Workflows
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scRNA-Seq pipelines
Here we forge the tools to analyze single cell RNA-Seq experiments. The analysis workflow is based on the Bioconductor packages scater and scran as well as the Bioconductor workflows by Lun ATL, McCarthy DJ, & Marioni JC [*A step-by-step workflow for low-level analysis of single-cell RNA-seq ...
scRNA-Seq pipelines
Here we forge the tools to analyze single cell RNA-Seq experiments. The analysis workflow is based on the Bioconductor packages scater and scran as well as the Bioconductor workflows by Lun ATL, McCarthy DJ, & Marioni JC [*A step-by-step workflow for low-level analysis of single-cell RNA-seq ...
This workflow performs the process of protein-ligand docking, step by step, using the BioExcel Building Blocks library (biobb).
Abstract CWL Automatically generated from the Galaxy workflow file: GTN 'Pangeo 101 for everyone - Introduction to Xarray'.
In this tutorial, we analyze particle matter < 2.5 μm/m3 data from Copernicus Atmosphere Monitoring Service to understand Xarray Galaxy Tools:
- Understand how an Xarray dataset is organized;
- Get metadata from Xarray dataset such as variable names, units, coordinates (latitude, longitude, level), etc;
- Plot an Xarray dataset on a geographical map and learn to customize ...
Protein-ligand complex parameterization
Parameterizes an input protein (PDB) and ligand (SDF) file prior to molecular dynamics simulation with GROMACS.
This is a simple workflow intended for use as a subworkflow in more complex MD workflows. It is used as a subworkflow by the GROMACS MMGBSA and dcTMD workflows.
COVID-19: variation analysis on ARTIC ONT data
This workflow for ONT-sequenced ARTIC data is modeled after the alignment/variant-calling steps of the ARTIC pipeline. It performs, essentially, the same steps as that pipeline’s minion command, i.e. read mapping with minimap2 and variant calling with medaka. Like the Illumina ARTIC workflow it uses ivar for primer trimming. Since ONT-sequenced reads have a much ...
COVID-19: variation analysis on WGS PE data
This workflows performs paired end read mapping with bwa-mem followed by sensitive variant calling across a wide range of AFs with lofreq and variant annotation with snpEff 4.5covid19.
This workflow extracts 5 different time periods e.g. January- June 2019, 2020 and 2021, July-December 2019 and 2020 over a single selected location. Then statistics (mean, minimum, maximum) are computed. The final products are maximum, minimum and mean.
Description
The workflow takes an input file with Cancer Driver Genes predictions (i.e. the results provided by a participant), computes a set of metrics, and compares them against the data currently stored in OpenEBench within the TCGA community. Two assessment metrics are provided for that predictions. Also, some plots (which are optional) that allow to visualize the performance of the tool are generated. The workflow consists in three standard steps, defined by OpenEBench. The tools needed ...
Type: Nextflow
Creators: José Mª Fernández, Asier Gonzalez-Uriarte, Javier Garrayo-Ventas
Submitter: Laura Rodriguez-Navas