Dataset Information

Popmarker: Identifying Phylogenetic Markers at the Population Level.

ABSTRACT: As phylogenomic approach becomes a common practice for constructing true bacterial phylogenies, it has become apparent that single molecular markers such as 16S ribosomal DNA often lead to misclassification of species. In this study, we present a program called Popmarker that uses the true species phylogeny and identifies a minimum set of molecular markers reflecting the bacterial evolution history and phylogenetic relationship at the resolution of populations. Popmarker ranks the proteome according to the correlation of whole species tree or subtree branch length against orthologous sequence distances. We demonstrate that 5 proteins of 2 top ranks achieve the same resolution as concatenation of 2203 single-copy orthologous genes and the right species classification as well as correct split of the 2 groups of Vibrio campbellii . The top-ranking genes selected by Popmarker are candidates that lead to speciation and are useful in distinguishing close related species in microbiome study.

SUBMITTER: Ke HM

PROVIDER: S-EPMC5562387 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Popmarker: Identifying Phylogenetic Markers at the Population Level.

Ke Huei-Mien HM Yu Chun-Ping CP Liu Yu-Ching YC Tsai Isheng J IJ

Evolutionary bioinformatics online 20170816

As phylogenomic approach becomes a common practice for constructing true bacterial phylogenies, it has become apparent that single molecular markers such as 16S ribosomal DNA often lead to misclassification of species. In this study, we present a program called Popmarker that uses the true species phylogeny and identifies a minimum set of molecular markers reflecting the bacterial evolution history and phylogenetic relationship at the resolution of populations. Popmarker ranks the proteome accor ...[more]

PMID: 29104426

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Project description:BACKGROUND:Genetic markers can be used to identify and verify the origin of individuals. Motivation for the inference of ancestry ranges from conservation genetics to forensic analysis. High density assays featuring Single Nucleotide Polymorphism (SNP) markers can be exploited to create a reduced panel containing the most informative markers for these purposes. The objectives of this study were to evaluate methods of marker selection and determine the minimum number of markers from the BovineSNP50 BeadChip required to verify the origin of individuals in European cattle breeds. Delta, Wright's FST, Weir & Cockerham's FST and PCA methods for population differentiation were compared. The level of informativeness of each SNP was estimated from the breed specific allele frequencies. Individual assignment analysis was performed using the ranked informative markers. Stringency levels were applied by log-likelihood ratio to assess the confidence of the assignment test. RESULTS:A 95% assignment success rate for the 384 individually genotyped animals was achieved with <80, <100, <140 and <200 SNP markers (with increasing stringency threshold levels) across all the examined methods for marker selection. No further gain in power of assignment was achieved by sampling in excess of 200 SNP markers. The marker selection method that required the lowest number of SNP markers to verify the animal's breed origin was Wright's FST (60 to 140 SNPs depending on the chosen degree of confidence). Certain breeds required fewer markers (<100) to achieve 100% assignment success. In contrast, closely related breeds require more markers (~200) to achieve>95% assignment success. The power of assignment success, and therefore the number of SNP markers required, is dependent on the levels of genetic heterogeneity and pool of samples considered. CONCLUSIONS:While all SNP selection methods produced marker panels capable of breed identification, the power of assignment varied markedly among analysis methods. Thus, with effective exploration of available high density genetic markers, a diagnostic panel of highly informative markers can be produced.

Project description:BackgroundSerological markers for exposure to different Plasmodium species have recently been used in multiplex immunoassays based on the Luminex technology. However, interpretation of the assay results requires consideration of the half-life of specific antibodies against these markers. Therefore, the aim of the present study was to document the half-life of malaria specific serological makers, as well as assessing the sensitivity of these markers to pick up recent changes in malaria exposure.MethodsA recently developed multiplex immunoassay was used to measure the intensity of antibody (Ab) responses against 19 different Plasmodium specific antigens, covering different human malaria parasites and two vector saliva antigens. Therefore, 8439 blood samples from five cross-sectional surveys in Ratanakiri, Cambodia, were analysed. These involve a random selection from two selected surveys, and an additional set of blood samples of individuals that were randomly re-sampled three, four or five times. A generalized estimating equation model and linear regression models were fitted on log transformed antibody intensity data.ResultsResults showed that most (17/21) Ab-responses are higher in PCR positive than PCR negative individuals. Furthermore, these antibody-responses follow the same upward trend within each age group. Estimation of the half-lives showed differences between serological markers that reflect short- (seasonal) and long-term (year round) transmission trends. Ab levels declined significantly together with a decrease of PCR prevalence in a group of malaria endemic villages.ConclusionFor Plasmodium falciparum, antibodies against LSA3.RE, GLURP and Pf.GLURP.R2 are most likely to be a reflexion of recent (range from 6 to 8 months) exposure in the Mekong Subregion. PvEBP is the only Plasmodium vivax Ag responding reasonably well, in spite of an estimated Ab half-life of more than 1 year. The use of Ab intensity data rather dichotomizing the continuous Ab-titre data (positive vs negative) will lead to an improved approach for serological surveillance.

Project description:IntroductionUS claims data contain medical data on large heterogeneous populations and are excellent sources for medical research. Some claims data do not contain complete death records, limiting their use for mortality or mortality-related studies. A model to predict whether a patient died at the end of the follow-up time (referred to as the end of observation) is needed to enable mortality-related studies.ObjectiveThe objective of this study was to develop a patient-level model to predict whether the end of observation was due to death in US claims data.MethodsWe used a claims dataset with full death records, Optum© De-Identified Clinformatics® Data-Mart-Database-Date of Death mapped to the Observational Medical Outcome Partnership common data model, to develop a model that classifies the end of observations into death or non-death. A regularized logistic regression was trained using 88,514 predictors (recorded within the prior 365 or 30 days) and externally validated by applying the model to three US claims datasets.ResultsApproximately 25 in 1000 end of observations in Optum are due to death. The Discriminating End of observation into Alive and Dead (DEAD) model obtained an area under the receiver operating characteristic curve of 0.986. When defining death as a predicted risk of?>?0.5, only 2% of the end of observations were predicted to be due to death and the model obtained a sensitivity of 62% and a positive predictive value of 74.8%. The external validation showed the model was transportable, with area under the receiver operating characteristic curves ranging between 0.951 and 0.995 across the US claims databases.ConclusionsUS claims data often lack complete death records. The DEAD model can be used to impute death at various sensitivity, specificity, or positive predictive values depending on the use of the model. The DEAD model can be readily applied to any observational healthcare database mapped to the Observational Medical Outcome Partnership common data model and is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/DeadModel .

Dataset Information

Popmarker: Identifying Phylogenetic Markers at the Population Level.

Publications

Popmarker: Identifying Phylogenetic Markers at the Population Level.

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