Workflows

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831 Workflows visible to you, out of a total of 904
ChIP-seq
Stable

ChIP-Seq pipeline

Here we provide the tools to perform paired end or single read ChIP-Seq analysis including raw data quality control, read mapping, peak calling, differential binding analysis and functional annotation. As input files you may use either zipped fastq-files (.fastq.gz) or mapped read data (.bam files). In case of paired end reads, corresponding fastq files should be named using .R1.fastq.gz and .R2.fastq.gz suffixes.

Pipeline Workflow

All analysis steps are illustrated in ...

Type: Bpipe

Creator: Sergi Sayols

Submitter: Sergi Sayols

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DNA-seq
Stable

DNA-Seq pipeline

Here we provide the tools to perform paired end or single read DNA-Seq analysis including raw data quality control, read mapping, variant calling and variant filtering.

Pipeline Workflow

All analysis steps are illustrated in the pipeline ...

Type: Bpipe

Creator: Sergi Sayols

Submitter: Sergi Sayols

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scRNA-seq MARS-seq
Stable

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 ...

Type: Bpipe

Creator: Sergi Sayols

Submitter: Sergi Sayols

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scRNA-seq Smart-seq 2
Stable

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 ...

Type: Bpipe

Creator: Sergi Sayols

Submitter: Sergi Sayols

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Protein-ligand docking (fpocket)
Stable

This workflow performs the process of protein-ligand docking, step by step, using the BioExcel Building Blocks library (biobb).

Type: Common Workflow Language

Creators: Adam Hospital, Genís Bayarri

Submitter: Genís Bayarri

DOI: 10.48546/workflowhub.workflow.257.1

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Pangeo 101 for everyone - introduction to Xarray
Work-in-progress

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 ...

Type: Galaxy

Creators: None

Submitter: Anne Fouilloux

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protein-ligand-complex-parameterization/main

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.

Type: Galaxy

Creator: Simon Bray

Submitter: WorkflowHub Bot

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sars-cov-2-ont-artic-variant-calling/COVID-19-ARTIC-ONT

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 ...

Type: Galaxy

Creator: Wolfgang Maier

Submitter: WorkflowHub Bot

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sars-cov-2-pe-illumina-wgs-variant-calling/COVID-19-PE-WGS-ILLUMINA

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.

Type: Galaxy

Creator: Wolfgang Maier

Submitter: WorkflowHub Bot

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Investigation of lockdown effect on air quality between January 2019 to May 2021.
Stable

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.

Type: Galaxy

Creators: None

Submitter: Anne Fouilloux

DOI: 10.48546/workflowhub.workflow.251.1

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