Ontology highlight
ABSTRACT:
PROVIDER: PRJNA878660 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR21496014_1.fastq.gz | Fastqsanger.gz | |||
| SRR21496014_2.fastq.gz | Fastqsanger.gz | |||
| SRR21496015_1.fastq.gz | Fastqsanger.gz | |||
| SRR21496015_2.fastq.gz | Fastqsanger.gz | |||
| SRR21496016_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 12 |
Similar Datasets
Project description:Adaptation to public goods cheats in Pseudomonas aeruginosa
| PRJEB17843 | ENA
Project description:<p>Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC–MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC–MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.</p>
2019-10-30 | MTBLS1040 | MetaboLights
Project description:In this study, authors had briefly talked about the comparison of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for binary classification of cancer. It is concluded that, SVMs perform better on cancer prediction which are not expressed by their genotype whereas ANNs capture the flaws in the other case. In this paper, the author's had provided a good strategy to construct SVM model. The model was coded from their approach and Grid Search method was used for estimation of a better set of parameters. The resulted model had provided better evaluation metrics than the one mentioned in the manuscript. The model is then exported to Open Neural Network Exchange (ONNX) format to avail the model to be accessible in various platforms thereby promoting the FAIReR (Findable, Accessible, Interoperable, Reusable, and Reproducible) protocol for sharing machine learning models. Docker files were provided for both training and testing environment so that the curator can reproduce the results.
The prediction output '0' means the sample is Benign, otherwise its Malignant.
2024-07-13 | MODEL2407130001 | BioModels
Project description:RNA sequencing of pig tissues for transcriptome annotation and expression analysis. Tissue specific RNA-seq data was generated to support annotation of coding and non-coding genes and to measure tissue specific expression. This study is part of the FAANG project, promoting rapid prepublication of data to support the research community. These data are released under Fort Lauderdale principles, as confirmed in the Toronto Statement (Toronto International Data Release Workshop. Birney et al. 2009. Pre-publication data sharing. Nature 461:168-170). Any use of this dataset must abide by the FAANG data sharing principles. Data producers reserve the right to make the first publication of a global analysis of this data. If you are unsure if you are allowed to publish on this dataset, please contact alan.archibald@roslin.ed.ac.uk, lel.eory@roslin.ed.ac.uk and faang@iastate.edu to enquire. The full guidelines can be found at http://www.faang.org/data-share-principle”.
2017-02-10 | E-MTAB-5895 | biostudies-arrayexpress