Virulence factor prediction in <Emphasis Type="Italic">Streptococcus pyogenes</Emphasis> using classification and clustering based on microarray data

Interesting biological information as, for example, gene expression data (microarrays), can be extracted from publicly available genomic data. As a starting point in order to narrow down the great possibilities of wet lab experiments, global high throughput data and available knowledge should be used to infer biological knowledge and emit biological hypothesis. Here, based on microarray data, we propose the use of cluster and classification methods that have become very popular and are implemented in freely available software in order to predict the participation in virulence mechanisms of different proteins coded by genes of the pathogen Streptococcus pyogenes. Confidence of predictions is based on classification errors of known genes and repetitive prediction by more than three methods. A special emphasis is done on the nonlinear kernel classification methods used. We propose a list of interesting candidates that could be virulence factors or that participate in the virulence process of S. pyogenes. Biological validations should start using this list of candidates as they show similar behavior to known virulence factors.     

Gene Set Enrichment Analysis (GSEA) of Toxoplasma gondii expression datasets links cell cycle progression and the bradyzoite developmental program

Background

Large amounts of microarray expression data have been generated for the Apicomplexan parasite Toxoplasma gondii in an effort to identify genes critical for virulence or developmental transitions. However, researchers’ ability to analyze this data is limited by the large number of unannotated genes, including many that appear to be conserved hypothetical proteins restricted to Apicomplexa. Further, differential expression of individual genes is not always informative and often relies on investigators to draw big-picture inferences without the benefit of context. We hypothesized that customization of gene set enrichment analysis (GSEA) to T. gondii would enable us to rigorously test whether groups of genes serving a common biological function are co-regulated during the developmental transition to the latent bradyzoite form.

Results

Using publicly available T. gondii expression microarray data, we created Toxoplasma gene sets related to bradyzoite differentiation, oocyst sporulation, and the cell cycle. We supplemented these with lists of genes derived from community annotation efforts that identified contents of the parasite-specific organelles, rhoptries, micronemes, dense granules, and the apicoplast. Finally, we created gene sets based on metabolic pathways annotated in the KEGG database and Gene Ontology terms associated with gene annotations available at http://www.toxodb.org. These gene sets were used to perform GSEA analysis using two sets of published T. gondii expression data that characterized T. gondii stress response and differentiation to the latent bradyzoite form.

Conclusions

GSEA provides evidence that cell cycle regulation and bradyzoite differentiation are coupled. Δgcn5A mutants unable to induce bradyzoite-associated genes in response to alkaline stress have different patterns of cell cycle and bradyzoite gene expression from stressed wild-type parasites. Extracellular tachyzoites resemble a transitional state that differs in gene expression from both replicating intracellular tachyzoites and in vitro bradyzoites by expressing genes that are enriched in bradyzoites as well as genes that are associated with the G1 phase of the cell cycle. The gene sets we have created are readily modified to reflect ongoing research and will aid researchers’ ability to use a knowledge-based approach to data analysis facilitating the development of new insights into the intricate biology of Toxoplasma gondii.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-515) contains supplementary material, which is available to authorized users.     

PAGE: Parametric Analysis of Gene Set Enrichment

Background  

Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.     

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.     

A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.     

limma powers differential expression analyses for RNA-sequencing and microarray studies

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.     

NuGO PPS1 mouse study 1: preliminary statistical analysis

Time course experiments are aimed at characterizing the dynamic regulation of gene expression in biological systems. Data are collected at different time points to monitor the dynamic behaviour of gene expression. The NuGO PPS Mouse Study 1 investigates the development of high fat-induced insulin resistance (IR) over time in APOE*3Leiden (E3L) mice. The study consists in a series of analyses at time points, which are crucial in the development of central and peripheral IR. Affymetrix arrays have been made on critical organs. We present the results of the preliminary statistical analysis on these microarray data. We used a non-parametric approach to identify genes the expression of which changed over time, separately for three tissues: liver, muscle and white adipose tissue. We specified for each gene a basic ANOVA model, in order to check the null hypothesis that gene expression did not vary over time. We addressed the multiple tests problem calculating positive false discovery rate and q values for the F test statistics. The appropriateness of the hypothesis of homogeneous variances over time was investigated by mean of the Bartlett’s test for homoschedasticity. This is a relevant point because heteroschedasticity could be indicative of outlying behaviour of some individuals at specific time points. The necessity to use a moderated F test was evaluated. We found that a considerable part of the genes varied expression over time. For part of the genes, the variance of the response was not homogeneous over time. Response differed by tissue.     

Bacterial artificial chromosome fingerprint arrays for the differentiation of transcriptomic differences in mycobacteria

Although microarray technology has become more widespread as a discovery tool for bacterial pathogenesis, it remains a method available only to laboratories with access to expensive equipment and costly analysis software. Mycobacterium tuberculosis, the causative agent for tuberculosis (TB), afflicts one-third of the global population, and kills between 2 and 3 million people per year. While the majority of cases of TB occur in developing areas of the world, facilities in these regions may not be able to support microarray analysis. Additionally, a major limitation of microarrays is that only genes on the array are being assayed. With acquired virulence and drug resistance in microbes, a method less dependent on a predetermined list of gene targets is advantageous. We present a method of expression analysis based on bacterial artificial chromosomes (BACs) that can be applied with standard laboratory equipment and free analysis software. This technique, bacterial artificial chromosome fingerprint arrays (BACFA), was developed and utilised to identify expression differences between intracellular strains of M. tuberculosis, one virulent (H37Rv) and one attenuated (H37Ra). Southern blots of restriction-enzyme digested BAC fragments were sequentially hybridised with strain-specific cDNA probes to generate expression profiles that were used to isolate expression differences in broth grown and intracellular bacteria. Repeat comparisons of intracellular profiles via BACFA identified genomic regions differentially expressed by the two strains. Quantitative real-time PCR was used to assess the genes located in these fragments in order to confirm or deny the differential regulation of genes. In total, we identified six genes that were differentially regulated between strains inside the host cell (pks2, aceE, Rv1571, and frdBCD). We report that BACFA is an effective technique in the expression analysis of bacteria and can be considered complementary to the high-throughput analysis offered by microarrays.     

Exploring the functional landscape of gene expression: directed search of large microarray compendia

MOTIVATION: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. RESULTS: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community. AVAILABILITY: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL. SUPPLEMENTARY INFORMATION: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement.     

Equalizer reduces SNP bias in Affymetrix microarrays

Background

Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray’s performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.

Results

By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.

Conclusions

The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.     

Bayesian hierarchical modeling for time course microarray experiments

Time course microarray experiments designed to characterize the dynamic regulation of gene expression in biological systems are becoming increasingly important. One critical issue that arises when examining time course microarray data is the identification of genes that show different temporal expression patterns among biological conditions. Here we propose a Bayesian hierarchical model to incorporate important experimental factors and to account for correlated gene expression measurements over time and over different genes. A new gene selection algorithm is also presented with the model to simultaneously identify genes that show changes in expression among biological conditions, in response to time and other experimental factors of interest. The algorithm performs well in terms of the false positive and false negative rates in simulation studies. The methodology is applied to a mouse model time course experiment to correlate temporal changes in azoxymethane-induced gene expression profiles with colorectal cancer susceptibility.