Neognathae
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA280209 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR3470740.fastq.gz | Fastqsanger.gz | |||
| SRR3470750.fastq.gz | Fastqsanger.gz | |||
| SRR3470760.fastq.gz | Fastqsanger.gz | |||
| SRR3470731.fastq.gz | Fastqsanger.gz | |||
| SRR3470741.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 32 |
Similar Datasets
Project description:EGFR and MEK pathway signature RNA-Seq datasets
2025-01-24 | E-GEOD-59765 | ExpressionAtlas
Project description:High-resolution gene expression datasets of ontogenetic zones in the root apical meristem
2023-06-15 | E-GEOD-98097 | ExpressionAtlas
Project description:We analyzed 1042 meningiomas with whole-genome microarray including 12 meningiomas with multifocal sampling to characterize how CNV thresholds influence their molecular classification and outcome.
2025-01-31 | GSE287929 | GEO
Project description:We analyzed 1042 meningiomas with whole-genome microarray including 12 meningiomas with multifocal sampling to characterize how CNV thresholds influence their molecular classification and outcome.
2025-01-31 | GSE287920 | GEO
Project description:Untargeted mass spectrometry metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data pre-processing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresh-olds 1) improved limit of detection, 2) increased the number of features detected with an associated isotope pattern and/or MS-MS fragmentation spectrum and 3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.
2022-08-27 | MSV000090220 | MassIVE
Project description:Site-specific phosphorylation events affect nearly all the cellular processes and correct phosphosite localization plays an important role in biological or medical health studies. However, direct false localization rate (FLR) control remains challenging in phosphoproteomics. Here, we propose DeepFLR, a deep learning-based framework utilizing spectrum prediction and the target-decoy method for FLR estimation. We demonstrate that the similarity between predicted and experimental phosphopeptide spectra is comparable to the measurement reproducibility. We further benchmark our method with four synthetic datasets and three real biological sample datasets, showcasing its ability for sensitive phosphosite localization with accurate FLR estimation.
2023-04-04 | PXD037580 |
Project description:Untargeted mass spectrometry metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data pre-processing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresh-olds 1) improved limit of detection, 2) increased the number of features detected with an associated isotope pattern and/or MS-MS fragmentation spectrum and 3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.
2022-08-27 | MSV000090220 | GNPS