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We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.

Background: Ischemic stroke (IS) is a major health risk without generally usable effective measures of primary prevention. Early warning signals that are easy to detect and widely available can save lives. Estonia has one nation-wide Electronic Health Record (EHR) database for the storage of medical information of patients from hospitals and primary care providers. Methods: We extracted structured and unstructured data from the EHRs of participants of the Estonian Biobank (EstBB) and evaluated different formats of input data to understand how this continuously growing dataset should be prepared for best prediction. The utility of the EHR database for finding blood- and urine-based biomarkers for IS was demonstrated by applying different analytical and machine learning (ML) methods. Results: Several early trends in common clinical laboratory parameter changes (set of red blood indices, lymphocyte/neutrophil ratio, etc.) were established for IS prediction. The developed ML models predicted the future occurrence of IS with very high accuracy and Random Forests was proved as the most applicable method to EHR data. Conclusions: We conclude that the EHR database and the risk factors uncovered are valuable resources in screening the population for risk of IS as well as constructing disease risk scores and refining prediction models for IS by ML.

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 loss-of-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 CTNNA3was 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.

Given the possible critical importance of placental gene imprinting and random monoallelic expression on fetal and infant health, most of those genes must be identified, in order to understand the risks that the baby might meet during pregnancy and after birth. Therefore, the aim of the current study was to introduce a workflow and tools for analyzing imprinted and random monoallelic gene expression in human placenta, by applying whole-transcriptome (WT) RNA sequencing of placental tissue and genotyping of coding DNA variants in family trios. Ten family trios, each with a healthy spontaneous single-term pregnancy, were recruited. Total RNA was extracted for WT analysis, providing the full sequence information for the placental transcriptome. Parental and child blood DNA genotypes were analyzed by exome SNP genotyping microarrays, mapping the inheritance and estimating the abundance of parental expressed alleles. Imprinted genes showed consistent expression from either parental allele, as demonstrated by the SNP content of sequenced transcripts, while monoallelically expressed genes had random activity of parental alleles. We revealed 4 novel possible imprinted genes (LGALS8, LGALS14, PAPPA2and SPTLC3) and confirmed the imprinting of 4 genes (AIM1, PEG10, RHOBTB3and ZFAT-AS1) in human placenta. The major finding was the identification of 4 genes (ABP1, BCLAF1, IFI30and ZFAT) with random allelic bias, expressing one of the parental alleles preferentially. The main functions of the imprinted and monoallelically expressed genes included: i) mediating cellular apoptosis and tissue development; ii) regulating inflammation and immune system; iii) facilitating metabolic processes; and iv) regulating cell cycle.

A gene function prediction method suitable for the design of targeted RNAi libraries is described and used to predict chromosome condensation genes. Systematic experimental validation of candidate genes in a focused RNAi screen by automated microscopy and quantitative image analysis reveals many new chromosome condensation factors.

DNA epigenetic modifications, such as methylation, are important regulators of tissue differentiation, contributing to processes of both development and cancer. Profiling the tissue-specific DNA methylome patterns will provide novel insights into normal and pathogenic mechanisms, as well as help in future epigenetic therapies. In this study, 17 somatic tissues from four autopsied humans were subjected to functional genome analysis using the Illumina Infinium HumanMethylation450 BeadChip, covering 486 428 CpG sites. Only 2% of the CpGs analyzed are hypermethylated in all 17 tissue specimens; these permanently methylated CpG sites are located predominantly in gene-body regions. In contrast, 15% of the CpGs are hypomethylated in all specimens and are primarily located in regions proximal to transcription start sites. A vast number of tissue-specific differentially methylated regions are identified and considered likely mediators of tissue-specific gene regulatory mechanisms since the hypomethylated regions are closely related to known functions of the corresponding tissue. Finally, a clear inverse correlation is observed between promoter methylation within CpG islands and gene expression data obtained from publicly available databases. This genome-wide methylation profiling study identified tissue-specific differentially methylated regions in 17 human somatic tissues. Many of the genes corresponding to these differentially methylated regions contribute to tissue-specific functions. Future studies may use these data as a reference to identify markers of perturbed differentiation and disease-related pathogenic mechanisms.

Progress in understanding the genetic basis of panic attacks may extend current knowledge on susceptibility to panic and pathogenesis of panic disorder. In the present study we applied the microarray Illumina platform for whole genome expression profiling in healthy subjects participating in the CCK4induced panic test. The study sample consisted of 31 male and female healthy volunteers, who were categorized according to predefined criteria as “panickers” or “nonpanickers” to a CCK4 challenge. The gene expression profiles were measured on peripheral blood cells at baseline and at 120 min postCCK4 injection using Illumina Human6 v2 BeadChips. The fold change was used to demonstrate rate of changes in average gene expressions between studied groups. Statistical analyses were performed using the false discovery rate FDR. Gene expression profiling 2 hr postCCK4 challenge showed changes in transcriptional levels of 226 genes. A total of 61 genes were differentially expressed between panickers and nonpanickers with most of them related to immune, enzymatic or stress regulation systems. Other distinctive mRNA transcripts were from the genes known to be related to phenotypes associated with increased occurrence of panic attacks, such as asthma, diabetes, or myocardial ischemia. Our findings provide preliminary evidence for genetic substrates of panic attacks on the transcriptional level and indicate potential biological proximity between acute panicogenesis and several somatic conditions. © 2008 WileyLiss, Inc.

Motivation: Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts).Results: We have developed a data analysis method for comparing mRNA measurements of gene disruption experiments with information about gene promoters. The method was applied to a well–known dataset to uncover correlations between known transcription factor binding site motifs in the upstream regions of all S. cerevisiae genes and the gene expression changes in various gene disruption experiments. The possible explanations of the correlations were categorized and analyzed using e.g. expression cascades. Several correlations turned out to be consistent with existing biological knowledge while some new ones suggest themselves for further study.Availability: The resulting tables are available at http://www.cs.helsinki.fi/u/kpalin/CorrDisrupt/.Contact: kimmo.palin@cs.helsinki.fi

Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts).

G protein coupled receptors (GPCRs) are found in great numbers in most eukaryotic genomes. They are responsible for sensing a staggering variety of structurally diverse ligands, with their activation resulting in the initiation of a variety of cellular signalling cascades. The physiological response that is observed following receptor activation is governed by the guanine nucleotide-binding proteins (G proteins) to which a particular receptor chooses to couple. Previous investigations have demonstrated that the specificity of the receptor-G protein interaction is governed by the intracellular domains of the receptor. Despite many studies it has proven very difficult to predict de novo, from the receptor sequence alone, the G proteins to which a GPCR is most likely to couple. We have used a data-mining approach, combining pattern discovery with membrane topology prediction, to find patterns of amino acid residues in the intracellular domains of GPCR sequences that are specific for coupling to a particular functional class of G proteins. A prediction system was then built, being based upon these discovered patterns. We can report this approach was successful in the prediction of G protein coupling specificity of unknown sequences. Such predictions should be of great use in providing in silicocharacterisation of newly cloned receptor sequences and for improving the annotation of GPCRs stored in protein sequence databases. Available at: http://www.ebi.ac.uk/~croning/coupling.htmlContact: croning@ebi.ac.uk

G protein coupled receptors (GPCRs) are found in great numbers in most eukaryotic genomes. They are responsible for sensing a staggering variety of structurally diverse ligands, with their activation resulting in the initiation of a variety of cellular signalling cascades. The physiological response that is observed following receptor activation is governed by the guanine nucleotide-binding proteins (G proteins) to which a particular receptor chooses to couple. Previous investigations have demonstrated that the specificity of the receptor-G protein interaction is governed by the intracellular domains of the receptor. Despite many studies it has proven very difficult to predict de novo , from the receptor sequence alone, the G proteins to which a GPCR is most likely to couple. We have used a data-mining approach, combining pattern discovery with membrane topology prediction, to find patterns of amino acid residues in the intracellular domains of GPCR sequences that are specific for coupling to a particular functional class of G proteins. A prediction system was then built, being based upon these discovered patterns. We can report this approach was successful in the prediction of G protein coupling specificity of unknown sequences. Such predictions should be of great use in providing in silico characterisation of newly cloned receptor sequences and for improving the annotation of GPCRs stored in protein sequence databases. Available at: http://www.ebi.ac.uk/~croning/coupling.htmlContact: croning@ebi.ac.uk

Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices – tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We developed m:Explorer for identifying process-specific transcription factors (TFs) from multiple genome-wide sources, including transcriptome, DNA-binding and chromatin data. m:Explorer robustly outperforms similar techniques in finding cell cycle TFs in Saccharomyces cerevisiae. We predicted and experimentally tested regulators of quiescence (G0), a model of ageing, over a six-week time-course. We validated nine of top-12 predictions as novel G0TFs, including Δmga2, Δcst6, Δbas1with higher viability and G0-essential TFs Tup1, Swi3. Pathway analysis associates longevity to reduced growth, reprogrammed metabolism and cell wall remodeling. m:Explorer (http://biit.cs.ut.ee/mexplorer/) is instrumental in interrogating eukaryotic regulatory systems using heterogeneous data.

The current epidemic of obesity has caused a surge of interest in the study of adipose tissue formation. While major progress has been made in defining the molecular networks that control adipocyte terminal differentiation, the early steps of adipocyte development and the embryonic origin of this lineage remain largely unknown.

We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http://biit.cs.ut.ee/mem/.