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PROVIDER: PRJNA205486 | ENA |
REPOSITORIES: ENA
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Project description:David2008 - Genome-scale metabolic network of
Aspergillus nidulans (iHD666)
This model is described in the article:
Analysis of Aspergillus
nidulans metabolism at the genome-scale.
David H, Ozçelik IS, Hofmann G,
Nielsen J.
BMC Genomics 2008; 9: 163
Abstract:
BACKGROUND: Aspergillus nidulans is a member of a diverse
group of filamentous fungi, sharing many of the properties of
its close relatives with significance in the fields of
medicine, agriculture and industry. Furthermore, A. nidulans
has been a classical model organism for studies of development
biology and gene regulation, and thus it has become one of the
best-characterized filamentous fungi. It was the first
Aspergillus species to have its genome sequenced, and automated
gene prediction tools predicted 9,451 open reading frames
(ORFs) in the genome, of which less than 10% were assigned a
function. RESULTS: In this work, we have manually assigned
functions to 472 orphan genes in the metabolism of A. nidulans,
by using a pathway-driven approach and by employing comparative
genomics tools based on sequence similarity. The central
metabolism of A. nidulans, as well as biosynthetic pathways of
relevant secondary metabolites, was reconstructed based on
detailed metabolic reconstructions available for A. niger and
Saccharomyces cerevisiae, and information on the genetics,
biochemistry and physiology of A. nidulans. Thereby, it was
possible to identify metabolic functions without a gene
associated, and to look for candidate ORFs in the genome of A.
nidulans by comparing its sequence to sequences of
well-characterized genes in other species encoding the function
of interest. A classification system, based on defined
criteria, was developed for evaluating and selecting the ORFs
among the candidates, in an objective and systematic manner.
The functional assignments served as a basis to develop a
mathematical model, linking 666 genes (both previously and
newly annotated) to metabolic roles. The model was used to
simulate metabolic behavior and additionally to integrate,
analyze and interpret large-scale gene expression data
concerning a study on glucose repression, thereby providing a
means of upgrading the information content of experimental data
and getting further insight into this phenomenon in A.
nidulans. CONCLUSION: We demonstrate how pathway modeling of A.
nidulans can be used as an approach to improve the functional
annotation of the genome of this organism. Furthermore we show
how the metabolic model establishes functional links between
genes, enabling the upgrade of the information content of
transcriptome data.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180016.
To cite BioModels Database, please use:
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An enhanced, curated and annotated resource for published
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2015-07-30 | MODEL1507180016 | BioModels
Project description:Phylogenomic analysis of Cnidaria Transcriptome or Gene expression
| PRJNA263637 | ENA
Project description:Primary outcome(s): Cancer related genome analysis, gene expression and molecular characterization analysis
| 2647002 | ecrin-mdr-crc
Project description:Co-expression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting the functional roles of individual genes at a system-wide scale. To enable network reconstructions we built a large-scale gene expression atlas comprised of 62,547 mRNAs, 17,862 non-modified proteins, and 6,227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. There was little edge conservation in co-expression and GRNs reconstructed using transcriptome versus proteome data yet networks from either data type were enriched in ontological categories and effective in predicting known regulatory relationships. This integrated gene expression atlas provides a valuable community resource. The networks should facilitate plant biology research and they provide a conceptual framework for future systems biology studies highlighting the importance of studying gene regulation at several levels.
| MSV000079290 | MassIVE
Project description:Co-expression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting the functional roles of individual genes at a system-wide scale. To enable network reconstructions we built a large-scale gene expression atlas comprised of 62,547 mRNAs, 17,862 non-modified proteins, and 6,227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. There was little edge conservation in co-expression and GRNs reconstructed using transcriptome versus proteome data yet networks from either data type were enriched in ontological categories and effective in predicting known regulatory relationships. This integrated gene expression atlas provides a valuable community resource. The networks should facilitate plant biology research and they provide a conceptual framework for future systems biology studies highlighting the importance of studying gene regulation at several levels.
| MSV000079303 | MassIVE
Project description:Interventions: Group 1: Quantitative Expression Analysis of the proteom and gene Expression of Primary Tumor, normal tissue, and metastases
Primary outcome(s): Disease associated Proteins and Genes
Study Design: Allocation: ; Masking: ; Control: ; Assignment: ; Study design purpose: basic science
| 2431957 | ecrin-mdr-crc
Project description:Spike-specific T and B cells from splenocytes of immunized mice were profiled with 10x sequencing for gene expression (Chromium Next Automated GEM 5’ v2 kit), cell surface marker (Chromium Automated 5’ Feature Barcode kit) and immune receptor (Chromium Automated Mouse BCR/TCR Amplification and Library Construction kit)
2023-08-07 | GSE222403 | GEO
Project description:Primary outcome(s): 1. Evaluation of genome abnormality and gene expression by omics analysis of tumor etc. 2. TCR repertoire analysis and RNA expression analysis etc. of T cells in tumor tissue and peripheral blood. 3. Prediction and identification of tumor neo-antigen and evaluation of immunogenicity etc. 4. Analyze ctDNA(16S rRNA PCR) and feces of patients with advanced solid malignancies over time to profile and monitor cancer-related genomic alterations 5. Assessment of the relationship between the analysis above and clinical pathological features or therapeutic efficacy etc.
| 2648665 | ecrin-mdr-crc
Project description:Cancer research requires models closely resembling the tumor in the patient. Human tissue cultures can overcome interspecies limitations of animal models or the loss of tissue architecture in in vitro models. However, analysis of tissue slices is often limited to histology. Here, we demonstrate that slices are also suitable for whole transcriptome sequencing and present a method for automated histochemistry of whole slices. Tumor and peritumoral tissue from a patient with glioblastoma was processed to slice cultures, which were treated with standard therapy including temozolomide and X-irradiation. Then, RNA sequencing and automated histochemistry was performed. RNA sequencing was successfully performed with a sequencing depth of 243 to 368 x 106 reads per sample. Comparing tumor and peritumoral tissue, we identified 1888 genes significantly downregulated and 2382 genes upregulated in tumor. Treatment significantly downregulated 2017 genes, whereas 1399 genes were upregulated. Pathway analysis revealed changes in the expression profile of treated glioblastoma tissue pointing towards downregulated proliferation. This was confirmed by automated analysis of whole tissue slices stained for Ki67. In conclusion, we demonstrate that RNA sequencing of tissue slices is possible and that histochemical analysis of whole tissue slices can be automated which increases the usability of this preclinical model.
2020-02-04 | GSE119102 | GEO