Healyfunch3894

MOTIVATION Recent progress in m7G RNA methylation studies has focused on its internal (rather than capped) presence within mRNAs. Tens of thousands of internal mRNA m7G sites have been identified within mammalian transcriptomes, and a single resource to best share, annotate and analyze the massive m7G data generated recently is sorely needed. RESULTS We report here m7GHub, a comprehensive online platform for deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine. The m7GHub consists of four main components, including the first internal mRNA m7G database containing 44,058 experimentally-validated internal mRNA m7G sites, a sequence-based high-accuracy predictor, the first web server for assessing the impact of mutations on m7G status, and the first database recording 1,218 disease-associated genetic mutations that may function through regulation of m7G methylation. Together, m7GHub will serve as a useful resource for research on internal mRNA m7G modification. AVAILABILITY m7GHub is freely accessible online at www.xjtlu.edu.cn/biologicalsciences/m7ghub. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.SUMMARY Decoding the properties of immune repertoires is key to understanding the adaptive immune response to challenges such as viral infection. One important quantitative property is differential usage of Ig genes between biological conditions. Yet, most analyses for differential Ig gene usage are performed qualitatively or with inadequate statistical methods. Here we introduce IgGeneUsage, a computational tool for the analysis of differential Ig gene usage. IACS-10759 IgGeneUsage employs Bayesian inference with hierarchical models to analyze complex gene usage data from high-throughput sequencing experiments of immune repertoires. It quantifies differential Ig gene usage probabilistically and avoids some common problems related to the current practice of null-hypothesis significance testing. AVAILABILITY AND IMPLEMENTATION IgGeneUsage is an R-package freely available as part of Bioconductor at https//bioconductor.org/packages/IgGeneUsage/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Importance Timely eye care can prevent unnecessary vision loss. Objectives To estimate the number of US adults 18 years or older at high risk for vision loss in 2017 and to evaluate use of eye care services in 2017 compared with 2002. Design, Setting, and Participants This survey study used data from the 2002 (n = 30 920) and 2017 (n = 32 886) National Health Interview Survey, an annual, cross-sectional, nationally representative sample of US noninstitutionalized civilians. Analysis excluded respondents younger than 18 years and those who were blind or unable to see. Covariates included age, sex, race/ethnicity, marital status, educational level, income-to-poverty ratio, health insurance status, diabetes diagnosis, vision or eye problems, and US region of residence. Main Outcomes and Measures Three self-reported measures were visiting an eye care professional in the past 12 months, receiving a dilated eye examination in the past 12 months, and needing but being unable to afford eyeglasses in the past 12 month% (95% CI, 8.0%-9.5%) said they could not afford eyeglasses (compared with 51.1% [95% CI, 49.9%-52.3%], 52.4% [95% CI, 51.2%-53.6%], and 8.3% [95% CI, 7.7%-8.9%], respectively, in 2002). In 2017, individuals with lower income compared with high income were more likely to report eyeglasses as unaffordable (13.6% [95% CI, 11.6%-15.9%] compared with 5.7% [95% CI, 4.9%-6.6%]). Conclusions and Relevance Compared with data from 2002, more US adults were at high risk for vision loss in 2017. Although more adults used eye care, a larger proportion reported eyeglasses as unaffordable. Focusing resources on populations at high risk for vision loss, increasing awareness of the importance of eye care, and making eyeglasses more affordable could promote eye health, preserve vision, and reduce disparities.MOTIVATION A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This "n ≪ p" property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relations between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection. RESULTS To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data. AVAILABILITY The method is available at https//github.com/yunchuankong/forgeNet. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Importance Although retinal multimodal imaging is needed for diagnosing reticular pseudodrusen (RPD), the incidence of RPD in the general population typically has been assessed only using fundus photographs, which may underestimate their incidence. Objectives To describe the incidence of RPD using retinal color photographs, spectral-domain optical coherence tomography scans, fundus autofluorescence, and near-infrared reflectance images among individuals 77 years of age or older and to analyze the associated risk factors of RPD. Design, Setting, and Participants The ALIENOR (Antioxydants, Lipides Essentiels, Nutrition et Maladies Oculaires) Study is a cohort of French individuals 77 years of age or older. Data for this study were collected between February 22, 2011, and February 15, 2017, with a mean (SD) follow-up of 3.7 (1.0) years (range, 1.2-5.6 years). At baseline, 501 individuals were eligible to participate. Of 1002 eyes, 197 had prevalent RPD, advanced age-related macular degeneration, or ungradable images.