A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies.

BACKGROUND: Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking. RESULTS: Here we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH (Epigenetic Dissection of Intra-Sample Heterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking. CONCLUSIONS: Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods

Tissue-independent and tissue-specific patterns of DNA methylation alteration in cancer

BACKGROUND: There is growing evidence that DNA methylation alterations contribute to carcinogenesis. While cancer tissue exhibits widespread DNA methylation changes, the proportion of tissue-specific versus tissue-independent DNA methylation alterations in cancer is unclear. In addition, it is unknown which factors determine the patterns of aberrant DNA methylation in cancer. RESULTS: Using HumanMethylation450 BeadChips (450k), we here analyze genome-wide DNA methylation patterns of ten types of fetal tissue, in addition to matched normal-cancer data for corresponding tissue types, encompassing over 3000 samples. We demonstrate that the level of aberrant cancer DNA methylation in gene promoters and gene bodies is highly correlated between cancer types. We estimate that up to 60 % of the DNA methylation variation in a cancer genome of a given tissue type is explained by the corresponding variation in a cancer genome of another type, implying that much of the cancer DNA methylation landscape is tissue independent. We further show that histone marks in normal cells are better predictors of aberrant cancer DNA methylation than the corresponding signals in human embryonic stem cells. We build predictors of cancer DNA methylation patterns and show that although inclusion of three histone marks (H3K4me3, H3K27me3 and H3K36me3) improves model accuracy, the bivalent marks are the most predictive. Finally, we show that chromatin accessibility of gene promoters in normal tissue dictates the promoter's propensity to acquire aberrant DNA methylation in cancer in so far as it determines its level of DNA methylation in normal tissue. CONCLUSIONS: Our data show that a considerable fraction of the aberrant cancer DNA methylation landscape results from a mechanism that is largely tissue specific. Histone marks as specified in the normal cell of origin provide highly predictive models of aberrant cancer DNA methylation and outperform those derived from the same marks in hESCs

Sensing Social Media Signals for Cryptocurrency News

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.Comment: full version of the paper, that is accepted at ACM WWW '19 Conference, MSM'19 Worksho

EpiDISH web server: Epigenetic Dissection of Intra-Sample-Heterogeneity with online GUI

It is well recognized that cell-type heterogeneity hampers the interpretation of Epigenome-Wide Association Studies (EWAS). Many tools have emerged to address this issue, including several R/Bioconductor packages that infer cell-type composition. Here we present a web application for cell-type deconvolution, which offers the functionality of our EpiDISH Bioconductor/R package in a user-friendly GUI environment. Users can upload their data to infer cell-type composition and differentially methylated cytosines in individual cell-types (DMCTs) for a range of different tissues. Availability and implementation EpiDISH web server is implemented with Shiny in R, and is freely available at https://www.biosino.org/EpiDISH/

Risk factors for antenatal depression, postnatal depression and parenting stress

<p>Abstract</p> <p>Background</p> <p>Given that the prevalence of antenatal and postnatal depression is high, with estimates around 13%, and the consequences serious, efforts have been made to identify risk factors to assist in prevention, identification and treatment. Most risk factors associated with postnatal depression have been well researched, whereas predictors of antenatal depression have been less researched. Risk factors associated with early parenting stress have not been widely researched, despite the strong link with depression. The aim of this study was to further elucidate which of some previously identified risk factors are most predictive of three outcome measures: antenatal depression, postnatal depression and parenting stress and to examine the relationship between them.</p> <p>Methods</p> <p>Primipara and multiparae women were recruited antenatally from two major hoitals as part of the <it>beyondblue </it>National Postnatal Depression Program <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. In this subsidiary study, 367 women completed an additional large battery of validated questionnaires to identify risk factors in the antenatal period at 26–32 weeks gestation. A subsample of these women (N = 161) also completed questionnaires at 10–12 weeks postnatally. Depression level was measured by the Beck Depression Inventory (BDI).</p> <p>Results</p> <p>Regression analyses identified significant risk factors for the three outcome measures. (1). Significant predictors for antenatal depression: low self-esteem, antenatal anxiety, low social support, negative cognitive style, major life events, low income and history of abuse. (2). Significant predictors for postnatal depression: antenatal depression and a history of depression while also controlling for concurrent parenting stress, which was a significant variable. Antenatal depression was identified as a mediator between seven of the risk factors and postnatal depression. (3). Postnatal depression was the only significant predictor for parenting stress and also acted as a mediator for other risk factors.</p> <p>Conclusion</p> <p>Risk factor profiles for antenatal depression, postnatal depression and parenting stress differ but are interrelated. Antenatal depression was the strongest predictor of postnatal depression, and in turn postnatal depression was the strongest predictor for parenting stress. These results provide clinical direction suggesting that early identification and treatment of perinatal depression is important.</p

Epigenetic reprogramming of fallopian tube fimbriae in BRCA mutation carriers defines early ovarian cancer evolution

The exact timing and contribution of epigenetic reprogramming to carcinogenesis are unclear. Women harbouring BRCA1/2 mutations demonstrate a 30–40-fold increased risk of high-grade serous extra-uterine Müllerian cancers (HGSEMC), otherwise referred to as ‘ovarian carcinomas’, which frequently develop from fimbrial cells but not from the proximal portion of the fallopian tube. Here we compare the DNA methylome of the fimbrial and proximal ends of the fallopian tube in BRCA1/2 mutation carriers and non-carriers. We show that the number of CpGs displaying significant differences in methylation levels between fimbrial and proximal fallopian tube segments are threefold higher in BRCA mutation carriers than in controls, correlating with overexpression of activation-induced deaminase in their fimbrial epithelium. The differentially methylated CpGs accurately discriminate HGSEMCs from non-serous subtypes. Epigenetic reprogramming is an early pre-malignant event integral to BRCA1/2 mutation-driven carcinogenesis. Our findings may provide a basis for cancer-preventative strategies

Integrative analysis of 3604 GWAS reveals multiple novel cell type-specific regulatory associations

BACKGROUND: Genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are known to preferentially co-locate to active regulatory elements in tissues and cell types relevant to disease aetiology. Further characterisation of associated cell type-specific regulation can broaden our understanding of how GWAS signals may contribute to disease risk. RESULTS: To gain insight into potential functional mechanisms underlying GWAS associations, we developed FORGE2 ( https://forge2.altiusinstitute.org/ ), which is an updated version of the FORGE web tool. FORGE2 uses an expanded atlas of cell type-specific regulatory element annotations, including DNase I hotspots, five histone mark categories and 15 hidden Markov model (HMM) chromatin states, to identify tissue- and cell type-specific signals. An analysis of 3,604 GWAS from the NHGRI-EBI GWAS catalogue yielded at least one significant disease/trait-tissue association for 2,057 GWAS, including > 400 associations specific to epigenomic marks in immune tissues and cell types, > 30 associations specific to heart tissue, and > 60 associations specific to brain tissue, highlighting the key potential of tissue- and cell type-specific regulatory elements. Importantly, we demonstrate that FORGE2 analysis can separate previously observed accessible chromatin enrichments into different chromatin states, such as enhancers or active transcription start sites, providing a greater understanding of underlying regulatory mechanisms. Interestingly, tissue-specific enrichments for repressive chromatin states and histone marks were also detected, suggesting a role for tissue-specific repressed regions in GWAS-mediated disease aetiology. CONCLUSION: In summary, we demonstrate that FORGE2 has the potential to uncover previously unreported disease-tissue associations and identify new candidate mechanisms. FORGE2 is a transparent, user-friendly web tool for the integrative analysis of loci discovered from GWAS

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty

In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories

Using fractional exhaled nitric oxide (FeNO) to diagnose steroid-responsive disease and guide asthma management in routine care

Acknowledgements We thank Robin Taylor for his informative thinking and publications on FeNO, which have helped to influence and direct the thinking of the authors. Funding Extraction of the real-life dataset was funded by Research in Real Life Limited, the analysis of the dataset and the writing of this manuscript were co-funded (50:50) by Research in Real Life Limited and Aerocrine.Peer reviewedPublisher PD