Control Genes and Variability: Absence of Ubiquitous Reference Transcripts in Diverse Mammalian Expression Studies

A flexible transcription initiation architecture and a uniform regulatory grammar buffer expression variability and the effects of genetic variants on essential genes while a rigid transcription initiation architecture and a diverse regulatory grammar ensure condition-specific responsiveness of other genes.

Association of transcriptional levels of folate-mediated one-carbon metabolism-related genes in cancer cell lines with drug treatment response

Microarray data are a rich source of information, containing the collected expression values of thousands of genes for well defined states of a cell or tissue. Vast amounts of data (thousands of arrays) are publicly available and ready for analysis, e.g. to scrutinise correlations between genes at the level of gene expression. The large variety of arrays available makes it possible to combine different independent experiments to extract new knowledge. Starting with a large set of data, relevant information can be isolated for further analysis. To extract the required information from data sets of such size and complexity requires an appropriate and powerful analysis method. In this study, we chose to use an unsupervised hierarchical clustering algorithm, Chaotic Map Clustering (CMC), in a coupled two-way approach to analyse such data. However, the clustering approach is intrinsically difficult, both in terms of the unknown structure of the data and interpretation of the clustering results. It is therefore critical to evaluate the quality of any unsupervised procedure for such a complex set of data and to validate the clustering results, separating those clusters that are due simply to noise or statistical fluctuations. We used a resampling method to perform this validation. The resampling procedure applies the clustering algorithm to a large number of random sub-samples of the original data matrix and, consequently, the whole process becomes computationally intensive and time consuming. Using Grid technology, we show that we can drastically speed up this process by distributing the clustering of each matrix to a separate worker node, and thus retrieve resampling results within a few hours instead of several days. Further, we offer an online service to cluster large microarray data sets and conduct the subsequent validation described in this paper.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or non-convex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetry-based K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.

The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions. To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.

<p>Supplementary figure 3. Work flow and filtering steps for gene selection following machine learning to discover EAC driver genes. (a) For cross validation we used all normalised reads from EdgeR as cpm. These were put through the R package CMA using four cross-validation methods to find driver genes between EAC and NDBE. Loodat (leave-one-out), Bootstrap, Monte Carlo, Five-cross validation was used. All four methods were compared and genes present in {greater than or equal to}2 methods were kept (n=52). These 52 genes were then investigated for differential expression in EAC vs NDBE (n=39). Gene ontology enrichment was run on this filtered set. Furthermore, genes from all 4 cross validations methods were used as a gene panel and validated in 2 independent micro array data sets comparing EAC vs NDBE (GSE37203 and GSE26886). Random forest analysis was conducted to identify the most important genes of the 12-gene panel using the combined microarray datasets, and subsequently tested on two separate, public microarrays of benign and malignant gastric and colonic tissues. (b) The RNA-seq expression of the selected 12-gene panel of MSMO1, ACAA1, COL17A1, CXCL2, MKNK2, AQP9, E2F3, DHCR24, CTSL, KLF4, PM20D2, CREB5 for EAC and NDBE. Y-axis is log2(cpm 1). (c) AUROC curves for the 4-gene signature on RNA-seq data (left) the two individual microarray data sets (right, bottom left) comparing EAC and NDBE that were used to identify the 4-gene signature. (bottom right) Independent microarray data set used to validate the 4-gene signature on (d) Decision tree of the 12-gene signature on colonic (left) and gastric (right) normal and tumor tissues identifying E2F3 and KLF4 as main classification drivers.</p>

BackgroundThe majority of preclinical biomedical research involves studies of males rather than females. It is thought that researchers have avoided females based on the idea that female traits are more variable than those of males because of cyclic variation in effects of ovarian hormones.MethodsTo test the assumption of inherently greater female variability, we analyzed 293 microarray datasets measuring gene expression in various tissues of mice and humans, comprising analysis of more than 5 million probes.ResultsMeta-analysis showed that on average, male gene expression is slightly more variable than that of females although the difference is small. We also tested if the X chromosome of humans shows greater variability in gene expression in males than in females, as might be expected because of hemizygous exposure of polymorphic X alleles but again found little sex difference.ConclusionOur analysis supports and extends previous studies reporting no overall greater phenotypic variability in females.Electronic supplementary materialThe online version of this article (doi:10.1186/s13293-015-0036-8) contains supplementary material, which is available to authorized users.

We evaluated wildlife population health from the perspective of statistical means vs. variances. We outlined the choices necessary to provide the framework for our study. These consisted of spatial and temporal boundaries (e.g., choice of sentinel species, populations, time frame), measurement techniques (molecular to population level), and appropriate statistical analyses. We chose to assess the health of 19 sea otter populations, located in the north Pacific from the Aleutian Islands, AK, to Santa Barbara, CA, and varying in population growth rates and length of occupancy. Our focal metric was gene expression (i.e., mRNA transcripts) data that we had previously generated across sea otter populations as a measure of population health. We used statistical methods with different approaches (i.e., means vs. variances) and examined the subsequent interpretive outcomes and how these influence our assessment of “health.” Interpretations based on analyses using variances versus means overlapped to some degree. In general, sea otter populations with low variation in gene expression were limited by food resources and at or near carrying capacity. In populations where the variation in gene expression was moderate or high, four out of five populations were increasing in abundance, or had been recently increasing. Where we had additional information on sources of stressors at the level of the population, we were able to draw inferences from those stressors to specific gene expression results. For example, gene expression patterns of sea otters from Western Prince William Sound were consistent with long term exposure to petroleum hydrocarbons, whereas in Kachemak Bay, patterns were consistent with exposure to algal toxins. Ultimately, determination of population or ecosystem health will be most informative when multiple metrics are examined across disciplines in the context of specific scenarios and goals.

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large ( = 1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX).

Despite evidence that differences in gene expression levels contribute significantly to phenotypic variation across individuals, there has been only limited effort to study gene expression variation in human tissue. To characterize expression variation in the normal human retina, we utilized a custom retinal microarray to analyze 33 normal retinas from 19 donors, aged 29-90 years. Statistical models were designed to separate and quantify biological and technical sources of variation, including age, gender, eye laterality, gene function and age-by-gender interaction. Although the majority of the 9406 genes analyzed showed relatively stable expression levels across different donors (for an average gene the expression level value of 95 out of a 100 individuals fell within a 1.23-fold range), 2.6% of genes showed significant donor-to-donor variation, with a false discovery rate of 10%. The mean expression ratio standard deviation was 0.15+/-0.8, log2, with a range of 0.09-0.99. Genes selectively expressed in photoreceptors showed higher expression variation than other gene classes. Gender, age and other donor-specific factors contributed significantly to the expression variation of multiple genes, and groups of genes with an age- and gender-associated expression pattern were identified. Our findings show that a significant fraction of gene expression variation in the normal human retina is attributable to identifiable biological factors. The greater expression variability of many genes central to retinal function (including photoreceptor-specific genes) may be partially explained by the dynamics of the vision process, and raises the possibility that photoreceptor gene expression levels may contribute to phenotypic diversity across normal adult retinas. In addition, as such diversity may result in different levels of disease susceptibility, exploring its sources may provide insights into the pathogenesis of retinal disease.

A genome-wide survey of Catharanthus roseus receptor-like kinase1-like kinases (CrRLK1Ls) in rice revealed that the pattern of expression by some CrRLK1Ls is controlled by drought or circadian rhythms. This is probably accomplished through the functioning of Gigantea ( OsGI ). Such findings provide a novel angle for using CrRLK1Ls to study the drought-stress response and circadian regulation. The 17 CrRLK1L members of a novel RLK family have been identified in Arabidopsis. Each carries a putative extracellular carbohydrate-binding malectin-like domain. However, their roles in rice, a widely consumed staple food, are not well understood. To investigate the functions of CrRLK1Ls in rice, we utilized phylogenomics data obtained through anatomical and diurnal meta-expression analyses. This information was integrated with a large set of public microarray data within the context of the rice CrRLK1L family phylogenic tree. Chromosomal locations indicated that 3 of 16 genes were tandem-duplicated, suggesting possible functional redundancy within this family. However, integrated diurnal expression showed functional divergence between two of three genes, i.e., peak expression was detected during the day for OsCrRLK1L2, but during the night for OsCrRLK1L3. We found it interesting that OsCrRLK1L2 expression was repressed in osgigantea (osgi) mutants, which suggests that it could function downstream of OsGI. Network analysis associated with OsCrRLK1L2 and OsGI suggested a novel circadian regulation mechanism mediated by OsGI. In addition, two of five OsCrRLK1Ls preferentially expressed in the roots were stimulated by drought, suggesting a potential role for this family in water-use efficiency. This preliminary identification of CrRLK1Ls and study of their expression in rice will facilitate further functional classifications and applications in plant production.

A question has been raised in several publications as to whether or not the expression levels or their logarithms for different genes are normally distributed. To answer this question would require a large data set where both biological variability and technological noise are present. An earlier attempt to test this assumption was limited to technical replicates and did not take multiplicity of tests into account when assessing the net results of goodness-of-fit testing. Therefore, the problem calls for further exploration. We applied several statistical tests to a large set of high-density oligonucleotide microarray data in order to systematically test for log-normality of expression levels for all the reporter genes. The multiple testing aspect of the problem was addressed by designing a pertinent resampling procedure. The results of testing did not reject normality of log-intensities in the non-normalized data under study. However, the global log-normality hypothesis was rejected beyond all reasonable doubt when the data were normalized by the quantile normalization procedure. Our results are consistent with the hypothesis that non-normalized expression levels of different genes are approximately log-normally distributed. The quantile normalization causes dramatic changes in the shape of marginal distributions of log-intensities which may be an indication that this procedure interferes not only in the technological noise but the true biological signal as well. This possibility invites a special investigation.

Many crucial decisions, such as the location and timing of cell division, cell-fate determination, and embryonic axes establishment, are made in the early embryo, a time in development when there is often little or no transcription. For this reason, the control of variation in gene expression in the early embryo often relies on post-transcriptional control of maternal genes. Although the early embryo is rife with translational control, controlling mRNA activity is also important in other developmental processes, such as stem-cell proliferation, sex determination, neurogenesis and erythropoiesis.

Heat-imposed crop failure is often attributed to reduced thermotolerance of floral tissues; however, the underlying mechanism remains unknown. Here, we demonstrate that m6A RNA methylation increases in Arabidopsis flowers and negatively regulates gene expression variability. Stochastic gene expression provides flexibility to cope with environmental stresses. We find that reduced transcriptional fluctuation is associated with compromised activation of heat-responsive genes. Moreover, disruption of an RNA demethylase AtALKBH10B leads to lower gene expression variability, suppression of heat-activated genes, and strong reduction of plant fertility. Our work proposes a novel role for RNA methylation in the bet-hedging strategy of heat stress response.