Browsing by Subject "Computational Biology / methods"
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Item A Comparison of Gene Expression Profiles between Glucocorticoid Responder and Non-Responder Bovine Trabecular Meshwork Cells Using RNA Sequencing(PLOS, 2017-01-09) Bermudez, Jaclyn Y.; Webber, Hannah C.; Brown, Bartley; Braun, Terry A.; Clark, Abbot F.; Mao, WeimingThe most common ocular side effect of glucocorticoid (GC) therapy is GC-induced ocular hypertension (OHT) and GC-induced glaucoma (GIG). GC-induced OHT occurs in about 40% of the general population, while the other 60% are resistant. This study aims to determine the genes and pathways involved in differential GC responsiveness in the trabecular meshwork (TM). Using paired bovine eyes, one eye was perfusion-cultured with 100nM dexamethasone (DEX), while the fellow eye was used to establish a bovine TM (BTM) cell strain. Based on maximum IOP change in the perfused eye, the BTM cell strain was identified as a DEX-responder or non-responder strain. Three responder and three non-responder BTM cell strains were cultured, treated with 0.1% ethanol or 100nM DEX for 7 days. RNA and proteins were extracted for RNA sequencing (RNAseq), qPCR, and Western immunoblotting (WB), respectively. Data were analyzed using the human and bovine genome databases as well as Tophat2 software. Genes were grouped and compared using Student's t-test. We found that DEX induced fibronectin expression in responder BTM cells but not in non-responder cells using WB. RNAseq showed between 93 and 606 differentially expressed genes in different expression groups between responder and non-responder BTM cells. The data generated by RNAseq were validated using qPCR. Pathway analyses showed 35 pathways associated with differentially expressed genes. These genes and pathways may play important roles in GC-induced OHT and will help us to better understand differential ocular responsiveness to GCs.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.