Fast approximate hierarchical clustering using similarity heuristics

© 2008 Kull and Vilo; licensee BioMed Central Ltd

DNA methylation changes in endometrium and correlation with gene expression during the transition from pre-receptive to receptive phase

The inner uterine lining (endometrium) is a unique tissue going through remarkable changes each menstrual cycle. Endometrium has its characteristic DNA methylation profile, although not much is known about the endometrial methylome changes throughout the menstrual cycle. The impact of methylome changes on gene expression and thereby on the function of the tissue, including establishing receptivity to implanting embryo, is also unclear. Therefore, this study used genome-wide technologies to characterize the methylome and the correlation between DNA methylation and gene expression in endometrial biopsies collected from 17 healthy fertile-aged women from pre-receptive and receptive phase within one menstrual cycle. Our study showed that the overall methylome remains relatively stable during this stage of the menstrual cycle, with small-scale changes affecting 5% of the studied CpG sites (22,272 out of studied 437,022 CpGs, FDR <0.05). Of differentially methylated CpG sites with the largest absolute changes in methylation level, approximately 30% correlated with gene expression measured by RNA sequencing, with negative correlations being more common in 5 ' UTR and positive correlations in the gene 'Body' region. According to our results, extracellular matrix organization and immune response are the pathways most affected by methylation changes during the transition from pre-receptive to receptive phase.Peer reviewe

G = MAT: Linking Transcription Factor Expression and DNA Binding Data

Transcription factors are proteins that bind to motifs on the DNA and thus affect gene expression regulation. The qualitative description of the corresponding processes is therefore important for a better understanding of essential biological mechanisms. However, wet lab experiments targeted at the discovery of the regulatory interplay between transcription factors and binding sites are expensive. We propose a new, purely computational method for finding putative associations between transcription factors and motifs. This method is based on a linear model that combines sequence information with expression data. We present various methods for model parameter estimation and show, via experiments on simulated data, that these methods are reliable. Finally, we examine the performance of this model on biological data and conclude that it can indeed be used to discover meaningful associations. The developed software is available as a web tool and Scilab source code at http://biit.cs.ut.ee/gmat/

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank

<div><p>The Estonian Biobank, governed by the Institute of Genomics at the University of Tartu (Biobank), has stored genetic material/DNA and continuously collected data since 2002 on a total of 52,274 individuals representing ~5% of the Estonian adult population and is increasing. To explore the utility of data available in the Biobank, we conducted a phenome-wide association study (PheWAS) in two areas of interest to healthcare researchers; asthma and liver disease. We used 11 asthma and 13 liver disease-associated single nucleotide polymorphisms (SNPs), identified from published genome-wide association studies, to test our ability to detect established associations. We confirmed 2 asthma and 5 liver disease associated variants at nominal significance and directionally consistent with published results. We found 2 associations that were opposite to what was published before (rs4374383:AA increases risk of NASH/NAFLD, rs11597086 increases ALT level). Three SNP-diagnosis pairs passed the phenome-wide significance threshold: rs9273349 and E06 (thyroiditis, p = 5.50x10^-8); rs9273349 and E10 (type-1 diabetes, p = 2.60x10^-7); and rs2281135 and K76 (non-alcoholic liver diseases, including NAFLD, p = 4.10x10^-7). We have validated our approach and confirmed the quality of the data for these conditions. Importantly, we demonstrate that the extensive amount of genetic and medical information from the Estonian Biobank can be successfully utilized for scientific research.</p></div

The FunGenES Database: A Genomics Resource for Mouse Embryonic Stem Cell Differentiation

Embryonic stem (ES) cells have high self-renewal capacity and the potential to differentiate into a large variety of cell types. To investigate gene networks operating in pluripotent ES cells and their derivatives, the “Functional Genomics in Embryonic Stem Cells” consortium (FunGenES) has analyzed the transcriptome of mouse ES cells in eleven diverse settings representing sixty-seven experimental conditions. To better illustrate gene expression profiles in mouse ES cells, we have organized the results in an interactive database with a number of features and tools. Specifically, we have generated clusters of transcripts that behave the same way under the entire spectrum of the sixty-seven experimental conditions; we have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in “Expression Waves” and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic resources. The FunGenES database provides a comprehensive resource for studies into the biology of ES cells

Genetic variation in the Estonian population : pharmacogenomics study of adverse drug effects using electronic health records

Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel lossof-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery