AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics.

With the current surge of spatial transcriptomics (ST) studies, researchers are exploring the deep interactive cell-play directly in tissues, in situ. However, with the current technologies, measurements consist of mRNA transcript profiles of mixed origin. Recently, applications have been proposed to tackle the deconvolution process, to gain knowledge about which cell types (SC) are found within. This is usually done by incorporating metrics from single-cell (SC) RNA, from similar tissues. Yet, most existing tools are cumbersome, and we found them hard to integrate and properly utilize. Therefore, we present AntiSplodge, a simple feed-forward neural-network-based pipeline designed to effective deconvolute ST profiles by utilizing synthetic ST profiles derived from real-life SC datasets. AntiSplodge is designed to be easy, fast and intuitive while still being lightweight. To demonstrate AntiSplodge, we deconvolute the human heart and verify correctness across time points. We further deconvolute the mouse brain, where spot patterns correctly follow that of the underlying tissue. In particular, for the hippocampus from where the cells originate. Furthermore, AntiSplodge demonstrates top of the line performance when compared to current state-of-the-art tools. Software availability: https://github.com/HealthML/AntiSplodge/.

Differential regulation of the DNA methylome in adults born during the Great Chinese Famine in 1959-1961.

BACKGROUND: Extensive epidemiological studies have established the association between exposure to early-life adversity and health status and diseases in adults. Epigenetic regulation is considered as a key mediator for this phenomenon but analysis on humans is sparse. The Great Chinese Famine lasting from 1958 to 1961 is a natural string of disasters offering a precious opportunity for elucidating the underlying epigenetic mechanism of the long-term effect of early adversity. METHODS: Using a high-throughput array platform for DNA methylome profiling, we conducted a case-control epigenome-wide association study on early-life exposure to Chinese famine in 79 adults born during 1959-1961 and compared to 105 unexposed subjects born 1963-1964. RESULTS: The single CpG site analysis of whole epigenome revealed a predominant pattern of decreased DNA methylation levels associated with fetal exposure to famine. Four CpG sites were detected with p < 1e-06 (linked to EHMT1, CNR1, UBXN7 and ESM1 genes), 16 CpGs detected with 1e-06 < p < 1e-05 and 157 CpGs with 1e-05 < p < 1e-04, with a predominant pattern of hypomethylation. Functional annotation to genes and their enriched biological pathways mainly involved neurodevelopment, neuropsychological disorders and metabolism. Multiple sites analysis detected two top-rank differentially methylated regions harboring RNF39 on chromosome 6 and PTPRN2 on chromosome 7, both showing epigenetic association with stress-related conditions. CONCLUSION: Early-life exposure to famine could mediate DNA methylation regulations that persist into adulthood with broad impacts in the activities of genes and biological pathways. Results from this study provide new clues to the epigenetic embedding of early-life adversity and its impacts on adult health.

Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins.

Cognitive aging is one of the major problems worldwide, especially as people get older. This study aimed to perform global gene expression profiling of cognitive function to identify associated genes and pathways and a novel transcriptional regulatory network analysis to identify important regulons. We performed single transcript analysis on 400 monozygotic twins using an assumption-free generalized correlation coefficient (GCC), linear mixed-effect model (LME) and kinship model and identified six probes (one significant at the standard FDR < 0.05 while the other results were suggestive with 0.18 ≤ FDR ≤ 0.28). We combined the GCC and linear model results to cover diverse patterns of relationships, and meaningful and novel genes like APOBEC3G, H6PD, SLC45A1, GRIN3B, and PDE4D were detected. Our exploratory study showed the downregulation of all these genes with increasing cognitive function or vice versa except the SLC45A1 gene, which was upregulated with increasing cognitive function. Linear models found only H6PD and SLC45A1, the other genes were captured by GCC. Significant functional pathways (FDR < 3.95e-10) such as focal adhesion, ribosome, cysteine and methionine metabolism, Huntington's disease, eukaryotic translation elongation, nervous system development, influenza infection, metabolism of RNA, and cell cycle were identified. A total of five regulons (FDR< 1.3e-4) were enriched in a transcriptional regulatory analysis in which CTCF and REST were activated and SP3, SRF, and XBP1 were repressed regulons. The genome-wide transcription analysis using both assumption-free GCC and linear models identified important genes and biological pathways implicated in cognitive performance, cognitive aging, and neurological diseases. Also, the regulatory network analysis revealed significant activated and repressed regulons on cognitive function.

Differential lncRNA expression profiling of cognitive function in middle and old aged monozygotic twins using generalized association analysis.

Cognitive impairment is the most prominent symptom in neurodegenerative disorders affecting quality of life and mortality. However, despite years of research, the molecular mechanism underlying the regulation of cognitive function and its impairment is poorly understood. This study aims to elucidate the role of long non-coding RNAs (lncRNAs) expression and lncRNA-mRNA interaction networks, by analyzing lncRNA expression in whole blood samples of 400 middle and old aged monozygotic twins in association with cognitive function using both linear models and a generalized correlation coefficient (GCC) to capture the diverse patterns of correlation. We detected 13 probes (p < 1e-03) displaying nonlinear and 7 probes (p < 1e-03) showing linear correlations. After combining the results, we identified 20 lncRNA probes with p < 1e-03. The top lncRNA probes were annotated to genes, along with the non-coding MALAT1, that play roles in neurodegenerative diseases. The top lncRNAs were linked to functional clusters including peptidyl-glycine modification, vascular smooth muscle cells, mitotic spindle organization and protein tyrosine phosphatase. In addition, mapping of the top significant lncRNAs to the lncRNA-mRNA interaction network detected significantly enriched biological pathways involving neuroactive ligand-receptor interaction, proteasome and chemokines. We show that GCC served as a complementary approach in detecting lncRNAs missed by the conventional linear models. A combination of GCC and linear models identified lncRNAs of diverse patterns of association enriched for GO biological and molecular functions meaningful in cognitive performance and cognitive decline. The novel lncRNA regulatory network further contributed to detect significant pathways implicated in cognition.

Novel DNA methylation marker discovery by assumption-free genome-wide association analysis of cognitive function in twins.

Privileged by rapid increase in available epigenomic data, epigenome-wide association studies (EWAS) are to make a profound contribution to understand the molecular mechanism of DNA methylation in cognitive aging. Current statistical methods used in EWAS are dominated by models based on multiple assumptions, for example, linear relationship between molecular profiles and phenotype, normal distribution for the methylation data and phenotype. In this study, we applied an assumption-free method, the generalized correlation coefficient (GCC), and compare it to linear models, namely the linear mixed model and kinship model. We use DNA methylation associated with a cognitive score in 400 and 206 twins as discovery and replication samples respectively. DNA methylation associated with cognitive function using GCC, linear mixed model, and kinship model, identified 65 CpGs (p < 1e-04) from discovery sample displaying both nonlinear and linear correlations. Replication analysis successfully replicated 9 of these top CpGs. When combining results of GCC and linear models to cover diverse patterns of relationships, we identified genes like KLHDC4, PAPSS2, and MRPS18B as well as pathways including focal adhesion, axon guidance, and some neurological signaling. Genomic region-based analysis found 15 methylated regions harboring 11 genes, with three verified in gene expression analysis, also the 11 genes were related to top functional clusters including neurohypophyseal hormone and maternal aggressive behaviors. The GCC approach detects valuable methylation sites missed by traditional linear models. A combination of methylation markers from GCC and linear models enriched biological pathways sensible in neurological function that could implicate cognitive performance and cognitive aging.

Genetic and environmental determinants of O6-methylguanine DNA-methyltransferase (MGMT) gene methylation: a 10-year longitudinal study of Danish twins.

BACKGROUND: Epigenetic inactivation of O6-methylguanine DNA-methyltransferase (MGMT) is associated with increased sensitivity to alkylating chemotherapeutic agents in glioblastoma patients. The genetic background underlying MGMT gene methylation may explain individual differences in treatment response and provide a clue to a personalized treatment strategy. Making use of the longitudinal twin design, we aimed, for the first time, to estimate the genetic contributions to MGMT methylation in a Danish twin cohort. METHODS: DNA-methylation from whole blood (18 monozygotic (MZ) and 25 dizygotic (DZ) twin pairs) repeated 10 years apart from the Longitudinal Study of Aging Danish Twins (LSADT) were used to search for genetic and environmental contributions to DNA-methylation at 170 CpG sites of across the MGMT gene. Both univariate and bivariate twin models were applied. The intraclass correlations, performed on cross-sectional data (246 MZ twin pairs) from an independent study population, the Middle-Aged Danish Twins (MADT), were used to assess the genetic influence at each CpG site of MGMT for replication. RESULTS: Univariate twin model revealed twelve CpG sites showing significantly high heritability at intake (wave 1, h2 > 0.43), and seven CpG sites with significant heritability estimates at end of follow-up (wave 2, h2 > 0.5). There were six significant CpG sites, located at the gene body region, that overlapped among the two waves (h2 > 0.5), of which five remained significant in the bivariate twin model, which was applied to both waves. Within MZ pair correlation in these six CpGs from MADT demarks top level of genetic influence. There were 11 CpGs constantly have substantial common environmental component over the 10 years. CONCLUSIONS: We have identified 6 CpG sites linked to the MGMT gene with strong and persistent genetic control based on their DNA methylation levels. The genetic basis of MGMT gene methylation could help to explain individual differences in glioblastoma treatment response and most importantly, provide references for mapping the methylation Quantitative Trait Loci (meQTL) underlying the genetic regulation.

A Double-Blind, Placebo-Controlled Parallel Group Study to Evaluate the Effect of a Single Oral Dose of 5-HT1A Antagonist GSK958108 on Ejaculation Latency Time in Male Patients Suffering From Premature Ejaculation.

BACKGROUND: Premature ejaculation (PE) is a common male neurobiological sexual disorder, related to a disturbance in central serotonin (5-hydroxytryptamine or 5-HT) neurotransmission. AIM: To assess the efficacy of a single oral dose of 5HT1A receptor antagonist GSK958108 on ejaculation latency time (ELT) in male subjects suffering from PE. METHODS: A total of 35 male subjects were enrolled in a Phase 1 double-blind, placebo-controlled, parallel group masturbation-model study. All subjects completed the study. No subject was withdrawn from the study. There were no major protocol deviations reported during the study. OUTCOMES: The primary outcome of the study was to evaluate the effect of a single oral dose of 5HT1A receptor antagonist GSK958108 on ELT as measured in the masturbation model; additionally, we investigated drug's safety and tolerability. RESULTS: In the 3 mg GSK958108 treatment group, the ELT was estimated to be 16% longer (1.542 vs 1.328, 95% CI: -16% to +61%) than if the subjects had taken placebo. In the 7 mg GSK958108 treatment group, the ELT was estimated to be 77% longer (2.346 vs 1.328, 95% CI: +28% to +144%) than in the placebo group. The systemic exposure to GSK958108 increased with dosage between 3 mg and 7 mg. A significant trend toward an increase of ELT was observed with increasing plasma concentrations of GSK958108. A total of 4 patients all treated with 7 mg dose experienced minor drug related adverse events (5 adverse events in 4 patients): somnolence (n = 3), headache (n = 1), tinnitus (n = 1). CLINICAL IMPLICATIONS: In the current double-blind, placebo-controlled parallel group study the 5HT1A receptor antagonist GSK958108 was tested in 3 mg and 7 mg doses for PE treatment in humans. It was shown that GSK958108 significantly delayed ejaculation showing a new and safe alternative in PE treatment. STRENGTHS & LIMITATIONS: The present study showed innovative results suggesting an important role of 5HT1A receptor antagonist in the PE treatment. However, the use of masturbation model and the small population are the main limitations of this investigation. CONCLUSION: 5HT1A receptor antagonist GSK958108 3 mg per day and 7 mg per day was found to be well-tolerated, safe and effective for the treatment of PE subjects and demonstrated a strong association between 5HT1A receptors and ejaculation control in humans (NCT00861484). Migliorini F, Tafuri A, Bettica P, et al. A Double-Blind, Placebo-Controlled Parallel Group Study to Evaluate the Effect of a Single Oral Dose of 5-HT1A Antagonist GSK958108 on Ejaculation Latency Time in Male Patients Suffering From Premature Ejaculation. J Sex Med 2021;18:63-71.

Enabling single-cell trajectory network enrichment.

Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.

EWASex: an efficient R-package to predict sex in epigenome-wide association studies.

SUMMARY: Epigenome-Wide Association Study (EWAS) has become a powerful approach to identify epigenetic variations associated with diseases or health traits. Sex is an important variable to include in EWAS to ensure unbiased data processing and statistical analysis. We introduce the R-package EWASex, which allows for fast and highly accurate sex-estimation using DNA methylation data on a small set of CpG sites located on the X-chromosome under stable X-chromosome inactivation in females. RESULTS: We demonstrate that EWASex outperforms the current state of the art tools by using different EWAS datasets. With EWASex, we offer an efficient way to predict and to verify sex that can be easily implemented in any EWAS using blood samples or even other tissue types. It comes with pre-trained weights to work without prior sex labels and without requiring access to RAW data, which is a necessity for all currently available methods. AVAILABILITY AND IMPLEMENTATION: The EWASex R-package along with tutorials, documentation and source code are available at https://github.com/Silver-Hawk/EWASex. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Generalized correlation coefficient for genome-wide association analysis of cognitive ability in twins.

Despite a strong genetic background in cognitive function only a limited number of single nucleotide polymorphisms (SNPs) have been found in genome-wide association studies (GWASs). We hypothesize that this is partially due to mis-specified modeling concerning phenotype distribution as well as the relationship between SNP dosage and the level of the phenotype. To overcome these issues, we introduced an assumption-free method based on generalized correlation coefficient (GCC) in a GWAS of cognitive function in Danish and Chinese twins to compare its performance with traditional linear models. The GCC-based GWAS identified two significant SNPs in Danish samples (rs71419535, p = 1.47e-08; rs905838, p = 1.69e-08) and two significant SNPs in Chinese samples (rs2292999, p = 9.27e-10; rs17019635, p = 2.50e-09). In contrast, linear models failed to detect any genome-wide significant SNPs. The number of top significant genes overlapping between the two samples in the GCC-based GWAS was higher than when applying linear models. The GCC model identified significant genetic variants missed by conventional linear models, with more replicated genes and biological pathways related to cognitive function. Moreover, the GCC-based GWAS was robust in handling correlated samples like twin pairs. GCC is a useful statistical method for GWAS that complements traditional linear models for capturing genetic effects beyond the additive assumption.

Differential long noncoding RNA profiling of BMI in twins.

Aim: Many efforts have been deployed to identify genetic variants associated with BMI. Alternatively, we explore epigenetic contribution to BMI variation by focusing on long noncoding RNAs (lncRNAs) which represents a key layer of epigenetic control. Materials & methods: We analyzed lncRNA expression in whole blood of 229 monozygotic twin pairs in association with BMI using generalized estimating equations. Results & conclusion: Six lncRNA probes were identified as significant (false discovery rate <0.05), with BMI showing causal effects on the expression of the significant lncRNAs. Functional annotation of differential profiles identified Gene ontology biological processes including kidney development, regulations of lipid biosynthetic process, circadian rhythm, notch signaling, etc. Whole blood lncRNAs are significantly expressed in response to BMI variation.

Exploratory analysis of age and sex dependent DNA methylation patterns on the X-chromosome in whole blood samples.

BACKGROUND: Large numbers of autosomal sites are found differentially methylated in the aging genome. Due to analytical difficulties in dealing with sex differences in X-chromosome content and X-inactivation (XCI) in females, this has not been explored for the X chromosome. METHODS: Using data from middle age to elderly individuals (age 55+ years) from two Danish cohorts of monozygotic twins and the Scottish Lothian Birth Cohort 1921, we conducted an X-chromosome-wide analysis of age-associated DNA methylation patterns with consideration of stably inferred XCI status. RESULTS: Through analysing and comparing sex-specific X-linked DNA methylation changes over age late in life, we identified 123, 293 and 55 CpG sites significant (FDR < 0.05) only in males, only in females and in both sexes of Danish twins. All findings were significantly replicated in the two Danish twin cohorts. CpG sites escaping XCI are predominantly de-methylated with increasing age across cohorts. In contrast, CpGs highly methylated in both sexes are methylated even further with increasing age. Among the replicated sites in Danish samples, 16 (13%), 24 (8.2%) and 3 (5.5%) CpGs were further validated in LBC1921 (FDR < 0.05). CONCLUSIONS: The X-chromosome of whole blood leukocytes displays age- and sex-dependent DNA methylation patterns in relation to XCI across cohorts.

Weighted Gene Coregulation Network Analysis of Promoter DNA Methylation on All-Cause Mortality in Old-Aged Birth Cohorts Finds Modules of High-Risk Associated Biomarkers.

Overall or all-cause mortality is a key measure of health in a population. Multiple epigenome-wide association studies have been conducted on all-cause mortality with limited significant findings and low replication. To elucidate the coregulated DNA methylation patterns associated with all-cause mortality, we conducted a weighted DNA methylation coregulation network analysis on whole-blood samples of 1,425 older individuals from the Lothian Birth Cohorts of 1921 and 1936. Our network-based analysis defined coregulated DNA methylation patterns in gene promoters into clusters or modules whose correlation with all-cause mortality was assessed by survival analysis. We found two significant modules or gene clusters associated with all-cause mortality in LBC1921 based on their eigengenes; one negatively correlated (p = 8.14E-03, 698 genes) and one positively correlated (p = 4.26E-02, 1,431 genes) with the risk of death. The two modules were replicated in LBC1936 with the same directions of correlation (p = 6.35E-02 and p = 3.64E-02, respectively). Furthermore, the modules revealed 32 genes associated with all-cause mortality (FDR < 0.05) linked to various diseases, including cancer and diabetes. Additionally, we performed pathway analysis and found 22 pathways (FDR < 0.05), including a pathway for taste transduction, which has been shown to be associated with poor prognosis in acutely hospitalized patients, and several pathways were linked to different types of cancer. The results from our network analysis show that DNA methylation of multiple genes could have been coregulated in an association with the overall risk of death. The identified epigenetic markers might help with our understanding of the molecular basis of all-cause mortality and general health.

Age-dependent DNA methylation patterns on the Y chromosome in elderly males.

The Y chromosome, a sex chromosome that only exists in males, has been ignored in traditional epigenetic association studies for multiple reasons. However, sex differences in aging-related phenotypes and mortality could suggest a critical role of the sex chromosomes in the aging process. We obtained blood-based DNA methylation data on the Y chromosome for 624 men from four cohorts and performed a chromosome-wide epigenetic association analysis to detect Y-linked CpGs differentially methylated over age and cross-validated the significant CpGs in the four cohorts. We identified 40-219 significant CpG sites (false discovery rate <0.05) with >82% of them hypermethylated with increasing age, which is in strong contrast to the patterns reported on the autosomal chromosomes. Comparing the rate of change in the Y-linked DNA methylation across cohorts that represent different age intervals revealed a trend of acceleration in DNA methylation with increasing age. The age-dependent DNA methylation patterns on the Y chromosome were further examined for their association with all-cause mortality with results suggesting that the predominant pattern of age-related hypermethylation on the Y chromosome is associated with reduced risk of death.

DNA methylome profiling in identical twin pairs discordant for body mass index.

OBJECTIVE: Body mass index (BMI) serves as an important measurement of obesity and adiposity, which are highly correlated with cardiometabolic diseases. Although high heritability has been estimated, the identified genetic variants by genetic association studies only explain a small proportion of BMI variation. As an active effort for further exploring the molecular basis of BMI variation, large-scale epigenome-wide association studies have been conducted but with limited number of loci reported, perhaps due to poorly controlled confounding factors, including genetic factors. Being genetically identical, monozygotic twins discordant for BMI are ideal subjects for analyzing the epigenetic association between DNA methylation and BMI, providing perfect control on their genetic makeups largely responsible for BMI variation. SUBJECTS: We performed an epigenome-wide association study on BMI using 30 identical twin pairs (15 male and 15 female pairs) with age ranging from 39 to 72 years and degree of BMI discordance ranging from 3-7.5 kg/m2. Methylation data from whole blood samples were collected using the reduced representation bisulfite sequencing technique. RESULTS: After adjusting for blood cell composition and clinical variables, we identified 136 CpGs with p-value < 1e-4, 30 CpGs with p < 1e-05 but no CpGs reached genome-wide significance. Genomic region-based analysis found 11 differentially methylated regions harboring coding and non-coding genes some of which were validated by gene expression analysis on independent samples. CONCLUSIONS: Our DNA methylation sequencing analysis on identical twins provides new references for the epigenetic regulation on BMI and obesity.

DNA methylome profiling of all-cause mortality in comparison with age-associated methylation patterns.

BACKGROUND: Multiple epigenome-wide association studies have been performed to identify DNA methylation patterns regulated by aging or correlated with risk of death. However, the inter-relatedness of the epigenetic basis of aging and mortality has not been well investigated. METHODS: Using genome-wide DNA methylation data from the Lothian Birth Cohorts, we conducted a genome-wide association analysis of all-cause mortality and compared this with age-associated methylation patterns reported on the same samples. RESULTS: Survival analysis using the Cox regression model identified 2552 CpG sites with genome-wide significance (false discovery rate < 0.05) for all-cause mortality. CpGs whose methylation levels are associated with increased mortality appear more distributed from the gene body to the intergenic regions whereas CpGs whose methylation levels are associated with decreased mortality is more concentrated at the promoter regions. In comparison with reported CpGs displaying significant age-dependent methylation patterns in the same samples, we observed a limited but highly significant overlap between mortality-associated and age-associated CpGs (p value 2.52e-06). Most importantly, the overlapping CpGs are dominated by those whose overall age-related methylation patterns reduce the risk of death. CONCLUSION: All-cause mortality is significantly associated with altered methylation at multiple genomic sites with differential distribution in gene regions for CpGs correlated with increased or decreased risk of death. The age-dependent methylation changes could reflect an active response to the aging process that contributes to maintain individual survival.

Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas.

In recent decades, the accumulation of data on 16s ribosomal RNA genes has yielded free and public databases such as SILVA, GreenGenes, The Ribosomal Database Project, and IMG, handling massive amounts of raw data and meta information. 16s rRNA gene contains hypervariable regions with great classification power. As a result, numerous classification tools have emerged including state-of-the-art tools such as Mothur, Qiime, and the 16s classifier. However, there is a gap between the sequence databases, the taxonomy profiling tools and available meta information such as geo/body-location information. Here, we present BioAtlas, and interactive web tool for searching, exploring, and analyzing prokaryotic distributions by integration of various resources of metagenomics databases. In the following section we show how to use BioAtlas to (1) search and explore prokaryote occurrences across the geospatial map of the world, (2) investigate and hunt for occurrences across generic user-generated surface-specific maps, with an example map of a human female, with data from Bouslimani et al., and (3) classify a user-given sequences dataset through our online platform for visual exploration of the spatial abundances of the identified microbes.

DNA Methylation and All-Cause Mortality in Middle-Aged and Elderly Danish Twins.

Several studies have linked DNA methylation at individual CpG sites to aging and various diseases. Recent studies have also identified single CpGs whose methylation levels are associated with all-cause mortality. In this study, we perform an epigenome-wide study of the association between CpG methylation and mortality in a population of 435 monozygotic twin pairs from three Danish twin studies. The participants were aged 55-90 at the time of blood sampling and were followed for up to 20 years. We validated our results by comparison with results from a British and a Swedish cohort, as well as results from the literature. We identified 2806 CpG sites associated with mortality (false discovery rate ( FDR ) < 0.05 ), of which 24 had an association p-value below 10 - 7 . This was confirmed by intra-pair comparison controlling for confounding effects. Eight of the 24 top sites could be validated in independent datasets or confirmed by previous studies. For all these eight sites, hypomethylation was associated with poor survival prognosis, and seven showed monozygotic correlations above 35%, indicating a potential moderate to strong heritability, but leaving room for substantial shared or unique environmental effects. We also set up a predictor for mortality using least absolute shrinkage and selection operator (LASSO) regression. The predictor showed good performance on the Danish data under cross-validation, but did not perform very well in independent samples.

Interactive microbial distribution analysis using BioAtlas.

Massive amounts of 16S rRNA sequencing data have been stored in publicly accessible databases, such as GOLD, SILVA, GreenGenes (GG), and the Ribosomal Database Project (RDP). Many of these sequences are tagged with geo-locations. Nevertheless, researchers currently lack a user-friendly tool to analyze microbial distribution in a location-specific context. BioAtlas is an interactive web application that closes this gap between sequence databases, taxonomy profiling and geo/body-location information. It enables users to browse taxonomically annotated sequences across (i) the world map, (ii) human body maps and (iii) user-defined maps. It further allows for (iv) uploading of own sample data, which can be placed on existing maps to (v) browse the distribution of the associated taxonomies. Finally, BioAtlas enables users to (vi) contribute custom maps (e.g. for plants or animals) and to map taxonomies to pre-defined map locations. In summary, BioAtlas facilitates map-supported browsing of public 16S rRNA sequence data and analyses of user-provided sequences without requiring manual mapping to taxonomies and existing databases. AVAILABILITY: http://bioatlas.compbio.sdu.dk/.

Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and MethylationEPIC data processing.

Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.html.