Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

<p>Abstract</p> <p>Background</p> <p>Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand.</p> <p>Results</p> <p>EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data.</p> <p>Conclusion</p> <p>EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from <url>http://www.ezarray.com/</url>.</p

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p

Covert Genetic Selections to Optimize Phenotypes

In many high complexity systems (cells, organisms, institutions, societies, economies, etc.), it is unclear which components should be regulated to affect overall performance. To identify and prioritize molecular targets which impact cellular phenotypes, we have developed a selection procedure (“SPI”–single promoting/inhibiting target identification) which monitors the abundance of ectopic cDNAs. We have used this approach to identify growth regulators. For this purpose, complex pools of S. cerevisiae cDNA transformants were established and we quantitated the evolution of the spectrum of cDNAs which was initially present. These data emphasized the importance of translation initiation and ER-Golgi traffic for growth. SPI provides functional insight into the stability of cellular phenotypes under circumstances in which established genetic approaches cannot be implemented. It provides a functional “synthetic genetic signature” for each state of the cell (i.e. genotype and environment) by surveying complex genetic libraries, and does not require specialized arrays of cDNAs/shRNAs, deletion strains, direct assessment of clonal growth or even a conditional phenotype. Moreover, it establishes a hierarchy of importance of those targets which can contribute, either positively or negatively, to modify the prevailing phenotype. Extensions of these proof-of-principle experiments to other cell types should provide a novel and powerful approach to analyze multiple aspects of the basic biology of yeast and animal cells as well as clinically-relevant issues

Discovery and Validation of Molecular Biomarkers for Colorectal Adenomas and Cancer with Application to Blood Testing

BACKGROUND & AIMS: Colorectal cancer incidence and deaths are reduced by the detection and removal of early-stage, treatable neoplasia but we lack proven biomarkers sensitive for both cancer and pre-invasive adenomas. The aims of this study were to determine if adenomas and cancers exhibit characteristic patterns of biomarker expression and to explore whether a tissue-discovered (and validated) biomarker is differentially expressed in the plasma of patients with colorectal adenomas or cancer. METHODS: Candidate RNA biomarkers were identified by oligonucleotide microarray analysis of colorectal specimens (222 normal, 29 adenoma, 161 adenocarcinoma and 50 colitis) and validated in a previously untested cohort of 68 colorectal specimens using a custom-designed oligonucleotide microarray. One validated biomarker, KIAA1199, was assayed using qRT-PCR on plasma extracted RNA from 20 colonoscopy-confirmed healthy controls, 20 patients with adenoma, and 20 with cancer. RESULTS: Genome-wide analysis uncovered reproducible gene expression signatures for both adenomas and cancers compared to controls. 386/489 (79%) of the adenoma and 439/529 (83%) of the adenocarcinoma biomarkers were validated in independent tissues. We also identified genes differentially expressed in adenomas compared to cancer. KIAA1199 was selected for further analysis based on consistent up-regulation in neoplasia, previous studies and its interest as an uncharacterized gene. Plasma KIAA1199 RNA levels were significantly higher in patients with either cancer or adenoma (31/40) compared to neoplasia-free controls (6/20). CONCLUSIONS: Colorectal neoplasia exhibits characteristic patterns of gene expression. KIAA1199 is differentially expressed in neoplastic tissues and KIAA1199 transcripts are more abundant in the plasma of patients with either cancer or adenoma compared to controls

Mitochondrial Physiology and Gene Expression Analyses Reveal Metabolic and Translational Dysregulation in Oocyte-Induced Somatic Nuclear Reprogramming

While reprogramming a foreign nucleus after somatic cell nuclear transfer (SCNT), the enucleated oocyte (ooplasm) must signal that biomass and cellular requirements changed compared to the nucleus donor cell. Using cells expressing nuclear-encoded but mitochondria-targeted EGFP, a strategy was developed to directly distinguish maternal and embryonic products, testing ooplasm demands on transcriptional and post-transcriptional activity during reprogramming. Specifically, we compared transcript and protein levels for EGFP and other products in pre-implantation SCNT embryos, side-by-side to fertilized controls (embryos produced from the same oocyte pool, by intracytoplasmic injection of sperm containing the EGFP transgene). We observed that while EGFP transcript abundance is not different, protein levels are significantly lower in SCNT compared to fertilized blastocysts. This was not observed for Gapdh and Actb, whose protein reflected mRNA. This transcript-protein relationship indicates that the somatic nucleus can keep up with ooplasm transcript demands, whilst transcription and translation mismatch occurs after SCNT for certain mRNAs. We further detected metabolic disturbances after SCNT, suggesting a place among forces regulating post-transcriptional changes during reprogramming. Our observations ascribe oocyte-induced reprogramming with previously unsuspected regulatory dimensions, in that presence of functional proteins may no longer be inferred from mRNA, but rather depend on post-transcriptional regulation possibly modulated through metabolism

A origem das parcerias público-privada na governança global da educação

Durante a última década, a globalização da governança educacional por meio de parcerias público-privadas (PPP) tem gerado considerável debate quanto ao seu significado, propósito, status e resultados. Este debate é particularmente aquecido no setor da educação por causa da ampla aceitação da educação como atividade complexa, social e política que deve permanecer, em grande parte, se não totalmente, no setor público, servindo a interesses públicos. O artigo analisa a rápida expansão das parcerias público-privadas em educação (PPPE) articulada à introdução de regras de mercado no setor. Neste estudo nos concentramos sobre o papel de uma rede de desenvolvimento global, fundamental na globalização de um tipo particular de PPPE, indicando que a ideia de PPP encaixa-se em um projeto mais amplo de reconstituição da educação pública no âmbito do setor de serviços, a ser governada como parte da construção de uma sociedade de mercado.Over the past decade, the globalization and governing of education through Public Private Partnerships (PPPs) have generated considerable debate as to their meaning, purpose, status and outcomes. This debate is particularly heated in the education sector because of the widely-held view that education is a complex social and political activity that should remain largely, if not wholly, in the public sector serving public interests. The article analyses the rapid expansion of Education Public Private Partnerships (EPPPs) and the associated introduction of market rules into the education sector. We focus on the role of a key global development network in globalizing a particular kind of ePPPs, and show that the EPPP idea fi ts into a wider project of reconstituting public education as an education services industry to be governed as part of the construction of a market society

Saltatory remodeling of Hox chromatin in response to rostrocaudal patterning signals

Hox genes controlling motor neuron subtype identity are expressed in rostrocaudal patterns that are spatially and temporally collinear with their chromosomal organization. Here we demonstrate that Hox chromatin is subdivided into discrete domains that are controlled by rostrocaudal patterning signals that trigger rapid, domain-wide clearance of repressive histone H3 Lys27 trimethylation (H3K27me3) polycomb modifications. Treatment of differentiating mouse neural progenitors with retinoic acid leads to activation and binding of retinoic acid receptors (RARs) to the Hox1–Hox5 chromatin domains, which is followed by a rapid domain-wide removal of H3K27me3 and acquisition of cervical spinal identity. Wnt and fibroblast growth factor (FGF) signals induce expression of the Cdx2 transcription factor that binds and clears H3K27me3 from the Hox1–Hox9 chromatin domains, leading to specification of brachial or thoracic spinal identity. We propose that rapid clearance of repressive modifications in response to transient patterning signals encodes global rostrocaudal neural identity and that maintenance of these chromatin domains ensures the transmission of positional identity to postmitotic motor neurons later in development.Leona M. and Harry B. Helmsley Charitable TrustNational Institutes of Health (U.S.) (Grant P01 NS055923)Smith Family Foundatio

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts