Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 1000

Humulus lupulus brewing cultivars classification based on HPLC-MS metabolomic profiling

ABSTRACT: Dataset related to the article about metabolome based classification of Humulus lupulus brewing cultivars. There are two types of files: whole set of LC-HRMS profiling from IT-TOF, and .raw data from orbitrap with fragmentation of marker compounds (found in corresponding article) and major compounds of hops.

INSTRUMENT(S): Orbitrap Exploris 120, LCMS-IT-TOF

ORGANISM(S): Humulus Lupulus (ncbitaxon:3486)

SUBMITTER: Ikhalaynen Yuriy

PROVIDER: MSV000089747 | MassIVE | Tue Jun 28 01:44:00 BST 2022

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Similar Datasets

GNPS Humulus lupulus brewing cultivars classification based on HPLC-MS metabolomic profiling

Project description:Dataset related to the article about metabolome based classification of Humulus lupulus brewing cultivars. There are two types of files: whole set of LC-HRMS profiling from IT-TOF, and .raw data from orbitrap with fragmentation of marker compounds (found in corresponding article) and major compounds of hops.

2022-06-28 | MSV000089747 | GNPS

Molecular classification of bladder cancer: global mRNA classification versus tumor cell phenotype classification.

Project description:In this study gene expression profiles for 307 cases of advanced bladder cancers were compared to molecular phenotype at the tumor cell level. TUR-B tissue for RNA extraction was macrodissected from the close vicinity of the tissue sampled for immunohistochemistry to ensure high-quality sampling and to minimize the effects of intra-tumor heterogeneity. Despite excellent agreement between gene expression values and IHC-score at the single marker level, broad differences emerge when samples are clustered at the global mRNA versus tumor cell (IHC) levels. Classification at the different levels give different results in a systematic fashion, which implicates that analysis at both levels is required for optimal subtype-classification of bladder cancer.

2017-03-13 | GSE83586 | GEO

Pseudomonas classification

Project description:Classification of the plant-associated phenotype of Pseudomonas strains using genome properties

| PRJEB45033 | ENA

Mutational classification of colorectal cancer

Project description:Mutational classification of colorectal cancer

| PRJNA319429 | ENA

TB disease classification

Project description:Validation of gene expression levels to assess classification of TB patients and healthy controls

2015-12-21 | GSE74092 | GEO

Transcription profiling of primary mouse hepatocytes reveals classification for genotoxic and non-genotoxic carcinogens by gene expression improves with exposure time

Project description:The prediction of possible carcinogenicity of chemicals for humans represents an ongoing challenge. Chronic rodent bioassays predict human cancer risk at only limited reliability while simultaneously being expensive and long-lasting. In order to seek for alternatives, in the present study, the ability of a transcriptomics-based primary mouse hepatocyte model to classify carcinogens by their modes of action was evaluated. As it is obvious that exposure will induce a cascade of gene expression modifications, in particular, the influence of exposure time in vitro on discriminating genotoxic (GTX) carcinogens from non-genotoxic (NGTX) carcinogens class prediction was investigated. Primary mouse hepatocytes from male C57Bl6 mice were treated for 12, 24, 36 and 48 h with two GTX and two NGTX carcinogens. For the purpose of validation, two additional GTX compounds were studied after incubation periods of 24 and 48 h. Immunostaining of ?H2AX foci was applied to phenotypically verify DNA damage. Whole genome gene expression modifications were analyzed by means of Affymetrix mouse genome 430 2.0 microarrays. Datasets were normalized using RMA and differentially expressed genes were selected for assessing class prediction. The ?H2AX assay confirmed significant induction of DNA damage after treatment with GTX compounds, whereas NGTX compounds showed no activity beyond background levels. Discrimination of GTX and NGTX carcinogens by Prediction Analysis of Microarray (PAM) was validated and resulted in a perfect classification. The present study shows that gene expression profiling in primary mouse hepatocytes is promising for discriminating between GTX and NGTX compounds and that this classification improves with increasing treatment period.

2010-06-09 | E-MEXP-2209 | biostudies-arrayexpress

Bladder tumour stage classification

Project description:Bladder tumours used to construct a tumour stage classifier Keywords = bladder cancer, disease stages, classification Keywords: other

2002-12-08 | GSE89 | GEO

Human leiomyosarcoma samples, molecular classification

Project description:Molecular classification of human leiomyosarcomas To identify new molecular subgroups

2016-12-31 | GSE76216 | GEO

Refinement of breast cancer molecular classification by miRNA expression profiles

Project description:Background: Accurate classification of breast cancer using gene expression profiles has contributed to a better understanding of the biological mechanisms behind the disease and has paved the way for better prognostication and treatment prediction. Results: We found that miRNA profiles largely recapitulate intrinsic subtypes. In the case of HER2-enriched tumors a small set of miRNAs including the HER2-encoded mir-4728 identifies the group with very high specificity. We also identified differential expression of the miR-99a/let-7c/miR-125b miRNA cluster as a marker for separation of the Luminal A and B subtypes. High expression of this miRNA cluster is linked to better overall survival among patients with Luminal A tumors. Correlation between the miRNA cluster and their precursor LINC00478 is highly significant suggesting that its expression could help improve the accuracy of present day’s signatures. Conclusions: We show here that miRNA expression can be translated into mRNA profiles and that the inclusion of miRNA information facilitates the molecular diagnosis of specific subtypes, in particular the clinically relevant sub-classification of luminal tumors.

2019-06-20 | GSE131599 | GEO

Classification of developmental toxicants in a human iPSC transcriptomics-based test

Project description:There is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we used the hiPSC-based UKK-24h-test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. All compounds were analyzed at the maximal plasma concentration (Cmax) and the 20-fold Cmax for a 24 h incubation period in three independent experiments. Using the 1,000 probe sets with the highest variability and including information on cytotoxicity, a penalized logistic regression based classifier (top-1,000 classifier) reached an AUC, accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96 and 0.88, respectively to correctly classify the compounds as teratogens or non-teratogens. This top-1,000 classifier reached a higher AUC, accuracy and sensitivity than a second classifier (SPS-classifier), which used the number of significant probe sets to classify the compounds. At least, the SPS-classifier had a specificity of 1. Classification at 20-fold Cmax did not lead to better performance metrics than testing at 1-fold Cmax. Finally, inclusion of cytotoxicity information into the classifier clearly improved the performance metrics. In conclusion, although further optimization by additional readouts and inclusion of cell systems that model different developmental processes is required, the UKK-24h-test will support the early discovery-phase detection of human developmental toxicants already in its present form.

2022-06-24 | GSE187001 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data