Workflows

This Galaxy-E workflow was made from the "Cleaning GBIF data for the use in biogeography" tutorial and allows to:

• Use CoordinateCleaner to automatically flag problematic records
• Use GBIF provided meta-data to improve coordinate quality, tailored to your downstream analyses
• Use automated cleaning algorithms of CoordinateCleaner to identify problematic contributing datasets
• Visualize data ...

Introduction

vibbits/rnaseq-editing is a bioinformatics pipeline that can be used to analyse RNA sequencing data obtained from organisms with a reference genome and annotation followed by a prediction step of editing sites using RDDpred.

The pipeline is largely based on the nf-core RNAseq pipeline.

The initial nf-core pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable ...

Workflow for Metagenomics from bins to metabolic models (GEMs)

Summary

• Prodigal gene prediction
• CarveMe genome scale metabolic model reconstruction
• MEMOTE for metabolic model testing
• SMETANA Species METabolic interaction ANAlysis

Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default

All tool CWL files and other workflows can be found here: Tools: https://gitlab.com/m-unlock/cwl Workflows: https://gitlab.com/m-unlock/cwl/workflows

**How ...

Workflow for Metagenomics binning from assembly

Minimal inputs are: Identifier, assembly (fasta) and a associated sorted BAM file

Summary

• MetaBAT2 (binning)
• MaxBin2 (binning)
• SemiBin (binning)
• DAS Tool (bin merging)
• EukRep (eukaryotic classification)
• CheckM (bin completeness and contamination)
• BUSCO (bin completeness)
• GTDB-Tk (bin taxonomic classification)

Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default

**All tool CWL ...

Workflow Kallisto RNAseq

(pseudoalignment on transcripts)

• Workflow Illumina Quality: https://workflowhub.eu/workflows/336?version=1
• kallisto

All tool CWL files and other workflows can be found here:
Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl
Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows

How to setup and use an UNLOCK workflow:
https://m-unlock.gitlab.io/docs/setup/setup.html

- deprecated -

Workflow for sequencing with ONT Nanopore, from basecalling to assembly.

• Guppy (basecalling of raw reads)
• MinIONQC (quality check)
• FASTQ merging from multi into one file
• Kraken2 (taxonomic classification)
• Krona (classification visualization)
• Flye (de novo assembly)
• Medaka (assembly polishing)
• QUAST (assembly quality reports)

All tool CWL files and other workflows can be found here: Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl Workflows: ...

Joint multi-omics dimensionality reduction approaches for CAKUT data using peptidome and proteome data

Brief description In (Cantini et al. 2020), Cantini et al. evaluated 9 representative joint dimensionality reduction (jDR) methods for multi-omics integration and analysis and . The methods are Regularized Generalized Canonical Correlation Analysis (RGCCA), Multiple co-inertia analysis (MCIA), Multi-Omics Factor Analysis (MOFA), Multi-Study Factor Analysis (MSFA), iCluster, Integrative NMF ...

For integrative analysis of CAKUT multi-omics data DIABLO method of the mixOmics package (version 6.10.9. Singh et. al. 2019) was used with sPLS-DA (sparse Partial Least Squares Discriminant Analysis Discriminant Analysis) and PLS-DA classification.

In this analysis, we created an extended pathway, using the WikiPathways repository (Version 20210110) and the three -omics datasets. For this, each of the three -omics datasets was first analyzed to identify differentially expressed elements, and pathways associated with the significant miRNA-protein links were detected. A miRNA-protein link is deemed significant, and may possibly be implying causality, if both a miRNA and its target are significantly differentially expressed.

The peptidome and ...

This workflow can be used to fit dose-response curves from normalised cell-based assay data (%confluence) using the KNIME HCS extension. The workflow expects triplicates for each of eight test concentrations. This workflow needs R-Server to run in the back-end. Start R and run the following command: library(Rserve); Rserve(args = "--vanilla"). Three types of outliers can be removed: 1 - Outliers from triplicate measurement (standard deviation cut-off can be selected), 2 - inactive and weekly ...