g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).

g:Profiler is a reliable and up-to-date functional enrichment analysis tool that supports various evidence types, identifier types and organisms. The toolset integrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings. g:Profiler provides multiple programmatic interfaces to access its functionality. These can be easily integrated into custom workflows and external tools, making them valuable resources for researchers who want to develop their own solutions. g:Profiler has been available since 2007 and is used to analyse millions of queries. Research reproducibility and transparency are achieved by maintaining working versions of all past database releases since 2015. g:Profiler supports 849 species, including vertebrates, plants, fungi, insects and parasites, and can analyse any organism through user-uploaded custom annotation files. In this update article, we introduce a novel filtering method highlighting Gene Ontology driver terms, accompanied by new graph visualizations providing a broader context for significant Gene Ontology terms. As a leading enrichment analysis and gene list interoperability service, g:Profiler offers a valuable resource for genetics, biology and medical researchers. It is freely accessible at https://biit.cs.ut.ee/gprofiler.

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).

Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.

PAWER: protein array web exploreR.

BACKGROUND: Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge. RESULTS: Here we present PAWER, the online web tool dedicated solely to protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through the user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows. CONCLUSIONS: PAWER is freely available at https://biit.cs.ut.ee/pawer .

Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions.

PURPOSE: Biomedical databases combining electronic medical records and phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations. METHODS: We developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia. RESULTS: Our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants. CONCLUSION: We find that microarrays are a cost-effective solution for creating preemptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.

Comparison of variation in frequency for SNPs associated with asthma or liver disease between Estonia, HapMap populations and the 1000 genome project populations.

Allele-specific analyses to understand frequency differences across populations, particularly populations not well studied, are important to help identify variants that may have a functional effect on disease mechanisms and phenotypic predisposition, facilitating new Genome-Wide Association Studies (GWAS). We aimed to compare the allele frequency of 11 asthma-associated and 16 liver disease-associated single nucleotide polymorphisms (SNPs) between the Estonian, HapMap and 1000 genome project populations. When comparing EGCUT with HapMap populations, the largest difference in allele frequencies was observed with the Maasai population in Kinyawa, Kenya, with 12 SNP variants reporting statistical significance. Similarly, when comparing EGCUT with 1000 genomes project populations, the largest difference in allele frequencies was observed with pooled African populations with 22 SNP variants reporting statistical significance. For 11 asthma-associated and 16 liver disease-associated SNPs, Estonians are genetically similar to other European populations but significantly different from African populations. Understanding differences in genetic architecture between ethnic populations is important to facilitate new GWAS targeted at underserved ethnic groups to enable novel genetic findings to aid the development of new therapies to reduce morbidity and mortality.

funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data.

BACKGROUND: A widely applied approach to extract knowledge from high-throughput genomic data is clustering of gene expression profiles followed by functional enrichment analysis. This type of analysis, when done manually, is highly subjective and has limited reproducibility. Moreover, this pipeline can be very time-consuming and resource-demanding as enrichment analysis is done for tens to hundreds of clusters at a time. Thus, the task often needs programming skills to form a pipeline of different software tools or R packages to enable an automated approach. Furthermore, visualising the results can be challenging. RESULTS: We developed a web tool, funcExplorer, which automatically combines hierarchical clustering and enrichment analysis to detect functionally related gene clusters. The functional characterisation is achieved using structured knowledge from data sources such as Gene Ontology, KEGG and Reactome pathways, Human Protein Atlas, and Human Phenotype Ontology. funcExplorer includes various measures for finding biologically meaningful clusters, provides a modern graphical user interface, and has wide-ranging data export and sharing options as well as software transparency by open-source code. The results are presented in a visually compact and interactive format, enabling users to explore the biological essence of the data. We compared our results with previously published gene clusters to demonstrate that funcExplorer can perform the data characterisation equally well, but without requiring labour-intensive manual interference. CONCLUSIONS: The open-source web tool funcExplorer enables scientists with high-throughput genomic data to obtain a preliminary interactive overview of the expression patterns, gene names, and shared functionalities in their dataset in a visually pleasing format. funcExplorer is publicly available at https://biit.cs.ut.ee/funcexplorer.

g:Profiler-a web server for functional interpretation of gene lists (2016 update).

Functional enrichment analysis is a key step in interpreting gene lists discovered in diverse high-throughput experiments. g:Profiler studies flat and ranked gene lists and finds statistically significant Gene Ontology terms, pathways and other gene function related terms. Translation of hundreds of gene identifiers is another core feature of g:Profiler. Since its first publication in 2007, our web server has become a popular tool of choice among basic and translational researchers. Timeliness is a major advantage of g:Profiler as genome and pathway information is synchronized with the Ensembl database in quarterly updates. g:Profiler supports 213 species including mammals and other vertebrates, plants, insects and fungi. The 2016 update of g:Profiler introduces several novel features. We have added further functional datasets to interpret gene lists, including transcription factor binding site predictions, Mendelian disease annotations, information about protein expression and complexes and gene mappings of human genetic polymorphisms. Besides the interactive web interface, g:Profiler can be accessed in computational pipelines using our R package, Python interface and BioJS component. g:Profiler is freely available at http://biit.cs.ut.ee/gprofiler/.

Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study.

BACKGROUND: Modern activity trackers, including the Fitbit Zip, enable the measurement of both the step count as well as physical activity (PA) intensities. However, there is a need for field-based validation studies in a variety of populations before using trackers for research. Therefore, the purpose of the current study was to investigate the validity of Fitbit Zip step count, moderate to vigorous physical activity (MVPA) and sedentary minutes, in different school segments in 3rd grade students. METHODS: Third grade students (N = 147, aged 9-10 years) wore a Fitbit Zip and an ActiGraph GT3x-BT accelerometer simultaneously on a belt for five days during school hours. The number of steps, minutes of MVPA and sedentary time during class time, physical education lessons and recess were extracted from both devices using time filters, based on the information from school time tables obtained from class teachers. The validity of the Fitbit Zip in different school segments was assessed using Bland-Altman analysis and Spearman's correlation. RESULTS: There was a strong correlation in the number of steps in all in-school segments between the two devices (r = 0.85-0.96, P < 0.001). The Fitbit Zip overestimated the number of steps in all segments, with the greatest overestimation being present in physical education lessons (345 steps). As for PA intensities, the agreement between the two devices in physical education and recess was moderate for MVPA minutes (r = 0.56 and r = 0.72, P < 0.001, respectively) and strong for sedentary time (r = 0.85 and r = 0.87, P < 0.001, respectively). During class time, the correlation was weak for MVPA minutes (r = 0.24, P < 0.001) and moderate for sedentary time (r = 0.57, P < 0.001). For total in-school time, the correlation between the two devices was strong for steps (r = 0.98, P < 0.001), MVPA (r = 0.80, P < 0.001) and sedentary time (r = 0.94, P < 0.001). CONCLUSION: In general, the Fitbit Zip can be considered a relatively accurate device for measuring the number of steps, MVPA and sedentary time in students in a school-setting. However, in segments where sedentary time dominates (e.g. academic classes), a research-grade accelerometer should be preferred.

seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data.

MOTIVATION: One of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach. RESULTS: We propose a novel method for DMR identification that detects the region boundaries according to the minimum description length (MDL) principle, essentially solving the problem of model selection. The significance of the regions is established using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches. AVAILABILITY AND IMPLEMENTATION: The methods have been implemented in R package seqlm that is available through Github: https://github.com/raivokolde/seqlm CONTACT: rkolde@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DosOpt: A Tool for Personalized Bayesian Dose Adjustment of Vancomycin in Neonates.

BACKGROUND: Our main aim has been to design a framework to improve vancomycin dosing in neonates. This required the development and verification of a computerized dose adjustment application, DosOpt, to guide the selection. METHODS: Model fitting in DosOpt uses Bayesian methods for deriving individual pharmacokinetic (PK) estimates from population priors and patient therapeutic drug monitoring measurements. These are used to simulate concentration-time curves and target-constrained dose optimization. DosOpt was verified by assessing bias and precision through several error metrics and normalized prediction distribution errors on samples simulated from the Anderson et al PK model. The performance of DosOpt was also evaluated using retrospective clinical data. Achieved probabilities of target concentration attainment were benchmarked against corresponding attainments in our clinical retrospective data set. RESULTS: Simulations showed no systemic forecast biases. Normalized prediction distribution error values of the base model were distributed by standardized Gaussian (P = 0.1), showing good model suitability. A retrospective test data set included 149 treatment episodes with 1-10 vancomycin concentration measurements per patient (median 2). Individual concentrations in PK estimation improved probability of target attainment and decreased the variance of the estimation. Including 3 individual concentrations in the kinetics estimation increased the probability of Ctrough attainment within 10-15 mg/L from 16% obtained with no individual data (95% confidence interval, 11%-24%) to 43% (21%-47%). CONCLUSIONS: DosOpt uses individual concentration data to estimate kinetics and find optimal doses that increase the probability of achieving desired trough concentrations. Its performance started to exceed target levels attained in retrospective clinical data sets with the inclusion of a single individual input concentration. This tool is freely available at http://www.biit.cs.ut.ee/DosOpt.

ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap.

The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.

TRIB3 enhances cell viability during glucose deprivation in HEK293-derived cells by upregulating IGFBP2, a novel nutrient deficiency survival factor.

Glucose deprivation occurs in several human diseases, including infarctions and solid tumors, and leads to cell death. In this article, we investigate the role of the pseudokinase Tribbles homolog 3 (TRIB3) in the cellular stress response to glucose starvation using cell lines derived from HEK293, which is highly glycolytic under standard conditions. Our results show that TRIB3 mRNA and protein levels are strongly upregulated in glucose-deprived cells via the induction of activating transcription factor 4 (ATF4) by the endoplasmic reticulum (ER) stress sensor kinase PERK. Cell survival in glucose-deficient conditions is enhanced by TRIB3 overexpression and reduced by TRIB3 knockdown. Genome-wide gene expression profiling uncovered approximately 40 glucose deprivation-responsive genes that are affected by TRIB3, including several genes involved in signaling processes and metabolism. Based on transcription factor motif analysis, the majority of TRIB3-downregulated genes are target genes of ATF4, which TRIB3 is known to inhibit. The gene most substantially upregulated by TRIB3 is insulin-like growth factor binding protein 2 (IGFBP2). IGFBP2 mRNA and protein levels are downregulated in cells subjected to glucose deprivation, and reduced IGFBP2 expression aggravates cell death during glucose deficiency, while overexpression of IGFBP2 prolongs cell survival. Moreover, IGFBP2 silencing abrogates the pro-survival effect of TRIB3. Since TRIB3 augments IGFBP2 expression in glucose-starved cells, the data indicate that IGFBP2 contributes to the attenuation of cell death by TRIB3. These results implicate TRIB3 and IGFBP2, both of which are known to be overexpressed in several types of cancers, as pro-survival modulators of cell viability in nutrient-deficient microenvironments.

FUN-L: gene prioritization for RNAi screens.

MOTIVATION: Most biological processes remain only partially characterized with many components still to be identified. Given that a whole genome can usually not be tested in a functional assay, identifying the genes most likely to be of interest is of critical importance to avoid wasting resources. RESULTS: Given a set of known functionally related genes and using a state-of-the-art approach to data integration and mining, our Functional Lists (FUN-L) method provides a ranked list of candidate genes for testing. Validation of predictions from FUN-L with independent RNAi screens confirms that FUN-L-produced lists are enriched in genes with the expected phenotypes. In this article, we describe a website front end to FUN-L. AVAILABILITY AND IMPLEMENTATION: The website is freely available to use at http://funl.org

Estimating differential expression from multiple indicators.

Regardless of the advent of high-throughput sequencing, microarrays remain central in current biomedical research. Conventional microarray analysis pipelines apply data reduction before the estimation of differential expression, which is likely to render the estimates susceptible to noise from signal summarization and reduce statistical power. We present a probe-level framework, which capitalizes on the high number of concurrent measurements to provide more robust differential expression estimates. The framework naturally extends to various experimental designs and target categories (e.g. transcripts, genes, genomic regions) as well as small sample sizes. Benchmarking in relation to popular microarray and RNA-sequencing data-analysis pipelines indicated high and stable performance on the Microarray Quality Control dataset and in a cell-culture model of hypoxia. Experimental-data-exhibiting long-range epigenetic silencing of gene expression was used to demonstrate the efficacy of detecting differential expression of genomic regions, a level of analysis not embraced by conventional workflows. Finally, we designed and conducted an experiment to identify hypothermia-responsive genes in terms of monotonic time-response. As a novel insight, hypothermia-dependent up-regulation of multiple genes of two major antioxidant pathways was identified and verified by quantitative real-time PCR.

A new way to protect privacy in large-scale genome-wide association studies.

MOTIVATION: Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues. RESULTS: We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Markov modeling for cost-effectiveness using federated health data network.

OBJECTIVE: To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. MATERIALS AND METHODS: We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. RESULTS: We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. DISCUSSION: Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. CONCLUSION: We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.

Fast probabilistic file fingerprinting for big data.

BACKGROUND: Biological data acquisition is raising new challenges, both in data analysis and handling. Not only is it proving hard to analyze the data at the rate it is generated today, but simply reading and transferring data files can be prohibitively slow due to their size. This primarily concerns logistics within and between data centers, but is also important for workstation users in the analysis phase. Common usage patterns, such as comparing and transferring files, are proving computationally expensive and are tying down shared resources. RESULTS: We present an efficient method for calculating file uniqueness for large scientific data files, that takes less computational effort than existing techniques. This method, called Probabilistic Fast File Fingerprinting (PFFF), exploits the variation present in biological data and computes file fingerprints by sampling randomly from the file instead of reading it in full. Consequently, it has a flat performance characteristic, correlated with data variation rather than file size. We demonstrate that probabilistic fingerprinting can be as reliable as existing hashing techniques, with provably negligible risk of collisions. We measure the performance of the algorithm on a number of data storage and access technologies, identifying its strengths as well as limitations. CONCLUSIONS: Probabilistic fingerprinting may significantly reduce the use of computational resources when comparing very large files. Utilisation of probabilistic fingerprinting techniques can increase the speed of common file-related workflows, both in the data center and for workbench analysis. The implementation of the algorithm is available as an open-source tool named pfff, as a command-line tool as well as a C library. The tool can be downloaded from http://biit.cs.ut.ee/pfff.

Meta-analysis of microRNA expression in lung cancer.

The prognostic and diagnostic value of microRNA (miRNA) expression aberrations in lung cancer has been studied intensely in recent years. However, due to the application of different technological platforms and small sample size, the miRNA expression profiling efforts have led to inconsistent results between the studies. We performed a comprehensive meta-analysis of 20 published miRNA expression studies in lung cancer, including a total of 598 tumor and 528 non-cancerous control samples. Using a recently published robust rank aggregation method, we identified a statistically significant miRNA meta-signature of seven upregulated (miR-21, miR-210, miR-182, miR-31, miR-200b, miR-205 and miR-183) and eight downregulated (miR-126-3p, miR-30a, miR-30d, miR-486-5p, miR-451a, miR-126-5p, miR-143 and miR-145) miRNAs. We conducted a gene set enrichment analysis to identify pathways that are most strongly affected by altered expression of these miRNAs. We found that meta-signature miRNAs cooperatively target functionally related and biologically relevant genes in signaling and developmental pathways. We have shown that such meta-analysis approach is suitable and effective solution for identification of statistically significant miRNA meta-signature by combining several miRNA expression studies. This method allows the analysis of data produced by different technological platforms that cannot be otherwise directly compared or in the case when raw data are unavailable.

Robust rank aggregation for gene list integration and meta-analysis.

MOTIVATION: The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task. RESULTS: Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings. AVAILABILITY: All the methods are implemented as a GNU R package RobustRankAggreg, freely available at the Comprehensive R Archive Network http://cran.r-project.org/.

Autoimmune regulator is acetylated by transcription coactivator CBP/p300.

The Autoimmune Regulator (AIRE) is a regulator of transcription in the thymic medulla, where it controls the expression of a large set of peripheral-tissue specific genes. AIRE interacts with the transcriptional coactivator and acetyltransferase CBP and synergistically cooperates with it in transcriptional activation. Here, we aimed to study a possible role of AIRE acetylation in the modulation of its activity. We found that AIRE is acetylated in tissue culture cells and this acetylation is enhanced by overexpression of CBP and the CBP paralog p300. The acetylated lysines were located within nuclear localization signal and SAND domain. AIRE with mutations that mimicked acetylated K243 and K253 in the SAND domain had reduced transactivation activity and accumulated into fewer and larger nuclear bodies, whereas mutations that mimicked the unacetylated lysines were functionally similar to wild-type AIRE. Analogously to CBP, p300 localized to AIRE-containing nuclear bodies, however, the overexpression of p300 did not enhance the transcriptional activation of AIRE-regulated genes. Further studies showed that overexpression of p300 stabilized the AIRE protein. Interestingly, gene expression profiling revealed that AIRE, with mutations mimicking K243/K253 acetylation in SAND, was able to activate gene expression, although the affected genes were different and the activation level was lower from those regulated by wild-type AIRE. Our results suggest that the AIRE acetylation can influence the selection of AIRE activated genes.