Jaak Vilo

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

g:Profiler—a web server for functional interpretation of gene lists (2011 update)

Functional interpretation of candidate gene lists is an essential task in modern biomedical research. Here, we present the 2011 update of g:Profiler (http://biit.cs.ut.ee/gprofiler/), a popular collection of web tools for functional analysis. g:GOSt and g:Cocoa combine comprehensive methods for interpreting gene lists, ordered lists and list collections in the context of biomedical ontologies, pathways, transcription factor and microRNA regulatory motifs and protein–protein interactions. Additional tools, namely the biomolecule ID mapping service (g:Convert), gene expression similarity searcher (g:Sorter) and gene homology searcher (g:Orth) provide numerous ways for further analysis and interpretation. In this update, we have implemented several features of interest to the community: (i) functional analysis of single nucleotide polymorphisms and other DNA polymorphisms is supported by chromosomal queries; (ii) network analysis identifies enriched protein–protein interaction modules in gene lists; (iii) functional analysis covers human disease genes; and (iv) improved statistics and filtering provide more concise results. g:Profiler is a regularly updated resource that is available for a wide range of species, including mammals, plants, fungi and insects.

g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments

g:Profiler (http://biit.cs.ut.ee/gprofiler/) is a public web server for characterising and manipulating gene lists resulting from mining high-throughput genomic data. g:Profiler has a simple, user-friendly web interface with powerful visualisation for capturing Gene Ontology (GO), pathway, or transcription factor binding site enrichments down to individual gene levels. Besides standard multiple testing corrections, a new improved method for estimating the true effect of multiple testing over complex structures like GO has been introduced. Interpreting ranked gene lists is supported from the same interface with very efficient algorithms. Such ordered lists may arise when studying the most significantly affected genes from high-throughput data or genes co-expressed with the query gene. Other important aspects of practical data analysis are supported by modules tightly integrated with g:Profiler. These are: g:Convert for converting between different database identifiers; g:Orth for finding orthologous genes from other species; and g:Sorter for searching a large body of public gene expression data for co-expression. g:Profiler supports 31 different species, and underlying data is updated regularly from sources like the Ensembl database. Bioinformatics communities wishing to integrate with g:Profiler can use alternative simple textual outputs.

Protein Interaction Verification and Functional Annotation by Integrated Analysis of Genome-Scale Data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.

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/.

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/. Contact: vilo@ut.ee Supplementary information Supplementary data are available at Bioinformatics online.

DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns

BackgroundDNA 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.ResultsOnly 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.ConclusionsThis 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.

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.

Expression Profiler: next generation—an online platform for analysis of microarray data

Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.

MicroRNA Expression Profiles of Human Blood Monocyte-derived Dendritic Cells and Macrophages Reveal miR-511 as Putative Positive Regulator of Toll-like Receptor 4

Dendritic cells (DCs) and macrophages (MFs) are important multifunctional immune cells. Like other cell types, they express hundreds of different microRNAs (miRNAs) that are recently discovered post-transcriptional regulators of gene expression. Here we present updated miRNA expression profiles of monocytes, DCs and MFs. Compared with monocytes, ∼50 miRNAs were found to be differentially expressed in immature and mature DCs or MFs, with major expression changes occurring during the differentiation. Knockdown of DICER1, a protein needed for miRNA biosynthesis, led to lower DC-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) and enhanced CD14 protein levels, confirming the importance of miRNAs in DC differentiation in general. Inhibition of the two most highly up-regulated miRNAs, miR-511 and miR-99b, also resulted in reduced DC-SIGN level. Prediction of miRNA-511 targets revealed a number of genes with known immune functions, of which TLR4 and CD80 were validated using inhibition of miR-511 in DCs and luciferase assays in HEK293 cells. Interestingly, under the cell cycle arrest conditions, miR-511 seems to function as a positive regulator of TLR4. In conclusion, we have identified miR-511 as a novel potent modulator of human immune response. In addition, our data highlight that miRNA influence on gene expression is dependent on the cellular environment.