To explore the relationship between new gene expression / mutation and survival of patients in colorectal cancer
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer
PROVIDER: 2676067 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Model destription:
The model describes the stochastic dynamics of two variables, protein and mRNA of a gene with constitutive expression.
Publication:
A New Efficient Approach to Fit Stochastic Models on the Basis of High-throughput Experimental Data Using a Model of IRF7 Gene Expression as Case Study
Authors
Luis U. Aguilera, Christoph Zimmer and Ursula Kummer.
2017-03-17 | MODEL1608100000 | BioModels
Project description:The study included post-mortem brain tissue samples from 68 schizophrenia patients and 44 age and sex matched control subjects. Whole transcriptome poly-A selected paired-end RNA sequencing was performed on tissue from prefrontal cortex and orbitofrontal cortex. RNA expression differences were detected between case and control individuals, focusing both on single genes and pathways.
| EGAS00001004199 | EGA
Project description:<p>Case control cohort: The details have been previously published: American Heart Journal. 2010;160:371-379 e372. Briefly, a nested case:control cohort of CATHGEN participants who had experienced death or MI (n = 250) after their index catheterization and age-, sex-, and race-matched controls (n = 250) who were free of death/MI > 2 years after cardiac catheterization was identified as part of the MURDOCK Horizon 1 Cardiovascular Disease Study.21 Sufficient RNA for microarray analysis was available in 447 members of this cohort.</p> <p>Observational cohort: 224 sequential CATHGEN samples were selected for whole blood RNA analysis, of which, 191 had sufficient RNA for microarray analysis.</p>
| phs000551.v1.p1 | EGA
Project description:Sensitivity and throughput of transcriptomics and proteomomics technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNAseq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and on any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in zebrafish embryos, which is an upcoming model in disease studies. We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 15,927 genes. Gene expression was also found to correlate with the abundance of 1,017 quantified proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed. By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies. The transcriptomics data is deposited in NCBI GEO. Proteomics informatics: Spectra were identified by Mascot and validated by PeptideProphet and ProteinProphet in the Trans-Proteomic Pipeline (TPP). The main search parameters are: full tryptic specificity, precursor mass searched within -0.5 to +2.5 Da, product ions within [-0.5,0.5] Da and allowing for one missed cleavage. Carbamidomethylation was assumed as the only and fixed PTM.
2016-06-09 | PXD000145 | Pride
Project description:ObjectivesMajor public high-throughput functional genomic data repositories, including the Gene Expression Omnibus (GEO) and ArrayExpress have rapidly expanded. As a result, a large number of diverse high-throughput functional genomic data retrieval systems have been developed. However, high-throughput functional genomic data retrieval remains challenging.MethodsWe developed Gene Expression data Explore (GEE), the first powerful, flexible web and mobile search application for searching whole-genome epigenetic data and microarray data in public databases, such as GEO and ArrayExpress.ResultsGEE provides an elaborate, convenient interface of query generation competences not available via various high-throughput functional genomic data retrieval systems, including GEO, ArrayExpress, and Atlas. In particular, GEE provides a suitable query generator using eVOC, the Experimental Factor Ontology (EFO), which is well represented with a variety of high-throughput functional genomic data experimental conditions. In addition, GEE provides an experimental design query constructor (EDQC), which provides elaborate retrieval filter conditions when the user designs real experiments.ConclusionsThe web version of GEE is available at http://www.snubi.org/software/gee, and its app version is available from the Apple App Store.
| S-EPMC4871849 | biostudies-literature
Project description:Interventions: Colorectal cancer group:None;Colorectal adenoma group:None
Primary outcome(s): Septin9 gene
Study Design: Case-Control study
| 40522 | ecrin-mdr-crc
Project description:BACKGROUND:High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type. METHODS:To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database. RESULTS:Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different. CONCLUSIONS:These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.
| S-EPMC7216615 | biostudies-literature
Project description:Case Control Candidate Gene Study for Human African Trypanosomiasis
| PRJEB35143 | ENA
Project description:Candidate Gene Case Control Study of Human African Trypanosomiasis
| PRJEB35388 | ENA
Project description:A systematic transcriptome survey is essential for the characterization and comprehension of the molecular basis underlying phenotypic variations. Recently developed RNA-seq methodology has facilitated efficient data acquisition and information mining of transcriptomes in multiple tissues/cell lines. Current mammalian transcriptomic databases are either tissue-specific or species-specific, and they lack in-depth comparative features across tissues and species. Here, we present a mammalian transcriptomic database (MTD) that is focused on mammalian transcriptomes, and the current version contains data from humans, mice, rats and pigs. Regarding the core features, the MTD browses genes based on their neighboring genomic coordinates or joint KEGG pathway and provides expression information on exons, transcripts and genes by integrating them into a genome browser. We developed a novel nomenclature for each transcript that considers its genomic position and transcriptional features. The MTD allows a flexible search of genes or isoforms with user-defined transcriptional characteristics and provides both table-based descriptions and associated visualizations. To elucidate the dynamics of gene expression regulation, the MTD also enables comparative transcriptomic analysis in both intraspecies and interspecies manner. The MTD thus constitutes a valuable resource for transcriptomic and evolutionary studies. The MTD is freely accessible at http://mtd.cbi.ac.cn.
| S-EPMC5221423 | biostudies-literature