| Abstract | Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophi/a melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. |
| Author Summary | We apply these methods to a compilation of data on gene eXpression in fruit flies, an important model organism. |
| E 3 A A g Time s 'r r a E A AA Time Time | We expect these additional waveforms to be more sensitive to asymmetric patterns of gene expression , resulting in discovery of additional rhythmic time series. |
| Introduction | Arguably the most well-studied periodic patterns are circadian rhythms: oscillatory changes in gene expression , metabolism, physiology, and behavior with approximately 24-hour (24 h) periods that enable organisms to anticipate and respond to daily changes in their environment, such as nutrient accessibility, temperature, and light [10—13]. |
| Introduction | The advent of high throughput methods for measuring gene expression now makes transcriptome-wide studies of this nature possible. |
| Introduction | As a result, gene expression time series are typically sparsely sampled (e.g., every 2—4 hours (h) in circadian studies), often without multiple measurements per time point, which we refer to here as “replicates”. |
| Overview | The methods that we consider are general and can be applied to detecting periodic behavior in any context, but we describe them here in terms of searching for circadian rhythms in gene expression for clarity. |
| Supporting Information | (A) Z-scored gene expression of genes from the metadataset involved in glutathione metabolism averaged across 24 h and interpolated to every 2 h. (B) Phase and asymmetry distribution of the genes from the metadataset involved in glutathione metabolism. |
| Supporting Information | (A) Z-scored gene expression of genes from the metadataset involved in oxidation reduction averaged across 24 h and interpolated to every 2 h. Black indicates time points Where data were not available (NA). |
| Supporting Information | Peak expression (phase) of these genes is distributed over 24 h. (A) Z-scored gene expression of genes from the metadataset involved in alternative splicing averaged across 24 h and interpolated to every 2 h. (B) Phase and asymmetry distribution of the genes from the 81 Data. |
| Abstract | Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. |
| Abstract | By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. |
| Introduction | Although putative CSCs have been identified by surface marker eXpression for several malignancies, isolated, and demonstrated to be chemotherapeutic resistant [7—11], it remains a significant challenge to obtain a prognostic measure of their abundance from tumour bulk gene eXpression profiles across multiple malignancies. |
| Introduction | Embryonic Stem (ES) cell gene eXpression signatures are clear candidates for such a measure and indeed have been demonstrated to be prognostic in breast and lung cancer [12—15]. |
| Introduction | Specifically, we consider signalling entropy which is computed from the integration of a sample’s genome-wide gene eXpression profile with an interactome, and provides an overall measure of the signalling promiscuity in the sample [16]. |
| Rationale of signalling entropy as a prognostic measure | Signalling entropy is derived from the integration of a sample’s gene expression profile with a human protein interactome, and provides a rough proxy for the overall level of signalling promiscuity in the sample. |
| Rationale of signalling entropy as a prognostic measure | We thus derived a condition for point-wise super-additivity of our measure and then considered a data set of gene expression profiles for 33 distinct adult tissues, representing 528 possible pairwise mixtures [29]. |
| Abstract | To investigate this relationship, we created an extensive normalized gene expression compendium forthe bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. |
| Abstract | Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (i3.5%) to 98.3% (i2.3%) for the various characteristics. |
| Adjustment of batch-effects in the transcriptome compendium | Although integrative analysis of multiple microarray gene expression (MAGE) datasets allows to distill the maximum relevant biological information from genomic datasets, the unwanted variation, so-called batch-effects arising from data merged from difference sources has been a major challenge to impede such effort [61]. |
| Author Summary | Our work argues that genome-scale gene expression can be a multipurpose marker for identifying latent, heterogeneous cellular and environmental states and that optimal classification can be achieved with a feature set of a couple hundred genes that might not necessarily have the most pronounced differential expression in the respective conditions. |
| Categorization of gene expression data | Categorization of gene expression data |
| Discussion | To address this question, we constructed an extensive, annotated gene expression compendium, where we trained Bayesian models for seven distinct classification tasks. |
| Introduction | More recently, a probabilistic human tissue and cell type predictor was built based solely on gene expression profiles [28]. |
| Introduction | In this work, we investigate how well we can predict cellular and environmental state from genome-wide expression, using known gene expression profiles as our only training data. |
| Introduction | This E. coli Gene Expression Compendium (EcoGEC), consists of publicly available data that were curated from online public databases such as GEO [30], ArrayExpress [31], SRA [32], SMD [33], M3D [34] and PortEco [35]. |
| Methods | When bedGraph format was used in the data, gene expression level was measured in RPKM using the bgrQuantifler program that is part of the RSEQ tool [60]. |
| Abstract | In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. |
| Acknowledgments | We thank the nurses of the breast center (CHUV), Dr. Iulien Dorier (SIB, CIG) for stimulating discussions and the Genomic Technologies Facility (GTF at CIG) for RNA preparation, gene expression profiling and for printing assistance. |
| Experimental design | We undertook a rigorous experimental design consisting in profiling changes in phenotype, cy-tokine secretion, gene expression and angiogenic activity from the same cell sample in response to treatments. |
| Gene expression profiling | Gene expression profiling |
| Introduction | Boolean modeling of steady state transitions helps in understanding the influence of perturbations on system wide behavior and has been used to identify the key molecular mechanisms controlling gene expression [4,5,6] and regulation [7,8], cell differentiation [9] and signal transduction [10,11,12,13,14,15,16,17,18,19,20]. |
| Introduction | Finally, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM infiltrating carcinoma of the breast remain plastic cells that can be reverted from pro-angiogenic and protumoral cells to immunological potent monocytes. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | Having identified the critical ligands and pathways controlling TEM plasticity, we next examined in TEM differentiated in vitro Whether differential gene expression might also contribute to the molecular basis of TEM plastic behavior. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | To this end, we selected VEGF/TNF-oc, ANG-2/TGF-[3 and PlGF/TGF-B treatments for gene expression profiling using Affimetrix whole genome microarrays, because these treatments were present in 17, 16 and 14, respectively of the 74 links (treatment/receptor/cytokine) retained in TEM regulatory network (S4 Table and Fig. |
| abundance of genes regulating differentiation and immune response of TEM differentiated in vitro | Enrichment analyses of the gene expression data against known pathways and functional gene categories were conducted as described in Materials and Methods. |
| Cryptic 3’SS selection is limited to tumors with mutations in HEAT repeat hotspots | Differences in cryptic 3’SS usage due to varying gene expression may contribute to the divergent prognostic implications of SF3BI mutation in various cancers [2,7]. |
| Cryptic 3’SSs are used infrequently relative to canonical 3’SSs | These results suggest that cryptic 3’SS selection may affect gene expression for a subset of genes. |
| Cryptic 3’SSs are used infrequently relative to canonical 3’SSs | 4A) suggests that most genes’ expression are not affected by cryptic 3’SS selection and most cryptic 3’SSs are observed at a low frequency because they are spliced in infrequently compared to their associated canonical 3’SSs. |
| Differential gene expression | Differential gene expression |
| Differential gene expression | Gene expression was estimated as described above. |
| Differential gene expression | We provided these read counts to DESeq2 (v1.2.10, R v3.0.3) and tested for differential gene expression using nbinomWaldTest using cancer type as a covariate for the analysis with different cancers [43]. |
| Gene expression | Gene expression |
| Gene expression | Gene expression was estimated by summing together the effective counts or FPKM values for all transcripts contained in a gene. |
| Author Summary | One promising strategy is to identify drugs that, at the transcriptional level, reverse the gene expression signature of a disease. |
| Author Summary | A major difficulty with this strategy is variability: different gene expression signatures of the same disease or drug treatment can show poor overlap across studies. |
| CMapBatch meta-analysis strategy: From individual cancer gene signatures to candidate therapeutics | Our analyses are based on 21 previously published gene expression signatures of lung cancer obtained from Oncomine [11] and CDIP, the Cancer Data Integration Portal (http://ophid. |
| Candidate therapeutics | On a set of 21 transcriptional signatures of lung cancer, we identified 247 drugs that significantly reverse these pathological gene expression changes (at FDR < 1%). |
| Characterizing and prioritizing candidate lung cancer therapeutics | At an FDR cutoff of 0.01, we find that 247 drugs (out of 1,309 drugs in CMap Build 2) significantly reverse the gene expression changes seen With lung cancer in the full set of 21 lung cancer signatures (Sl Table). |
| Introduction | The CMap tool takes as input a set of up-regulated probe sets and a set of down-regulated probe sets, and returns a list of drugs that reverts or mimics those gene eXpression changes. |
| Significant drugs are broad-acting: They affect more genes than other drugs | We used the CMap gene expression profiles from before and after drug treatment to calculate the number of genes differentially expressed in response to a drug, for each of the 1,309 drugs in the collection (see Materials and Methods). |
| Abstract | Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. |
| Author Summary | Using timecourse yeast gene expression data, we show how correlation of relative abundances can lead to conclusions opposite to those drawn from absolute abundances, and that its value changes when different components are included in the analysis. |
| Caution about correlation | As far as we are aware, none of the database providers explicitly address whether absolute levels of gene expression were constant across experimental conditions. |
| Correlations between relative abundances tell us absolutely nothing | The revisitation of this assumption [7] should raise alarm bells about the inferences drawn from many gene expression studies. |
| Introduction | To further illustrate how correlation can be misleading we applied it to absolute and relative gene expression data in fission yeast cells deprived of a key nutrient [6]. |
| Results | [6] on the absolute levels of gene expression (i.e., mRNA copies per cell) in fission yeast after cells were deprived of a key nutrient (Fig. |
| Results in relation to genome regulation in fission yeast | These findings raise intriguing questions as to the molecular mechanisms underlying this proportional regulation, suggesting sophisticated, coordinated control of numerous mRNAs at both transcriptional and post-transcriptional levels of gene expression . |
| Discussion | In this respect, we note that GR represses TNFoc induction of IL-8 gene expression by acting after transcription initiation [37]. |
| Experimental validation of the theory | Note that two cofactors cannot be of the same type at the same step, GR will not repress gene expression if it is an A before the CLS, and the Amin graphs allow GR to act anywhere as a D or as an A after the CLS. |
| Introduction | An unresolved question is Whether the underlying mechanism of each transcriptional component is the same or changes With the direction of gene eXpression output (i.e., increase in induction vs. decrease in repression). |
| Introduction | Thus, many cofactors are classified as either coactiva-tors or corepressors based solely upon their ability to increase or decrease respectively, the level of steroid-mediated gene eXpression [2,17]. |
| Theory of non-cooperative gene induction | Gene expression involves the binding of molecules, protein, and DNA into complexes that lead to transcription. |
| Y5.) + X'; = Y', +7C | This suggests that many transcriptional cofactors/comodulators are mono-functional and similarly modulate basic steps in gene eXpression irrespective of the directional change in gene product levels. |