Fig 4 - uploaded by Nadia Goué
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Typical process to create a new tutorial. Authors usually start by identifying suitable input datasets, and developing the workflow in Galaxy. Thus, a workflow can be automatically converted into a tutorial skeleton using the PTDK described in the next section. The tutorial is then tested, reviewed and finally merged into the GTN.
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There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analys...
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... tutorials provide a step-by-step guide to creating a tutorial in the GTN framework, covering everything from technical guides about the framework to pedagogical best practices and submission of tutorials to GitHub. Figure 4 depicts the typical process a tutorial contributor will go through when developing a new tutorial. The first step is usually to develop the analysis workflow in Galaxy. ...
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Programming and computational thinking have (re)gained an increased focus in compulsory education worldwide, consequently demanding teachers of various subjects to engage in the teaching and learning of the two. A recent curriculum reform in Norway emphasised the development of students’ computational skills by integrating programming into four s...
Citations
... These are deployed within the Galaxy bioinformatics ecosystem [16], providing a workbench wherein scientists can share, analyze, and visualize their results in a reproducible manner, carrying out analyses on scalable computer resources accessed through a web browser interface. The ecosystem also provides extensive online and on-demand training material via the Galaxy Training Network [17], including guidance on the usage of the platform for SARS-CoV-2 studies [18]. ...
The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a major health crisis worldwide with its continuously emerging new strains, resulting in new viral variants that drive “waves” of infection. PCR or antigen detection assays have been routinely used to detect clinical infections; however, the emergence of these newer strains has presented challenges in detection. One of the alternatives has been to detect and characterize variant-specific peptide sequences from viral proteins using mass spectrometry (MS)-based methods. MS methods can potentially help in both diagnostics and vaccine development by understanding the dynamic changes in the viral proteome associated with specific strains and infection waves. In this study, we developed an accessible, flexible, and shareable bioinformatics workflow that was implemented in the Galaxy Platform to detect variant-specific peptide sequences from MS data derived from the clinical samples. We demonstrated the utility of the workflow by characterizing published clinical data from across the world during various pandemic waves. Our analysis identified six SARS-CoV-2 variant-specific peptides suitable for confident detection by MS in commonly collected clinical samples.
