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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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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.
U.S. Government Workshttps://www.usa.gov/government-works
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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.
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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:
The files included are:
U.S. Government Workshttps://www.usa.gov/government-works
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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.
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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.
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Reference and custom annotation data expected as input by Galaxy SARS-CoV-2 variation analysis workflows developed by covid19.galaxyproject.org
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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.
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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
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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.
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.
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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)
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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).
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Datasets for assembly tutotorials.
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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).
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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.
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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”).
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The table summarizes the Metavisitor report files available as S16 and S17 Files.
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The data provided here is part of a Galaxy Training Network tutorial for genome annotation with Apollo.
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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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.