Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Taxonomic accuracy and complementarity between bulk and eDNA metabarcoding provides an alternative to morphology for biological assessment of freshwater macroinvertebrates

PROVIDER: PRJNA1084784 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR28242878_1.fastq.gz	Fastqsanger.gz
	SRR28242878_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 2 of 2

Similar Datasets

Towards functional profiling for eDNA based monitoring in coastal environments: a comparison of three approaches

Project description:Monitoring microbial communities can aid in understanding the state of these habitats. Environmental DNA (eDNA) techniques provide efficient and comprehensive monitoring by capturing broader diversity. Besides structural profiling, eDNA methods allow the study of functional profiles, encompassing the genes within the microbial community. In this study, three methodologies were compared for functional profiling of microbial communities in estuarine and coastal sites in the Bay of Biscay. The methodologies included inference from 16S metabarcoding data using Tax4Fun, GeoChip microarrays, and shotgun metagenomics.

2024-04-14 | E-MTAB-13075 | biostudies-arrayexpress

Project description:Accuracy of sediment eDNA metabarcoding for assessment of impact from offshore oil drilling

| PRJNA727023 | ENA

Project description:Metabarcoding stream macroinvertebrates and eDNA samples from streams in southern Ontario

| PRJNA575063 | ENA

Project description:NGS for macroinvertebrates assessment

| PRJNA417754 | ENA

Project description:Biodiversity assessment of coastal lagoon through eDNA metabarcoding

| PRJNA549294 | ENA

seawater metagenome

Project description:MiFish eDNA metabarcoding

| PRJDB9158 | ENA

Project description:Fish eDNA metabarcoding

| PRJNA1049655 | ENA

seawater metagenome

Project description:MiFish eDNA metabarcoding

| PRJDB11136 | ENA

Project description:Bulk metabarcoding of macroinvertebrates, Trebbia River, Northern Italy

| PRJNA655393 | ENA

A systematic evaluation of pattern discovery algorithms

Project description:Pattern discovery algorithms are methods for discovering recurrent, non-random motifs widely used in the analysis of biological sequences. Many algorithms exist but few comparisons have been made amongst them. We systematically profile eight representative methods at multiple parameter settings across 174 diverse experimental datasets, including ten novel ChIP-on-chip datasets. We executed 16,777 pattern discovery analyses to assess prediction accuracy, CPU usage and memory consumption. For 144 datasets we developed a gold-standard using machine-learning algorithms; cross-validation was used for the remaining datasets. Performance was highly disparate, with median accuracy ranging from 32% to 96%. Importantly we were unable to replicate previously reported algorithm-rankings, emphasizing the need to use many and diverse experimental datasets. We found deterministic algorithms like Projection and Oligo/Dyad had the highest prediction accuracy. Computational efficiency was not linearly related to dataset size and becomes critical: some algorithms are intractably slow on large datasets. This work provides the first combined assessment of the CPU, memory, and prediction accuracies of pattern discovery algorithms on real experimental datasets. HL60-Mnt-ChIP: ChIP-Chip with 10 biological replicates HL60-Trrap-ChIP: ChIP-Chip with 13 biological replicates

2010-05-19 | E-GEOD-15370 | 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