Galaxy workflow from Galaxy 101 for everyone. This workflow is used in the training "How to reproduce published Galaxy analyses" to learn how to run a published Galaxy workflow.

Current versions of all published workflows can be accessed at https://cpt.tamu.edu/galaxy-pub/workflows/list_published. (XLSX)

The table summarizes the report generated by Metavisitor from a batch of 40 sequence datasets (S14 File). Metadata associated with each indicated sequence dataset as well as the ability of Metavisitor to detect HIV in datasets and patients are indicated.

The way we process and analyze catalysis research data is revolutionazing. Galaxy, the open-source platform, transforms complex data processing and analysis into a seamless, user-friendly experience.

Ever wished for a time machine in your research? Galaxy's workflow tools allow you to recreate and share your analyses with ease, ensuring reproducibility and transparency in your catalysis studies.

How to Navigate Galaxy for catalysis-related research? Dr. Abraham Nieva de la Hidalga from UK Catalysis Hub will answer some of your questions on this topic. This video is a part of series of a Flash Pitch Event which took place at Annual Digital Catalysis & Catalysis-Related Sciences Conference (ADCR23) on 3rd of November 2023.

More information about the presentation at: https://zenodo.org/records/10172120

Stay tuned for more exciting content, and thank you for being a part of our growing community!

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The Data of this table were extracted from the Metavisitor report file available as S15 File. Values of the column “Coverage of complete viral genome (%)” correspond to the fractions (in %) of the complete viral genomes that are covered by blast hits of viral contigs to these genomes and values of the column “Mean blast bit score” correspond to the mean values of the bit scores observed for these blast hits. Note that blast alignments to incomplete viral genomes were not taken into account. For detection of false positives, reads were aligned to the bowtie2 vir1 index before de novo assembly and counts of these reads were reported in the column “Read mapping to vir1 using bowtie2”).

This dataset contains the workflow file, ready to import and use in the Galaxy portal (https://usegalaxy.eu/). The workflow can integrate four distinct datasets covering the Baltic Sea region (13-31°E, 53-66°N) to create comprehensive multi-layer geospatial visualizations for marine environmental analysis. The workflow combines biological, physical oceanographic, environmental monitoring, and bathing water quality data into a unified analytical framework.

Input dataset for Galaxy Training Material for the Analyze unaligned ncRNAs workflow.

See https://github.com/galaxyproject/training-material for more information.

Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological, and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL, and HomoloGene, to identify gene families and visualize syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries among multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences, provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families. A certain amount of expertise is required to configure and run the Ensembl Compara GeneTrees pipeline via command line. Therefore, we converted this pipeline into a Galaxy workflow, called GeneSeqToFamily, and provided additional functionality. This workflow uses existing tools from the Galaxy ToolShed, as well as providing additional wrappers and tools that are required to run the workflow. GeneSeqToFamily represents the Ensembl GeneTrees pipeline as a set of interconnected Galaxy tools, so they can be run interactively within the Galaxy's user-friendly workflow environment while still providing the flexibility to tailor the analysis by changing configurations and tools if necessary. Additional tools allow users to subsequently visualize the gene families produced by the workflow, using the Aequatus.js interactive tool, which has been developed as part of the Aequatus software project.

The table summarizes the Metavisitor report files available as S16 and S17 Files.

This is the input dataset for the MFAssignR Galaxy training workflow. The input dataset corresponds to the model data of MFAssignR (Raw_Neg_ML), containing a raw mass list, measured in a negative ESI mode.

Test Data for Galaxy tutorial "Clustering 3k PBMCs with Seurat" - SCTransform workflow

This is a bioinformatics exercise intended for use in a computer lab setting with life science majors.

See Method section for a description of the columns.

Metavisitor is a software package that allows biologists and clinicians without specialized bioinformatics expertise to detect and assemble viral genomes from deep sequence datasets. The package is composed of a set of modular bioinformatic tools and workflows that are implemented in the Galaxy framework. Using the graphical Galaxy workflow editor, users with minimal computational skills can use existing Metavisitor workflows or adapt them to suit specific needs by adding or modifying analysis modules. Metavisitor works with DNA, RNA or small RNA sequencing data over a range of read lengths and can use a combination of de novo and guided approaches to assemble genomes from sequencing reads. We show that the software has the potential for quick diagnosis as well as discovery of viruses from a vast array of organisms. Importantly, we provide here executable Metavisitor use cases, which increase the accessibility and transparency of the software, ultimately enabling biologists or clinicians to focus on biological or medical questions.

This is a multiple regression analysis workflow designed to predict algal bloom risk in the Baltic Sea based on oceanographic and nutrient data. The workflow combines data preprocessing, statistical modeling, and spatial visualization to assess water quality at bathing sites.

This dataset is associated with the Galaxy workflow "Cloud-Aerosole MT-MG Pre-Processing"

Main publication Poll report and form on HAL Authors The raw data was generated by the poll respondents The authors of this Dataset, excluding Vlad Visan, are such respondents. There are also other respondents who chose to remain anonymous The script was written by Vlad Visan The raw format was adapted to a numerical format by Vlad Visan Overall description A poll took place in February 2024, to understand the administrative burden of using Galaxy, specifically for small-scale admins. Context Useful to anyone considering using Galaxy Done as part of the technology monitoring phase of the "Gestionnaire de workflows" (Workflow Management System) project of the OSUG LabEx File descriptions raw_data_names_removed.tsv Raw poll answers. With any personally identifiable information redacted. SSA-Poll-19-Feb-2024-Filtered-Numerical.tab This numerically filtered format is required by the script The transformation could be done automatically in the future, but there are some subtleties: "-1" denotes "ignore/invalid" Some empty answers have to manually be converted to "0" I manually changed one answer that was "0" to "-1" after reading the associated comment which made it clear that "invalid" was more appropriate numericalCsvImportAndGenerateCharts.R The script parses the data, and creates one distribution/histogram graph per column It expects a filtered version, with only the numerical fields. Form-V2.pdf Survey questions, with several errors corrected: End-user assistance questions were worded wrongly Various spelling/wording mistakes

This dataset provides the inputs needed for the Galaxy Pathway Analysis workflow training tutorial (https://galaxy-synbiocad.org).

This workflow asseses the performance of predicted pathways by computing 4 criteria (target product flux, thermodynamic feasibility, pathway length, and enzyme availability). A score inform the user about the best candidate pathways to produce a compound of interest. The generated output is a collection of scored and ranked heterologous pathways.

The content of the dataset is as follows:

- A set of pathways provided in the SBML format (Systems Biology Markup Language) to be ranked, modeling heterologous pathways such as those outputted by the RetroSynthesis workflow (https://galaxy-synbiocad.org).

- The GEM (Genome-scale metabolic models) which is a formalized representation of the metabolism of the host organism (the model is E. coli iML1515), provided in the SBML format.

This is a training dataset for use in Galaxy materials science tutorials. These files can be used to demonstrate the AIRSS (Ab-Initio Random Structure Searching) method for finding muon stopping sites, using the UEP (Unperturbed Electrostatic Potential) technique for the optimisation stage of that method.

The files included are:

Si.cell: structure file containing atom locations
Si.den_fmt: electron density data, generated with CASTEP
Si.castep: CASTEP log file for the electron density calculation
Si-muairss-uep.yaml: configuration file for the AIRSS / UEP workflow

Data from https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?exp=SRX105188&cmd=search&m=downloads&s=seq and ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/002/985/GCA_000002985.3_WBcel235

For Galaxy Training https://rna.usegalaxy.eu/workflows/run?id=a108b575b16e6cb9