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Dataset results
17 results for “Galaxy Training Network”
Bacterial training dataset for Galaxy training network tutorials on Genome assembly
<p>This training dataset is from an imaginary <em>Staphylococcus aureus</em> bacterium with a miniature genome. There is a reference genome in various formats as well as some fastq reads of a closely related but also imaginary mutant strain.</p> <p>It is a useful dataset for demonstrating:</p> <ul> <li>de novo genome assembly</li> <li>read mapping and variant calling</li> <li>genome annotation</li> </ul> <p>The files included are:</p> <ul> <li><strong>wildtype.fna</strong>: the reference genome sequence of the wildtype strain in fasta format (a header line, then the nucleotide sequence of the genome.)</li> <li><strong>wildtype.gff</strong>: the reference genome sequence of the wildtype strain in general feature format (a list of features - one feature per line, then the nucleotide sequence of the genome.)</li> <li><strong>wildtype.gbk</strong>: the reference genome sequence in genbank format.</li> <li><strong>mutant_R1.fastq</strong> and <strong>mutant_R2.fastq</strong>: Fastq sequence reads of a closely related mutant strain. <ul> <li>The reads are paired-end.</li> <li>Each read is 150 bases long.</li> <li>The number of bases sequenced is equivalent to 19x the genome sequence of the wildtype strain. (Read coverage 19x - rather low!).</li> </ul> </li> </ul>
Training material for small RNA-seq data analysis (Galaxy Training Network tutorial)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3’ end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate <em>Drosophila</em> tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3’ end processing, <em>Symplekin</em>. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) <em>Drosophila</em> piRNA clusters, (3) <em>Symplekin</em>, and (4) genes encoding mass spectrometry-defined protein binding partners of <em>Symplekin</em> from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the <em>Drosophila</em> genome Apr. 2006 (BDGP R5/<em>dm3</em>) release.</p>
Training material for flye genome assembly (Galaxy Training Network tutorial)
<p>Datasets are subsets of 3 public datasets (Mucor mucedo Fresen. NRRL 3635 Standard Draft genome sequencing with PacBio technology)</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336965[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336964[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336963[accn]</p> <p> </p> <p> </p>
Training material for Genome assembly quality control (Galaxy Training Network tutorial)
<p>This Zenodo repository includes the required datasets for following the GTN: Genome assembly quality control.</p>
Dataset for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"
<p>Datasets for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"</p>
Training data for "From small to large-scale genome comparison", a tutorial for the Galaxy Training Network
<p>This dataset comprises two sequence pairs in FASTA format, one including two mycoplasmas (<em>Hyopneumoniae</em> 232 and 7422) and the other including the first chromosome of two plant genomes (<em>Aegilops tauschii</em> and <em>Triticum aestivum</em>).</p>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</p>
Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6),<a href="http://dx.doi.org/10.1093/gigascience/giy057"> http://dx.doi.org/10.1093/gigascience/giy057</a>). </p> <p>The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.</p>
Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network - Sample files for creating Mapping TSV
<p>Sample files for the last step on creating a Mapping TSV in the Galaxy Training Network tutorial on "Identifying Mycorrhizal Fungi from ITS2 sequencing using LotuS2"</p>
SARS-CoV-2 Viral Samples and Reference Genome for Galaxy Training Network SARS with Galaxy on AnVIL Tutorial
<p>In the lab activity, we'll see if there are genomic differences in the collected sample compared to the original SARS-CoV-2 genome (the reference). We need three files to do this:</p> <ul> <li><strong>SARS-CoV-2_reference_genome.fasta</strong> : the reference genome</li> <li><strong>VA_sample_forward_reads.fastq.gz</strong>: 1 of 2 raw read data files</li> <li><strong>VA_sample_reverse_reads.fastq.gz</strong>: 2 of 2 raw read data files</li> </ul> <p>The sample for this activity was derived from data collected at Virginia Commonwealth University in Richmond, VA. The researchers collected the sample with the goal of being able to track the spread and evolution of this virus state-wide, nationally, and internationally. You can download the original data <a href="https://www.ncbi.nlm.nih.gov/sra/?term=XGTK449087">here</a>.</p> <p>You can download the reference genome <a href="https://www.ncbi.nlm.nih.gov/nuccore/1798174254">here</a>.</p>
Galaxy Training Network - Screening assembled genomes for contamination using NCBI FCS
<p>This is a toy dataset generated from an assembled yeast genome (Saccharomyces cerevisiae; GCA_000146045.2) with contaminants added from human, Pseudomonas aeruginosa, and Illumina PCR primers.</p>
Contaminated genome assembly for decontamination training as part of the Galaxy Training Network
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Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network - Example Run Output
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STOC indicator trend - Galaxy Training Network
<p>Example file for the tutorial STOC indicator trend</p>
Dataset for Mitogenome Assembly Training, part of the Galaxy Training Network (GTN)
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Determining multivariable association between various meta'omic features using MaAslin2: Galaxy training network
<p>MaAsLin2 is comprehensive R package for efficiently determining multivariable association between phenotypes, environments, exposures, covariates and microbial meta’omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods.</p> <p>https://huttenhower.sph.harvard.edu/maaslin/</p>
Test Dataset for running Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network
<p>Test Dataset for running Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.