Microarray analysis to identify the differentially expressed genes induced by NROB
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ABSTRACT:
ORGANISM(S): Arabidopsis thaliana
PROVIDER: GSE65460 | GEO | 2015/07/01
SECONDARY ACCESSION(S): PRJNA274045
REPOSITORIES: GEO
Similar Datasets
Project description:Compared to wild type plants, overexpression of NROB results leaf early senescence, biomass and seeds yield decreased more than 70%. Microarray analysis the differential expression genes induced by NROB Young leaves of DAG 30 were harvested from WT and 35S: NROB #2 plants and frozen immediately in liquid nitrogen. Total RNA was extracted using the RNeasy plant mini kit (Qiagen) and was checked for a RIN number to inspect RNA integration by an Agilent Bioanalyzer 2100. Qualified and purified total RNA was amplified and labeled by Low Input Quick Amp Labeling Kit, Two-Color (Agilent technologies, US). Regulated genes were identified with a stringent significance threshold, namely a mean >2.0-fold change (transformants relative to WT control samples) and based on at least two replicates. Microarray analysis result was verified by real-time PCR
2015-07-01 | E-GEOD-65460 | biostudies-arrayexpress
Project description:Soybean is a rich source of protein and oil and a primary feedstock for biodiesel production. Previous research on soybean indicated that protein, oil and yield are controlled quantitatively in soybean seeds. However, genetic mechanisms controlling seed composition and yield in soybean remain unknown. We used Affymetrix Soybean GeneChips® to identify genes that are differentially expressed between developing seeds of the Minsoy and Archer soybean varieties, which differ in seed weight, yield, protein content and oil content. Some of the differentially expressed genes identified in this study may play important roles in controlling these traits.
2015-04-29 | GSE21598 | GEO
Project description:Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.The C++ code to implement the proposed method is available upon request for academic use.
| S-EPMC3607310 | biostudies-literature
Project description:The present study aimed to identify differentially expressed genes (DEGs) and biological processes (BPs) associated with asthma. DEGs between allergic asthma and healthy controls were screened from GSE15823. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was constructed, followed by module mining and functional analysis. Additionally, GSE41649 was downloaded to validate the reliability of the results. In GSE41649, DEGs were identified and compared with key DEGs identified in GSE15823. A total of 43 upregulated and 275 downregulated DEGs were obtained from GSE15823. Upregulated DEGs, such as nitric oxide synthase 2 (NOS2), were enriched in BPs related to oxidation reduction. Downregulated DEGs, such as chemokine (C-C motif) ligand 19 (CCL21) and Cys-X-Cys ligand (CXCL9), were enriched in immune response-related BPs. Protein tyrosine phosphatase receptor type C (PTPRC), CCL21, and CXCL9 were identified as hub genes. The DEGs in module 1 were significantly involved in the chemokine signaling pathway (P<0.05). The expression of the key genes obtained in GSE15823 demonstrated the same variation directions in the two datasets. The immune response, oxidants and nitric oxide metabolic pathways may have important roles in the progression of asthma. DEGs of PTPRC, CCL21, CXCL9 and NOS2 may be the potential targets for asthma diagnosis and treatment.
| S-EPMC6122392 | biostudies-literature
Project description:Soybean is a rich source of protein and oil and a primary feedstock for biodiesel production. Previous research on soybean indicated that protein, oil and yield are controlled quantitatively in soybean seeds. However, genetic mechanisms controlling seed composition and yield in soybean remain unknown. We used Affymetrix Soybean GeneChips® to identify genes that are differentially expressed between developing seeds of the Minsoy and Archer soybean varieties, which differ in seed weight, yield, protein content and oil content. Some of the differentially expressed genes identified in this study may play important roles in controlling these traits. The soybean plants of two soybean varieties Minsoy and Archer and two recombinant inbred lines from the cross that are similar in maturity but differ in yield were grown in St Paul, Minnesota during the summers of 2007 and 2008. In 2007, each line was planted as a single row. In 2008, a randomized complete block (RCB) design was used and each line had 3 replicates planted 1-2 weeks apart. Within each replicate, two rows per line were planted. Seeds were harvested at three developmental stages, namely, seed length = 2 mm, 3.5 mm, and 5-6 mm, which correspond approximately to soybean reproductive stages R4, R5 and early R6, respectively. In 2007 three independent samples were collected for each line and developmental stage. In 2008, two seed samples (one from each row) were collected for each line at each stage within each replicate. The pairs of seed samples were then pooled. Thus, three sets of independent tissue samples were collected for RNA extraction and hybridization on Affymetrix microarrys.
2015-04-29 | E-GEOD-21598 | biostudies-arrayexpress
Project description:BackgroundTime-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.ResultsIn this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say C1 and C2). We model the expression at C1 using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from C2 is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.ConclusionIn both cases, the proposed method identified biologically significant genes.
| S-EPMC2435549 | biostudies-literature
Project description:BackgroundWe previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these "differentially expressed GO terms" and have named the algorithm "matrix-assisted identification method of differentially expressed GO terms" (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms.FindingsWe combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson's correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated.ConclusionsMIMGO is a reliable method to identify differentially expressed GO terms comprehensively.
| S-EPMC3557167 | biostudies-literature
Project description:The MYB gene family encodes transcription factors with a diverse range of functions in Arabidopsis. This study demonstrated that MYB5, which is expressed in trichomes and seeds, plays a central role in trichome and seed development. A microarray analysis of myb5 seeds identified other members of the MYB5 regulatory network.
2009-03-02 | GSE14229 | GEO
Project description:Cotton premature leaf senescence often occurred with an increasing frequency in many cotton growing areas and caused serious reduction in yield and quality of cotton has been one of the impontant factors that restrict severely the production of cotton.Our laboratory studies showed chilling stress is the key factor that induced A. alternatia infection, caused Alternaria disease and then lead to cotton leaf senescence, but the molecular mechanism of cotton premature leaf senscence is still unclear. We used microarrays to study molecular mechanism of chilling stress causing Alternaria alternata infection and leading to cotton leaf senescence and find the key genes during this process.
2015-10-28 | GSE74412 | GEO
Project description:During the senescence stage, leaves undergo degeneration and relocate nutrients accumulated during the growth stage to sink parts such as seeds, critically contributing to plants’ productivity and fitness. Here, we asked how leaf transcriptome is regulated during the senescence stage by performing directional sequencing of total and small RNAs for the entire lifespan of Arabidopsis leaves.
2016-03-16 | GSE43616 | GEO