Browsing by Subject "bioinformatics"
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Item A Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures(MDPI, 2021-01-20) Smart, Utpal; Cihlar, Jennifer Churchill; Mandape, Sammed N.; Muenzler, Melissa; King, Jonathan L.; Budowle, Bruce; Woerner, August E.Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture's genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic.Item Effects of a Synthetic Amino Acid Diet: Insights from the Guy Microbiome, Inflammation, and Behavior(2021-05) Mancilla, Viviana J.; Allen, Michael S.; Jones, Harlan P.; Phillips, Nicole R.; Planz, John V.; Ellis, DorettePhenylketonuria (PKU) is an inborn error of phenylalanine metabolism primarily treated through a phenylalanine-restrictive diet and frequently supplemented with an amino acid formula to maintain proper nutrition. PKU patients often report high levels of anxiety along with symptoms of gastrointestinal distress (i.e., chronic diarrhea, constipation, cramps); symptoms previously associated with gut microbiome dysbiosis. Little is known of the effects of these dietary interventions on the gut microbiome of PKU patients, particularly in adults. The gut microbiome is a collection of microbes residing primarily in the large intestine. The colon is a major production site for short chain fatty acids (SCFAs) through anaerobic fermentation by commensal bacteria. SCFAs provide a source of energy for the colonocytes, as well as provide anti-inflammatory benefits. The production of SCFA appears to be dependent on the availability of soluble fibers and members of the gut microbiota capable of fermentation. We characterized the gut microbiome of adults with PKU for the first time and identified signs of dysbiosis. We then focused on the synthetic, low fiber, nature of the amino acid diet in a murine model. In this interdisciplinary study, we monitored the effect of a consuming synthetic diet on the composition of the murine gut microbiome over the course of 13 weeks, beginning at weaning. At the conclusion of the feeding period, mice we observed for anxiolytic behavior, locomotion, and cognition. We also searched for markers of inflammation through colon shrinkage, changes in cytokine levels within several tissues, and determined the concentration of SCFAs in the colon at the conclusion of the feeding period. The gut microbiome of mice fed the synthetic diet experienced significant deviation from the control group which affected relative abundance of beneficial bacteria. Mice on the synthetic diet were found to have shorter colons, lower concentration of SCFAs in the colon, and demonstrated elevated exploratory behavior.Item Genetic profiling of skin microbiomes for forensic human identification(2017-12-01) Schmedes, Sarah E.; Budowle, Bruce; Ge, Jianye; He, Johnny J.The field of microbial forensics has expanded from a focus in biodefense and biocrime attribution to include various metagenomics and microbiome applications made possible by advancements in sequencing and bioinformatics technologies. Recent developments in metagenomics and microbiome research with application to the forensic sciences, include post-mortem interval, body fluid identification, recent geolocation, and human identification. The primary goal of the dissertation described herein was to assess the feasibility of human identification from skin microbiomes using both shotgun metagenomic sequencing and targeted enrichment strategies. The main studies of this dissertation were conducted under the hypothesis that genes from stable, universal microbial species from the core skin microbiome can differentiate skin microbiomes of individuals and be applied towards forensic human identification purposes. The initial study presented describes the development of a tool, AutoCurE, used to identify errors in bacterial genome metadata from public databases and curate the data for subsequent use in comparative genomic studies. This study highlights the types of inconsistencies and errors which may be present in public genome databases and describes the development of a curated local bacterial database for use in subsequent studies. This doctoral research herein presents the development of a novel approach for human identification using stable, universal clade-specific markers from skin microbiomes. Initially, publically available shotgun metagenomic datasets generated from skin microbiome samples collected from 17 body sites from 12 individuals, sampled over three time points over the course of ~3-year period, were mined to identify stable, universal microbial markers. Supervised learning, specifically regularized multinomial logistic regression and 1-nearest-neighbor classification, were performed using the nucleotide diversities of clade-specific markers to predict the correct classification of skin microbiomes to their respective host individuals. Reduced subsets of markers were developed into a novel targeted metagenomics sequencing panel, the hidSkinPlex, to generate individual-specific skin microbiome profiles to use for human identification. Finally, the hidSkinPlex was evaluated on skin microbiome samples collected from eight individuals and three body sites, in triplicate, to demonstrate a proof-of-concept to differentiate individuals with high accuracy. The hidSkinPlex, comprised of 282 bacterial and 4 phage markers from 22 family-, genus-, species-, and subspecies-level clades, was used to correctly identify skin microbiomes from their respective donors with up to 92%, 96%, and 100% accuracy using samples from the foot, manubrium, and hand, respectively. Additionally, skin microbiomes were classified with up to 97% accuracy when the body site was unknown, and body site origin could be predicted with up to 86% accuracy. The hidSkinPlex is the first targeted metagenomics sequencing panel and method designed specifically for skin microbiomes with the intent of forensic human identification applicationsItem MaCHTools: Additional functionality for the imputation software MaCH(2016-12-01) Mitchel, Jeffrey S.; Robert C. Barber; Fan ZhangImputation of unknown genotypes is becoming a standard procedure in exploratory genetic association studies. Imputation is accomplished by comparing observed data from the study population to reference panels of individuals who are from a genetically similar population and genotyped at a dense set of polymorphic sites. Linkage disequilibrium within the reference panels is used to construct haplotypes and extrapolate allelic correlations in the test sample. Imputation has been shown to be accurate for the inference of genotypes at unobserved SNPs, as well as for quality control measures at genotyped locations. Imputing genotypes also allows cohorts that were genotyped on different platforms to be combined in a joint or meta-analysis. One of the most widely used imputation software packages is MaCH (http://csg.sph.umich.edu//abecasis/mach/). MaCH uses a powerful and accurate Markov chain-based algorithm, however its usability is lacking. MaCHTools allows the user to streamline their workflow with MaCH through input file specification, error checking, and QC measures, MaCHTools began as a series of Java scripts used to check input files and QC raw data as an initial step before imputing additional genotypes in MaCH. This set of scripts became invaluable to the GWAS workflow, but they were unpolished and ill-suited for public release to benefit the scientific community. This project aimed to bundle the scripts into a single executable program that provides a graphical user interface (GUI) to facilitate use by students and researchers to aid in streamlining the GWAS workflow. Additional functionalities include more efficient launching of jobs to compute clusters and compatibility with different Linux job handlers, the ability to easily switch between different GWAS projects including switching between different genotype data and reference datasets, more simplistic specification of parameters and thresholds, and several other usability improvements. The GWAS workflow that includes dataset preparation with MaCHTools coupled with haplotype estimation and imputation with MaCH was validated by replicating results from a published study of the genetic basis of Alzheimer’s endophenotypes in the Texas Alzheimer’s Research and Care Consortium. A similar analysis was then performed to determine the genetic basis of D, a latent variable that represents the dementing process.Item Proteomics-Based Identification of Retinal Protein Networks Impacted by Elevated Intraocular Pressure in the Hypertonic Saline Injection Model of Experimental Glaucoma(MDPI, 2023-08-26) Zaman, Khadiza; Nguyen, Vien; Prokai-Tatrai, Katalin; Prokai, LaszloElevated intraocular pressure is considered a major cause of glaucomatous retinal neurodegeneration. To facilitate a better understanding of the underlying molecular processes and mechanisms, we report a study focusing on alterations of the retina proteome by induced ocular hypertension in a rat model of the disease. Glaucomatous processes were modeled through sclerosing the aqueous outflow routes of the eyes by hypertonic saline injections into an episcleral vein. Mass spectrometry-based quantitative retina proteomics using a label-free shotgun methodology identified over 200 proteins significantly affected by ocular hypertension. Various facets of glaucomatous pathophysiology were revealed through the organization of the findings into protein interaction networks and by pathway analyses. Concentrating on retinal neurodegeneration as a characteristic process of the disease, elevated intraocular pressure-induced alterations in the expression of selected proteins were verified by targeted proteomics based on nanoflow liquid chromatography coupled with nano-electrospray ionization tandem mass spectrometry using the parallel reaction monitoring method of data acquisition. Acquired raw data are shared through deposition to the ProteomeXchange Consortium (PXD042729), making a retina proteomics dataset on the selected animal model of glaucoma available for the first time.Item skater: an R package for SNP-based kinship analysis, testing, and evaluation(F1000 Research Ltd., 2022-01-07) Turner, Stephen D.; Nagraj, V. P.; Scholz, Matthew; Jessa, Shakeel; Acevedo, Carlos; Ge, Jianye; Woerner, August E.; Budowle, BruceMotivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed. Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.