Priit Adler

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.

Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

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/.

GraphWeb: mining heterogeneous biological networks for gene modules with functional significance

Deciphering heterogeneous cellular networks with embedded modules is a great challenge of current systems biology. Experimental and computational studies construct complex networks of molecules that describe various aspects of the cell such as transcriptional regulation, protein interactions and metabolism. Groups of interacting genes and proteins reflect network modules that potentially share regulatory mechanisms and relate to common function. Here, we present GraphWeb, a public web server for biological network analysis and module discovery. GraphWeb provides methods to: (1) integrate heterogeneous and multispecies data for constructing directed and undirected, weighted and unweighted networks; (ii) discover network modules using a variety of algorithms and topological filters and (iii) interpret modules using functional knowledge of the Gene Ontology and pathways, as well as regulatory features such as binding motifs and microRNA targets. GraphWeb is designed to analyse individual or multiple merged networks, search for conserved features across multiple species, mine large biological networks for smaller modules, discover novel candidates and connections for known pathways and compare results of high-throughput datasets. The GraphWeb is available at http://biit.cs.ut.ee/graphweb/.

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.

KEGGanim: pathway animations for high-throughput data

MOTIVATION Gene expression analysis with microarrays has become one of the most widely used high-throughput methods for gathering genome-wide functional data. Emerging -omics fields such as proteomics and interactomics introduce new information sources. With the rise of systems biology, researchers need to concentrate on entire complex pathways that guide individual genes and related processes. Bioinformatics methods are needed to link the existing knowledge about pathways with the growing amounts of experimental data. RESULTS We present KEGGanim, a novel web-based tool for visualizing experimental data in biological pathways. KEGGanim produces animations and images of KEGG pathways using public or user uploaded high-throughput data. Pathway members are coloured according to experimental measurements, and animated over experimental conditions. KEGGanim visualization highlights dynamic changes over conditions and allows the user to observe important modules and key genes that influence the pathway. The simple user interface of KEGGanim provides options for filtering genes and experimental conditions. KEGGanim may be used with public or private data for 14 organisms with a large collection of public microarray data readily available. Most common gene and protein identifiers and microarray probesets are accepted for visualization input. AVAILABILITY http://biit.cs.ut.ee/KEGGanim/.

Comprehensive transcriptome analysis of mouse embryonic stem cell adipogenesis unravels new processes of adipocyte development

BackgroundThe 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.ResultsHere we performed genome-wide analysis of gene expression during adipogenesis of mouse embryonic stem cells (ESCs). We then pursued comprehensive bioinformatic analyses, including de novo functional annotation and curation of the generated data within the context of biological pathways, to uncover novel biological functions associated with the early steps of adipocyte development. By combining in-depth gene regulation studies and in silico analysis of transcription factor binding site enrichment, we also provide insights into the transcriptional networks that might govern these early steps.ConclusionsThis study supports several biological findings: firstly, adipocyte development in mouse ESCs is coupled to blood vessel morphogenesis and neural development, just as it is during mouse development. Secondly, the early steps of adipocyte formation involve major changes in signaling and transcriptional networks. A large proportion of the transcription factors that we uncovered in mouse ESCs are also expressed in the mouse embryonic mesenchyme and in adipose tissues, demonstrating the power of our approach to probe for genes associated with early developmental processes on a genome-wide scale. Finally, we reveal a plethora of novel candidate genes for adipocyte development and present a unique resource that can be further explored in functional assays.

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.

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.

Meta-signature of human endometrial receptivity: a meta-analysis and validation study of transcriptomic biomarkers

Previous transcriptome studies of the human endometrium have revealed hundreds of simultaneously up- and down-regulated genes that are involved in endometrial receptivity. However, the overlap between the studies is relatively small, and we are still searching for potential diagnostic biomarkers. Here we perform a meta-analysis of endometrial-receptivity associated genes on 164 endometrial samples (76 from ‘pre-receptive’ and 88 from mid-secretory, ‘receptive’ phase endometria) using a robust rank aggregation (RRA) method, followed by enrichment analysis, and regulatory microRNA prediction. We identify a meta-signature of endometrial receptivity involving 57 mRNA genes as putative receptivity markers, where 39 of these we confirm experimentally using RNA-sequencing method in two separate datasets. The meta-signature genes highlight the importance of immune responses, the complement cascade pathway and the involvement of exosomes in mid-secretory endometrial functions. Bioinformatic prediction identifies 348 microRNAs that could regulate 30 endometrial-receptivity associated genes, and we confirm experimentally the decreased expression of 19 microRNAs with 11 corresponding up-regulated meta-signature genes in our validation experiments. The 57 identified meta-signature genes and involved pathways, together with their regulatory microRNAs could serve as promising and sought-after biomarkers of endometrial receptivity, fertility and infertility.

Ranking Genes by Their Co-expression to Subsets of Pathway Members

Cellular processes are often carried out by intricate systems of interacting genes and proteins. Some of these systems are rather well studied and described in pathway databases, while the roles and functions of the majority of genes are poorly understood. A large compendium of public microarray data is available that covers a variety of conditions, samples, and tissues and provides a rich source for genome‐scale information. We focus our study on the analysis of 35 curated biological pathways in the context of gene co‐expression over a large variety of biological conditions. By defining a global co‐expression similarity rank for each gene and pathway, we perform exhaustive leave‐one‐out computations to describe existing pathway memberships using other members of the corresponding pathway as reference. We demonstrate that while successful in recovering biological base processes such as metabolism and translation, the global correlation measure fails to detect gene memberships in signaling pathways where co‐expression is less evident. Our results also show that pathway membership detection is more effective when using only a subset of corresponding pathway members as reference, supporting the existence of more tightly co‐expressed subsets of genes within pathways. Our study assesses the predictive power of global gene expression correlation measures in reconstructing biological systems of various functions and specificity. The developed computational network has immediate applications in detecting dubious pathway members and predicting novel member candidates.

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.