Changes in RNA concentrations in Ad5-infected HFFs determined by RT-PCR

<p><b>Copyright information:</b></p><p>Taken from "Adenovirus type 5 exerts genome-wide control over cellular programs governing proliferation, quiescence, and survival"</p><p>Genome Biology 2007;8(4):R58-R58.</p><p>Published online 12 Apr 2007</p><p>PMCID:PMC1896011.</p><p></p> Autoradiograms of products of reverse transcription (RT)-polymerase chain reaction (PCR) amplification of CDC6 RNA isolated from human foreskin fibroblasts (HFFs) infected for the periods indicated. The RNA samples used in experiment 1 were those also used for amplification and hybridization to microarrays, whereas experiments 2 and 3 total RNAs were from two other independent infections of quiescent HFFs. The RT-PCR signals for E2F2 and RHOQ RNAs from the three independent infections were quantified, as described in Materials and methods, zero transformed against the mean of the three zero time point samples included in each experiment, and converted to logvalues for comparison to the changes in concentration of these RNAs determined by hybridization to microarrays. Ad5, adenovirus type 5

Ad5 induced changes of expression of E2F target genes organized by function

<p><b>Copyright information:</b></p><p>Taken from "Adenovirus type 5 exerts genome-wide control over cellular programs governing proliferation, quiescence, and survival"</p><p>Genome Biology 2007;8(4):R58-R58.</p><p>Published online 12 Apr 2007</p><p>PMCID:PMC1896011.</p><p></p> E2F target genes, identified by Ren and coworkers [86], were organized by cellular function. Genes significantly regulated by Ad5 (those that pass fold change filter applied previously) are indicated with red/UP or green/DOWN. Grey indicates genes not significantly regulated by Ad5. (Note that none of the 67 E2F target genes were significantly downregulated.

Changes in expression of direct p53 target genes induced by Ad5 infection

<p><b>Copyright information:</b></p><p>Taken from "Adenovirus type 5 exerts genome-wide control over cellular programs governing proliferation, quiescence, and survival"</p><p>Genome Biology 2007;8(4):R58-R58.</p><p>Published online 12 Apr 2007</p><p>PMCID:PMC1896011.</p><p></p> The 50 or so genes that are direct p53 targets in human lung fibroblasts [94] are shown clustered based on the changes in their expression observed in adenovirus type 5 (Ad5) infected human foreskin fibroblasts. The column labeled p53 summarizes the p53 induced alterations in expression of these genes, which are listed at the right. Ramps above panels indicate increases in time after infection

Comparison of adenovirus-induced changes in gene expression in HeLa cells and HFFs

<p><b>Copyright information:</b></p><p>Taken from "Adenovirus type 5 exerts genome-wide control over cellular programs governing proliferation, quiescence, and survival"</p><p>Genome Biology 2007;8(4):R58-R58.</p><p>Published online 12 Apr 2007</p><p>PMCID:PMC1896011.</p><p></p> The genes reported to exhibit changes in expression at 6 hours or 10 and 21 hours after infection of HeLa cells by adenovirus type 2 (Ad2) [54,55] were isolated from our dataset and clustered on the basis of their responses to infection of human foreskin fibroblasts (HFFs). The changes observed in HeLa cells are summarized in the columns labeled Ad2, in which yellow and blue represent increased and decreased expression respectively. In panel b, the HeLa response is based on the average of the two time points. Ramps above panels indicate increases in time after infection

Kinetic patterns of expression of Ad5-responsive genes and associated cellular functions

<p><b>Copyright information:</b></p><p>Taken from "Adenovirus type 5 exerts genome-wide control over cellular programs governing proliferation, quiescence, and survival"</p><p>Genome Biology 2007;8(4):R58-R58.</p><p>Published online 12 Apr 2007</p><p>PMCID:PMC1896011.</p><p></p> The logexpression values of the 2,106 genes that passed the filters described in the text clustered into eight groups are shown at the left, and over-represented Gene Ontology (GO) terms in each cluster at the right. Also shown is a color-bar relating both logratios and fold changes (relative to the average zero values) to color intensity. Ad5, adenovirus type 5

Integrating Co-Expression Networks with GWAS to Detect Causal Genes Driving Elemental Accumulation in Maize

Genome wide association studies (GWAS) have identified thousands of loci linked to hundreds of traits in many different species. However, in many cases, the causal genes and and the cellular processes they contribute to, remain unknown. This problem is even more pronounced in non-model species where functional annotations are sparse and there is poor resolution in single nucleotide polymorphism (SNP) boundaries. The vast amounts of data available from high throughput sequencing, such as RNA-Seq, are a tantalizing resource to leverage in identifying potential candidates under GWAS SNPs, though are often underutilized or difficult to interpret. To mitigate these issues, here, we systematically integrate whole genome SNP data with functional information derived from gene co-expression networks using a computational framework called Camoco.<br>Camoco scores interactions among genes near GWAS peaks and establishes significance using a robust bootstrapping model. We demonstrate the precision of our method by simulating GWA studies using Gene Ontology (GO) terms. We then used our method to functionally inter-relate loci identified in a large scale, GWA study characterizing elemental accumulation in maize kernels. Our results demonstrate that simply taking the closest genes to significant GWAS SNPs will often lead to spurious results demonstrating the need for proper functional modeling and bootstrapping. Additionally, when deriving functional information from gene transcriptional networks, the biological context from which the transcription was measured is important. Inclusion of gene expression data from tissues not relevant to the elemental phenotypes collected abolishes the relationships between the co-expression networks and the GWAS SNPs. In the correct biological context, genes linked to GWAS hits for elemental accumulation were more significantly co-expressed than genes within similarly structured GO terms. Our framework provides a method to systematically evaluate the putative functional relationships among GWAS candidate loci as well as to efficiently prioritize gene lists produced from GWA studies

Relative performance of different methods with regard to the test set and novel set on GO biological process terms (size 101 to 300)

The relative performance of individual groups differs between the test set and novel set. In addition, the performance on the novel set was generally worse than on the test set. This indicates that cross-validation should be used carefully in assessing the relative performance of different algorithms and that evaluation on novel biology is necessary. Asterisks indicate second round submissions. GO, Gene Ontology.<p><b>Copyright information:</b></p><p>Taken from "Predicting gene function in a hierarchical context with an ensemble of classifiers"</p><p>http://genomebiology.com/2008/9/S1/S3</p><p>Genome Biology 2008;9(Suppl 1):S3-S3.</p><p>Published online 27 Jun 2008</p><p>PMCID:PMC2447537.</p><p></p

PAG XXVI - Camoco: identifying high priority candidate genes from GWAS using co-expression networks

Camoco is a fully featured computational framework for building, analyzing and integrating gene co-expression networks with loci identified in genome wide association studies (GWAS). Hundreds of links between genetic markers (SNPs) and agro-economically important traits have been identified by GWAS. Yet, the causal gene or allele often remains unknown due to many genes being in linkage disequilibrium (LD) with each of potentially dozens of genetic markers. Co-expression networks identify genes that share similar response patterns of gene expression making them a powerful tool for inferring the biological function of under-characterized genes. In the right biological context, sets of causal genes related to a GWAS trait will exhibit strong co-expression while inconsequential genes in LD with the marker exhibit random patterns of co-expression.<br>Camoco features methods to build, analyze, and explore co-expression networks using either microarray or RNA-Seq data. Once built, Camoco establishes a biological context for networks by evaluating their ability to recapitulate previously described ontologies (e.g. GO, KEGG, or MapMan). Vetted networks are then used to determine subsets of genes in close proximity to GWAS loci that are strongly co-expressed. GWAS SNPs are mapped to genes using a SNP-to-gene mapping algorithm using user-defined or map-based haplotype windows. High priority candidate genes are identified by evaluating gene-specific co-expression among candidate genes. Demonstrations will be shown using GWAS datasets and co-expression networks generated in both plants and animals. Camoco is free and open source software and available at http://github.com/LinkageIO/Camoco.<br

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-0

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p> using the parameters = 5 and = 10, visualized using Java TreeView [42]. NNN clusters have been colored, internally hierarchically clustered, and the cluster centroids have in turn been hierarchically clustered to provide an easily interpretable tree

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-2

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p>and GO term basis. Each cell represents an AUC score calculated analytically using the Wilcoxon Rank Sum formula; below baseline performance appears in blue, and yellow indicates higher performance. Data set and term combinations for which ten or fewer pairs were able to be evaluated are excluded and appear as gray missing values; functions for which less than 10% of methods were available due to gene exclusion by NNN, QTC, or SAMBA were removed. Visualization provided by TIGR MeV [41]