85 datasets found
  1. Z

    Training data for 'Unicycler assembly of SARS-CoV-2 genome with...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Aug 4, 2022
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    Maier, Wolfgang (2022). Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3732358
    Explore at:
    Dataset updated
    Aug 4, 2022
    Dataset authored and provided by
    Maier, Wolfgang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose:

    as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings.

    to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.

  2. Training material for the course "Exome analysis with GALAXY"

    • zenodo.org
    • explore.openaire.eu
    bin, txt, vcf
    Updated Jan 24, 2020
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    Paolo Uva; Gianmauro Cuccuru; Paolo Uva; Gianmauro Cuccuru (2020). Training material for the course "Exome analysis with GALAXY" [Dataset]. http://doi.org/10.5281/zenodo.61377
    Explore at:
    bin, txt, vcfAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Paolo Uva; Gianmauro Cuccuru; Paolo Uva; Gianmauro Cuccuru
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Galaxy is an open source, web-based platform for data intensive biomedical research. It makes accessible bioinformatics applications to users lacking programming skills, enabling them to easily build analysis workflows for NGS data.

    The course "Exome analysis using Galaxy" is aimed at PhD student, biologists, clinicians and researchers who are analysing, or need to analyse in the near future, high throughput exome sequencing data. The aim of the course is to make participants familiarise with the Galaxy platform and prepare them to work independently, using state-of-the art tools for the analysis of exome sequencing data.

    The course will be delivered using a mixture of lectures and computer based hands-on practical sessions. Lectures will provide an up-to-date overview of the strategies for the analysis of exome next-generation experiments, starting from the raw sequence data. Analyses include sequence quality control, alignment to a reference genome, refinement of aligned sequences, variant calling, annotation and interpretation, and tools for visual inspection of results. Participants will apply the knowledge gained during the course to the analysis of Illumina’s real exome datasets, and implement workflows to reproduce the complete analysis. After the course, participants will be able to create pipeline for their individual analyses.

    Those are the needed datasets for this course.

  3. g

    Software and supporting data for Colib'read on Galaxy.

    • gigadb.org
    • explore.openaire.eu
    Updated Jan 20, 2016
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    (2016). Software and supporting data for Colib'read on Galaxy. [Dataset]. http://doi.org/10.5524/100170
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    Dataset updated
    Jan 20, 2016
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    With NGS technologies, life sciences face a raw data deluge. Classical analysis processes of such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to directly focus on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools. Dedicated to ”whole genome assembly-free” treatments, the Colib’read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of de Bruijn graph and bloom filter, such analyses can be performed in few hours, using small amounts of memory. Applications on real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories. With the Colib’read Galaxy tools suite, we give the possibility to a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows to keep the maximum of biological information from data and use very low memory footprint.

  4. Bacterial training dataset for Galaxy training network tutorials on Genome...

    • zenodo.org
    • explore.openaire.eu
    bin
    Updated Jan 24, 2020
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    Simon Gladman; Torsten Seemann; Dieter Bulach; Simon Gladman; Torsten Seemann; Dieter Bulach (2020). Bacterial training dataset for Galaxy training network tutorials on Genome assembly [Dataset]. http://doi.org/10.5281/zenodo.582600
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Simon Gladman; Torsten Seemann; Dieter Bulach; Simon Gladman; Torsten Seemann; Dieter Bulach
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This training dataset is from an imaginary Staphylococcus aureus 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.

    It is a useful dataset for demonstrating:

    • de novo genome assembly
    • read mapping and variant calling
    • genome annotation

    The files included are:

    • wildtype.fna: the reference genome sequence of the wildtype strain in fasta format (a header line, then the nucleotide sequence of the genome.)
    • wildtype.gff: 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.)
    • wildtype.gbk: the reference genome sequence in genbank format.
    • mutant_R1.fastq and mutant_R2.fastq: Fastq sequence reads of a closely related mutant strain.
      • The reads are paired-end.
      • Each read is 150 bases long.
      • The number of bases sequenced is equivalent to 19x the genome sequence of the wildtype strain. (Read coverage 19x - rather low!).
  5. g

    Supporting data and materials for "NCBI BLAST+ integrated into Galaxy".

    • aspera.gigadb.org
    • explore.openaire.eu
    Updated Aug 12, 2015
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    (2015). Supporting data and materials for "NCBI BLAST+ integrated into Galaxy". [Dataset]. http://doi.org/10.5524/100149
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    Dataset updated
    Aug 12, 2015
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    The NCBI BLAST suite has become ubiquitous in modern molecular biology and is used for small tasks such as checking capillary sequencing results of single PCR products, genome annotation or even larger scale pan-genome analyses. For early adopters of the Galaxy web-based biomedical data analysis platform, integrating BLAST into Galaxy was a natural step for sequence comparison workflows. Here we provide the command line NCBI BLAST+ tool suite wrapped for use within Galaxy.
    The integration of the BLAST+ tool suite into Galaxy has the goal of making common BLAST tasks easy and advanced tasks possible. This project is an informal international collaborative effort, it is deployed and used on Galaxy servers worldwide.

  6. f

    Flexible and Accessible Workflows for Improved Proteogenomic Analysis Using...

    • acs.figshare.com
    • figshare.com
    xlsx
    Updated Jun 3, 2023
    + more versions
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    Pratik D. Jagtap; James E. Johnson; Getiria Onsongo; Fredrik W. Sadler; Kevin Murray; Yuanbo Wang; Gloria M. Shenykman; Sricharan Bandhakavi; Lloyd M. Smith; Timothy J. Griffin (2023). Flexible and Accessible Workflows for Improved Proteogenomic Analysis Using the Galaxy Framework [Dataset]. http://doi.org/10.1021/pr500812t.s003
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    xlsxAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    ACS Publications
    Authors
    Pratik D. Jagtap; James E. Johnson; Getiria Onsongo; Fredrik W. Sadler; Kevin Murray; Yuanbo Wang; Gloria M. Shenykman; Sricharan Bandhakavi; Lloyd M. Smith; Timothy J. Griffin
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

  7. Z

    SARS-CoV-2 genomics resources for Galaxy

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Jan 22, 2022
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    Maier, Wolfgang (2022). SARS-CoV-2 genomics resources for Galaxy [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4555734
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    Dataset updated
    Jan 22, 2022
    Dataset authored and provided by
    Maier, Wolfgang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Reference and custom annotation data expected as input by Galaxy SARS-CoV-2 variation analysis workflows developed by covid19.galaxyproject.org

  8. Training data for 'Genome annotation with Maker' tutorial (Galaxy Training...

    • zenodo.org
    • explore.openaire.eu
    • +1more
    application/gzip, bin
    Updated Dec 31, 2020
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    Anthony Bretaudeau; Anthony Bretaudeau (2020). Training data for 'Genome annotation with Maker' tutorial (Galaxy Training Material) [Dataset]. http://doi.org/10.5281/zenodo.1404209
    Explore at:
    bin, application/gzipAvailable download formats
    Dataset updated
    Dec 31, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anthony Bretaudeau; Anthony Bretaudeau
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data provided here are part of a Galaxy Training Network tutorial for genome annotation with Maker.

    It is based on data used in another Maker tutorial.

    The full genome was downloaded from NCBI, and mitochondria sequence removed from it for simplicity.

  9. Data from: PiRATE: a Pipeline to Retrieve and Annotate Transposable Elements...

    • seanoe.org
    bin
    Updated 2018
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    Jeremy Berthelier; Nathalie Casse; Nicolas Daccord; Véronique Jamilloux; Bruno Saint-Jean; Gregory Carrier (2018). PiRATE: a Pipeline to Retrieve and Annotate Transposable Elements [Dataset]. http://doi.org/10.17882/51795
    Explore at:
    binAvailable download formats
    Dataset updated
    2018
    Dataset provided by
    SEANOE
    Authors
    Jeremy Berthelier; Nathalie Casse; Nicolas Daccord; Véronique Jamilloux; Bruno Saint-Jean; Gregory Carrier
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    to date, genome assembly of non-model organisms is usually not at chromosomal level and higly fragmented. this fragmentation is recognized to be, in part, the result of a bad assembly of the transposable elements (tes) copies, increasing the difficulty to detect and annotate them.in this context, we designed a new bioinformatics pipeline named pirate for detect, classify and annotate tes of non-model organisms. pirate combines multiple analysis packages representing all the major approaches for te detection. the goal is to promote the detection of complete te sequences of every te families. the detection of complete te sequences, bearing recognizable conserved domains or specific motifs, allows to facilitate the classification step. the classification step of pirate has been optimized for algal genomes.each tools used by pirate are automated into a stand-alone galaxy. this pirate-galaxy can be used through a virtual machine, which can be download below.this pirate-galaxy is a suitable and flexible platform to study tes in the genome of every organisms.you can find a tutorial below.please contact us if you have any issues or comments : berthelier.j [at] laposte.net or gregory.carrier [at] ifremer.fror you can leave a message on github: https://github.com/jberthelier/pirate/issues

  10. Training sets for small genome assembly with Unicycler in Galaxy

    • zenodo.org
    • explore.openaire.eu
    bin
    Updated Jan 24, 2020
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    Delphine Lariviere; Delphine Lariviere (2020). Training sets for small genome assembly with Unicycler in Galaxy [Dataset]. http://doi.org/10.5281/zenodo.842795
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    binAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Delphine Lariviere; Delphine Lariviere
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Downsampled datasets for the small genome assembly in Galaxy training. These include 3 files : forward and reverse reads from Illumina sequencing , and Long read file from Oxford Nanopore sequencing.

  11. Data from: ReGaTE: Registration of Galaxy Tools in Elixir

    • ckan.earlham.ac.uk
    Updated Dec 30, 2018
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    ckan.earlham.ac.uk (2018). ReGaTE: Registration of Galaxy Tools in Elixir [Dataset]. https://ckan.earlham.ac.uk/dataset/ba54c894-e4ea-449e-897d-ae8b511365fe
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    Dataset updated
    Dec 30, 2018
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    Bioinformaticians routinely use multiple software tools and data sources in their day-to-day work and have been guided in their choices by a number of cataloguing initiatives. The ELIXIR Tools and Data Services Registry (bio.tools) aims to provide a central information point, independent of any specific scientific scope within bioinformatics or technological implementation. Meanwhile, efforts to integrate bioinformatics software in workbench and workflow environments have accelerated to enable the design, automation, and reproducibility of bioinformatics experiments. One such popular environment is the Galaxy framework, with currently more than 80 publicly available Galaxy servers around the world. In the context of a generic registry for bioinformatics software, such as bio.tools, Galaxy instances constitute a major source of valuable content. Yet there has been, to date, no convenient mechanism to register such services en masse. Findings: We present ReGaTE (Registration of Galaxy Tools in Elixir), a software utility that automates the process of registering the services available in a Galaxy instance. This utility uses the BioBlend application program interface to extract service metadata from a Galaxy server, enhance the metadata with the scientific information required by bio.tools, and push it to the registry. Conclusions: ReGaTE provides a fast and convenient way to publish Galaxy services in bio.tools. By doing so, service providers may increase the visibility of their services while enriching the software discovery function that bio.tools provides for its users. The source code of ReGaTE is freely available on Github at https://github.com/C3BI-pasteur-fr/ReGaTE.

  12. Additional file 3: Figure S2. of TRAPLINE: a standardized and automated...

    • springernature.figshare.com
    zip
    Updated May 30, 2023
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    Markus Wolfien; Christian Rimmbach; Ulf Schmitz; Julia Jung; Stefan Krebs; Gustav Steinhoff; Robert David; Olaf Wolkenhauer (2023). Additional file 3: Figure S2. of TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation [Dataset]. http://doi.org/10.6084/m9.figshare.c.3631766_D9.v1
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Markus Wolfien; Christian Rimmbach; Ulf Schmitz; Julia Jung; Stefan Krebs; Gustav Steinhoff; Robert David; Olaf Wolkenhauer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Visualization for RNA transcript quality control and comparison of per base quality score Q. The images are taken before (A) and after (B) quality trimming procedure (removes reads with Q ≤ 20) to estimate the effect of trimming. The quality score Q is plotted to the read position by using the FastQC package in Galaxy (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The color indicates the quality of the read: "red" low quality, "orange" median quality, "green" good quality. Red line expresses the mean of the measured values (yellow boxes are inter-quartile range) and the blue line represents the mean quality. (ZIP 81 kb)

  13. Training data for 'Genome annotation with Funannotate' tutorial (Galaxy...

    • zenodo.org
    application/gzip, bin
    Updated Apr 26, 2023
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    Anthony Bretaudeau; Anthony Bretaudeau; Alexandre Cormier; Alexandre Cormier; Stéphanie Robin; Stéphanie Robin; Erwan Corre; Erwan Corre; Laura Leroi; Laura Leroi (2023). Training data for 'Genome annotation with Funannotate' tutorial (Galaxy Training Material) [Dataset]. http://doi.org/10.5281/zenodo.5718469
    Explore at:
    application/gzip, binAvailable download formats
    Dataset updated
    Apr 26, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anthony Bretaudeau; Anthony Bretaudeau; Alexandre Cormier; Alexandre Cormier; Stéphanie Robin; Stéphanie Robin; Erwan Corre; Erwan Corre; Laura Leroi; Laura Leroi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data provided here are part of a Galaxy Training Network tutorial for genome annotation with funannotate.

    Genome was assembled following the GTN Flye assembly tutorial, then masked with RepeatMasker.

    RNASeq data: SRR8534859 reads were mapped to the genome using STAR (toolshed.g2.bx.psu.edu/repos/iuc/rgrnastar/rna_star/2.7.8a+galaxy0), then the bam was downsampled (15% with toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_DownsampleSam/2.18.2.1) to reduce the size of the dataset. Fastq files were then extracted from the resulting bam file (toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_SamToFastq/2.18.2.1).

    SwissProt_subset.fasta is a subset of SwissProt proteins that are known to have some similarity with the genome (found using Diamond against the genome, then extracting sequences matching with e-value < 0.0001).

  14. Sample data for Galaxy genome assembly tutorials

    • zenodo.org
    application/gzip
    Updated Oct 24, 2023
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    Anton Nekrutenko; Anton Nekrutenko; Delphine Lariviere; Delphine Lariviere; Cristobal Gallardo; Cristobal Gallardo; Alex Ostrovsky; Alex Ostrovsky (2023). Sample data for Galaxy genome assembly tutorials [Dataset]. http://doi.org/10.5281/zenodo.10034946
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    application/gzipAvailable download formats
    Dataset updated
    Oct 24, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anton Nekrutenko; Anton Nekrutenko; Delphine Lariviere; Delphine Lariviere; Cristobal Gallardo; Cristobal Gallardo; Alex Ostrovsky; Alex Ostrovsky
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Datasets for assembly tutotorials.

  15. Training data for 'Genome annotation with Funannotate' tutorial (Galaxy...

    • zenodo.org
    application/gzip, bin
    Updated Apr 26, 2023
    + more versions
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    Anthony Bretaudeau; Anthony Bretaudeau; Alexandre Cormier; Alexandre Cormier; Stéphanie Robin; Stéphanie Robin; Erwan Corre; Erwan Corre; Laura Leroi; Laura Leroi (2023). Training data for 'Genome annotation with Funannotate' tutorial (Galaxy Training Material) [Dataset]. http://doi.org/10.5281/zenodo.5726818
    Explore at:
    bin, application/gzipAvailable download formats
    Dataset updated
    Apr 26, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anthony Bretaudeau; Anthony Bretaudeau; Alexandre Cormier; Alexandre Cormier; Stéphanie Robin; Stéphanie Robin; Erwan Corre; Erwan Corre; Laura Leroi; Laura Leroi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data provided here are part of a Galaxy Training Network tutorial for genome annotation with funannotate.

    Genome was assembled following the GTN Flye assembly tutorial, then masked with RepeatMasker.

    RNASeq data: SRR8534859 reads were mapped to the genome using STAR (toolshed.g2.bx.psu.edu/repos/iuc/rgrnastar/rna_star/2.7.8a+galaxy0), then the bam was downsampled (10% with toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_DownsampleSam/2.18.2.1) to reduce the size of the dataset. Fastq files were then extracted from the resulting bam file (toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_SamToFastq/2.18.2.1).

    SwissProt_subset.fasta is a subset of SwissProt proteins that are known to have some similarity with the genome (found using Diamond against the genome, then extracting sequences matching with e-value < 0.0001).

  16. f

    Metavisitor, a Suite of Galaxy Tools for Simple and Rapid Detection and...

    • plos.figshare.com
    pdf
    Updated May 31, 2023
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    Guillaume Carissimo; Marius van den Beek; Kenneth D. Vernick; Christophe Antoniewski (2023). Metavisitor, a Suite of Galaxy Tools for Simple and Rapid Detection and Discovery of Viruses in Deep Sequence Data [Dataset]. http://doi.org/10.1371/journal.pone.0168397
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    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Guillaume Carissimo; Marius van den Beek; Kenneth D. Vernick; Christophe Antoniewski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  17. f

    Summary of virus detection in 36 traceable patients of the Use Case 3–2.

    • plos.figshare.com
    xls
    Updated Jun 5, 2023
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    Guillaume Carissimo; Marius van den Beek; Kenneth D. Vernick; Christophe Antoniewski (2023). Summary of virus detection in 36 traceable patients of the Use Case 3–2. [Dataset]. http://doi.org/10.1371/journal.pone.0168397.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Guillaume Carissimo; Marius van den Beek; Kenneth D. Vernick; Christophe Antoniewski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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”).

  18. f

    Summary of detection of Ebola and Lassa viruses in Use Case 3–3.

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Guillaume Carissimo; Marius van den Beek; Kenneth D. Vernick; Christophe Antoniewski (2023). Summary of detection of Ebola and Lassa viruses in Use Case 3–3. [Dataset]. http://doi.org/10.1371/journal.pone.0168397.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Guillaume Carissimo; Marius van den Beek; Kenneth D. Vernick; Christophe Antoniewski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

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

  19. Z

    Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Aug 4, 2022
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    Anthony Bretaudeau (2022). Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training Material) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4889109
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    Dataset updated
    Aug 4, 2022
    Dataset provided by
    Helena Rasche
    Anthony Bretaudeau
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data provided here is part of a Galaxy Training Network tutorial for genome annotation with Apollo.

  20. o

    WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods...

    • explore.openaire.eu
    Updated Oct 26, 2022
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    Gareth Price; Johan Gustafsson (2022). WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia [Dataset]. http://doi.org/10.5281/zenodo.7251309
    Explore at:
    Dataset updated
    Oct 26, 2022
    Authors
    Gareth Price; Johan Gustafsson
    Area covered
    Australia
    Description

    This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022. Event description Have you discovered a brilliant bioinformatics workflow but you’re not quite sure how to use it? In this webinar we will introduce the power of Galaxy for construction and (re)use of reproducible workflows, whether building workflows from scratch, recreating them from published descriptions and/or extracting from Galaxy histories. Using an established bioinformatics method, we’ll show you how to: Use the workflows creator in Galaxy Australia Build a workflow based on a published method Annotate workflows so that you (and others) can understand them Make workflows finable and citable (important and very easy to do!) Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. GalaxyWorkflows_Slides (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/IMkl6p7hkho

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Maier, Wolfgang (2022). Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3732358

Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)

Explore at:
Dataset updated
Aug 4, 2022
Dataset authored and provided by
Maier, Wolfgang
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose:

as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings.

to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.

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