstantial information exists and current DNA and dental record technologies cannot be applied.

Playing the ‘Name Game’ to Identify University Patents in Germany

Identifying academic inventors is crucial to understanding IP-related university-to-industry technology transfer and analyzing university patent portfolios. However, such identification requires solving the “who is who” problem at the individual inventor level. This article describes data collection and matching techniques applied to identify academic inventors in Germany. To manage the large Germany dataset, we adjusted a matching technique applied in prior research by comparing the inventor and professor names in the first step after cleaning. Moreover, we suggest a new approach for determining the similarity score. Instead of using a cumulative similarity score, we use the data to discern criteria combinations that best identify true positive matches. We evaluated our methodology by applying it to the EP-INV-PatStat database and comparing its results with using the professor title to identify academic inventors. For Germany, we found that the results are less sensitive to the name comparison algorithm than the specific filtering criteria used. Inventor identification based on the professor title underestimates patenting activity by academics.

A Dynamic Noise Level Algorithm for Spectral Screening of Peptide MS/MS Spectra

BackgroundHigh-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide identifications. These spectra cannot be identified with any positive peptide matches in some database search programs or are identified with false positives in others. Removing these spectra can improve the database search results and lower computational expense.ResultsA new algorithm has been developed to filter tandem mass spectra of poor quality from shotgun proteomic experiments. The algorithm determines the noise level dynamically and independently for each spectrum in a tandem mass spectrometric data set. Spectra are filtered based on a minimum number of required signal peaks with a signal-to-noise ratio of 2. The algorithm was tested with 23 sample data sets containing 62,117 total spectra.ConclusionsThe spectral screening removed 89.0% of the tandem mass spectra that did not yield a peptide match when searched with the MassMatrix database search software. Only 6.0% of tandem mass spectra that yielded peptide matches considered to be true positive matches were lost after spectral screening. The algorithm was found to be very effective at removal of unidentified spectra in other database search programs including Mascot, OMSSA, and X!Tandem (75.93%-91.00%) with a small loss (3.59%-9.40%) of true positive matches.

A mass accuracy sensitive probability based scoring algorithm for database searching of tandem mass spectrometry data.

BackgroundLiquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments.ResultsA probability based statistical scoring model for assessing peptide and protein matches in tandem MS database search was derived. The statistical scores in the model represent the probability that a peptide match is a random occurrence based on the number or the total abundance of matched product ions in the experimental spectrum. The model also calculates probability based scores to assess protein matches. Thus the protein scores in the model reflect the significance of protein matches and can be used to differentiate true from random protein matches.ConclusionThe model is sensitive to high mass accuracy and implicitly takes mass accuracy into account during scoring. High mass accuracy will not only reduce false positives, but also improves the scores of true positive matches. The algorithm is incorporated in an automated database search program MassMatrix.

Practical and predictive bioinformatics methods for the identification of potentially cross‐reactive protein matches

A bioinformatics comparison of proteins introduced into food crops through genetic engineering provides a mechanism to identify those proteins that may present an increased risk of allergic reactions for individuals with existing allergies. The goal is to identify proteins that are known to be allergens or are so similar to an allergen that they may induce allergic cross-reactions. Three comparative approaches have traditionally been used, or considered for safety evaluations. One identifies any short (6-8) amino acid segment of the protein that exactly matches a known allergen sequence. The second is an overall primary sequence comparison using Basic Local Alignment Search Tool (BLAST) or FASTA to find matches of greater than 35% identity over 80 amino acids. The third is based on 3-D prediction programs to identify 3-D similarities that might predict potential cross-reactivity. The utility of each of these approaches was debated in the bioinformatics workshop. The consensus agreement from the expert workshop participants was that the short-segment match (e. g., 6-8 amino acids) provides an unacceptably high rate of false positive matches and an uncertain rate of true positive matches, and was not particularly useful for an allergenicity evaluation performed in the context of comprehensive safety evaluation. There was no consensus regarding the most appropriate bioinformatics method, an acceptable scoring criteria for triggering closer examination subsequent to a positive match, or an acceptable scoring mechanism for ranking the utility of the various 3-D approaches that were discussed during the workshop. However, the general consensus was that the most practical approach at this time is to evaluate primary sequence identities to known allergens using either FASTA or BLAST. While there was good agreement that identities of greater than 35% over 80 or more amino acids (recommended by Codex in 2003) is quite conservative, the conclusion was that additional data or studies would be needed to justify changing this criterion as there is some evidence that some individuals sensitized to proteins in evolutionarily conserved protein families may experience cross-reactions to proteins sharing approximately 40% identity.

Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements.

PSI-BLAST is an iterative program to search a database for proteins with distant similarity to a query sequence. We investigated over a dozen modifications to the methods used in PSI-BLAST, with the goal of improving accuracy in finding true positive matches. To evaluate performance we used a set of 103 queries for which the true positives in yeast had been annotated by human experts, and a popular measure of retrieval accuracy (ROC) that can be normalized to take on values between 0 (worst) and 1 (best). The modifications we consider novel improve the ROC score from 0.758 +/- 0.005 to 0.895 +/- 0.003. This does not include the benefits from four modifications we included in the 'baseline' version, even though they were not implemented in PSI-BLAST version 2.0. The improvement in accuracy was confirmed on a small second test set. This test involved analyzing three protein families with curated lists of true positives from the non-redundant protein database. The modification that accounts for the majority of the improvement is the use, for each database sequence, of a position-specific scoring system tuned to that sequence's amino acid composition. The use of composition-based statistics is particularly beneficial for large-scale automated applications of PSI-BLAST.