Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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ExpressionData - A public resource of high quality datasets representing gene expression across tissues, conditions, diseases and genotypes.

ABSTRACT: Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, a dozen reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions. For most organisms, however, few or no such reference datasets are publicly available. Here, we describe a new type of datasets highly representative for the spatial, temporal and response dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments and aggregating results by anatomical parts, stages of development, perturbations, drugs, diseases, neoplasms, and genotypes. The proposed datasets were created for human and several model organisms and are publicly available at www.expressiondata.org. PMI: 18 Samples were used with 2 different experimental factors: 1. 2 harvesting times (morning/afternoon) 2. 3 pos-harvesting times. 3 biological replicates were used.

ORGANISM(S): Nicotiana tabacum

SUBMITTER: Sam Ansari

PROVIDER: E-GEOD-44938 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:For decades the tobacco plant has served as a model organism in plant biology to answer fundamental biological questions in the areas of plant development, physiology, and genetics. Due to the lack of sufficient coverage of genomic sequences, however, none of the expressed sequence tag (EST)-based chips developed to date cover gene expression from the whole genome. The availability of Tobacco Genome Initiative (TGI) sequences provides a useful resource to build a whole genome exon array, even if the assembled sequences are highly fragmented. Here, the design of a Tobacco Exon Array is reported and an application to improve the understanding of genes regulated by cadmium (Cd) in tobacco is described. From the analysis and annotation of the 1,271,256 Nicotiana tabacum fasta and quality files from methyl filtered genomic survey sequences (GSS) obtained from the TGI and ~56,000 ESTs available in public databases, an exon array with 272,342 probesets was designed (four probes per exon) and tested on two selected tobacco varieties. Two tobacco varieties out of 45 accumulating low and high cadmium in leaf were identified based on the GGE biplot analysis, which is analysis of the genotype main effect (G) plus analysis of the genotype by environment interaction (GE) of eight field trials (four fields over two years) showing reproducibility across the trials. The selected varieties were grown under greenhouse conditions in two different soils and subjected to exon array analyses using root and leaf tissue to understand the genetic make-up of the Cd accumulation. An Affymetrix Exon Array was developed to cover a large (~90%) proportion of the tobacco gene space. The Tobacco Exon Array will be available for research use through the Affymetrix array catalogue. As a proof of the exon array usability, we have demonstrated that the Tobacco Exon Array is a valuable tool for studying Cd accumulation in tobacco leaves. Data from field and greenhouse experiments supported by gene expression studies strongly suggested that the difference in leaf Cd accumulation between the two specific tobacco cultivars is dependent solely on genetic factors and genetic variability rather than on the environment. 22 samples were used with 3 different experimental factors: [1] 2 different tissues (leaves and roots/lateral), [2] 2 different mutations (V5 and V21), and [3] 2 different stimuli (Soil 1 and Soil 2). 2-3 biological replicates were used. One sample did not pass QC and is not included in this submission.

Dataset Information

ExpressionData - A public resource of high quality datasets representing gene expression across tissues, conditions, diseases and genotypes.

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