Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantifying rat diets by direct nanopore sequencing

ABSTRACT: New tools for diet analyses: nanopore sequencing of metagenomic DNA from stomach contents to quantify diet in an invasive population of rats

PROVIDER: PRJEB27647 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Diet composition of stomach contents of zokor

| PRJNA753876 | ENA

RNA-seq of stomach and pyloric caeca from Atlantic salmon (Salmo salar) fed diets containing chitin from shrimp and black soldier fly larvae

Project description:Farmed Atlantic salmon was given either a 6 % cellulose diet, a diet containing 6 % shrimp shell chitin or a diet containing 6 % chitin from black soldier fly larvae for a period of 4 weeks. The fish were split into six tanks at the beginning of the experiment; six fish per tank and two tanks per diet. RNA from stomach and pyloric caeca from four fish given each diet was sequenced.

2021-08-15 | E-MTAB-10594 | biostudies-arrayexpress

Project description:Stomach contents of Plateau zokor

| PRJNA867532 | ENA

Stomach contents

Project description:Stomach contents Raw sequence reads

| PRJNA1050383 | ENA

Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing

Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.

2023-03-01 | GSE195618 | GEO

Project description:DNA metabarcoding of fish stomach contents

| PRJNA653773 | ENA

Expression data from adult wild-type male Drosophila melanogaster fed control or high-sugar diets

Project description:Chronic high-sugar feeding (1 M or 34% sucrose) leads to hyperglycemia, obesity, and insulin resistance in adult flies, compared with those fed a control diet (0.15 M or 5% sucrose). We compared two days and four weeks of high-sugar feeding to look at short- and long- term effects on gene expression. We used Affymetrix Drosophila GeneChip 2.0 microarrays to quantify differential expression between control and high-sugar-fed flies at two time points.

2017-10-21 | GSE105448 | GEO

quanTIseq: quantifying immune contexture of human tumors

Project description:We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.

2019-02-06 | GSE107572 | GEO

Project description:The unrecognized components in the stomach contents

| PRJNA1083287 | ENA

CellMixS: quantifying and visualizing batch effects in single cell RNA-seq data

Project description:A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in scRNA-seq data from a variety of datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (\texttt{cms}) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented the \texttt{cms}, as well as related metrics to detect batch effects or measure structure preservation, in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.

2021-01-31 | E-MTAB-9916 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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