Tomas Hruz

Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation.

RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization

BackgroundRT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts.ResultsUsing the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions.ConclusionWe therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://www.genevestigator.com.

Genevestigator Transcriptome Meta-Analysis and Biomarker Search using Rice and Barley Gene Expression Databases

The wide-spread use of microarray technologies to study plant transcriptomes has led to important discoveries and to an accumulation of profiling data covering a wide range of different tissues, developmental stages, perturbations, and genotypes. Querying a large number of microarray experiments can provide insights that cannot be gained by analyzing single experiments. However, such a meta-analysis poses significant challenges with respect to data comparability and normalization, systematic sample annotation, and analysis tools. Genevestigator addresses these issues using a large curated expression database and a set of specifically developed analysis tools that are accessible over the internet. This combination has already proven to be useful in the area of plant research based on a large set of Arabidopsis data (Grennan, 2006). Here, we present the release of the Genevestigator rice and barley gene expression databases that contain quality-controlled and well annotated microarray experiments using ontologies. The databases currently comprise experiments from pathology, plant nutrition, abiotic stress, hormone treatment, genotype, and spatial or temporal analysis, but are expected to cover a broad variety of research areas as more experimental data become available. The transcriptome meta-analysis of the model species rice and barley is expected to deliver results that can be used for functional genomics and biotechnological applications in cereals.

Web-based analysis of the mouse transcriptome using Genevestigator

BackgroundGene function analysis often requires a complex and laborious sequence of laboratory and computer-based experiments. Choosing an effective experimental design generally results from hypotheses derived from prior knowledge or experimentation. Knowledge obtained from meta-analyzing compendia of expression data with annotation libraries can provide significant clues in understanding gene and network function, resulting in better hypotheses that can be tested in the laboratory.DescriptionGenevestigator is a microarray database and analysis system allowing context-driven queries. Simple but powerful tools allow biologists with little computational background to retrieve information about when, where and how genes are expressed. We manually curated and quality-controlled 3110 mouse Affymetrix arrays from public repositories. Data queries can be run against an annotation library comprising 160 anatomy categories, 12 developmental stage groups, 80 stimuli, and 182 genetic backgrounds or modifications. The quality of results obtained through Genevestigator is illustrated by a number of biological scenarios that are substantiated by other types of experimentation in the literature.ConclusionThe Genevestigator-Mouse database effectively provides biologically meaningful results and can be accessed at https://www.genevestigator.ethz.ch.

Global regulatory architecture of human, mouse and rat tissue transcriptomes

BackgroundPredicting molecular responses in human by extrapolating results from model organisms requires a precise understanding of the architecture and regulation of biological mechanisms across species.ResultsHere, we present a large-scale comparative analysis of organ and tissue transcriptomes involving the three mammalian species human, mouse and rat. To this end, we created a unique, highly standardized compendium of tissue expression. Representative tissue specific datasets were aggregated from more than 33,900 Affymetrix expression microarrays. For each organism, we created two expression datasets covering over 55 distinct tissue types with curated data from two independent microarray platforms. Principal component analysis (PCA) revealed that the tissue-specific architecture of transcriptomes is highly conserved between human, mouse and rat. Moreover, tissues with related biological function clustered tightly together, even if the underlying data originated from different labs and experimental settings. Overall, the expression variance caused by tissue type was approximately 10 times higher than the variance caused by perturbations or diseases, except for a subset of cancers and chemicals. Pairs of gene orthologs exhibited higher expression correlation between mouse and rat than with human. Finally, we show evidence that tissue expression profiles, if combined with sequence similarity, can improve the correct assignment of functionally related homologs across species.ConclusionThe results demonstrate that tissue-specific regulation is the main determinant of transcriptome composition and is highly conserved across mammalian species.

ExpressionData - A public resource of high quality curated datasets representing gene expression across anatomy, development and experimental conditions

Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, several reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions.Here, we describe a new type of standardized datasets representative for the spatial and temporal dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments. Expression data is aggregated by anatomical part or stage of development to yield a representative transcriptome for each category. For example, we created a genome-wide expression dataset representing the FDA tissue panel across 35 tissue types. The proposed datasets were created for human and several model organisms and are publicly available at http://www.expressiondata.org.

Higher-order distributions and nongrowing complex networks without multiple connections

We study stochastic processes that generate nongrowing complex networks without self-loops and multiple edges (simple graphs). The work concentrates on understanding and formulation of constraints which keep the rewiring stochastic processes within the class of simple graphs. To formulate these constraints a different concept of wedge distribution (paths of length 2) is introduced and its relation to degree-degree correlation is studied. The analysis shows that the constraints, together with edge selection rules, do not even allow the formulation of a closed master equation in the general case. We also introduce a particular stochastic process which does not contain edge selection rules, but which, we believe, can provide some insight into the complexities of simple graphs.

A simple framework for the generalized nearest neighbor problem

The problem of finding a nearest neighbor from a set of points in ℝd to a complex query object has attracted considerable attention due to various applications in computational geometry, bio-informatics, information retrieval, etc. We propose a generic method that solves the problem for various classes of query objects and distance functions in a unified way. Moreover, for linear space requirements the method simplifies the known approach based on ray-shooting in the lower envelope of an arrangement.

Nongrowing Preferential Attachment Random Graphs

Abstract We consider an edge rewiring process that is widely used to model the dynamics of scale-free weblike networks. This process uses preferential attachment and operates on sparse multigraphs with n vertices and m edges. We prove that its mixing time is optimal and develop a framework that simplifies the calculation of graph properties in the steady state. The applicability of this framework is demonstrated by calculating the degree distribution, the number of self-loops, and the threshold for the appearance of the giant component.

Reducing Java internet project risks: a case study of public measurement of client component functionality in the user community

A major risk for Internet software projects which have server and client components are decisions related to availability and features on client computers in the user community. Specifically, bioinformatics software developers intending to use Java face critical decisions about which Java version to implement, but few statistics are available about Java presence on user machines. To obtain this information, we implemented a measurement system to detect the presence, functionality and version of Java Virtual Machines on client computers of a large base of users from the biology community. We show that our system effectively collects the necessary information and provides decision-relevant statistics. Measurements performed on 1753 client computers showed that Java presence is high and dominated by the most recent Java versions. The proposed empirical approach can be used to reduce decision risks in any type of Internet software project with low level of control on client equipment and high demands on client interaction and performance. More details together with source code and measurement results can be obtained from the J-vestigator survey page (https://www.genevestigator.ethz.ch/index.php?page=jvestigator)