Microbiome - Taxonomy Profiling

Pathogens of all samples report generation and visualization

Microbiome - QC and Contamination Filtering

Nanopore datasets analysis - Phylogenetic Identification - antibiotic resistance genes detection and contigs building

Microbiome - Variant calling and Consensus Building

Annotation of an assembled bacterial genomes to detect genes, potential plasmids, integrons and Insertion sequence (IS) elements.

Antimicrobial resistance gene detection from assembled bacterial genomes

Short paired-end read analysis to provide quality analysis, read cleaning and taxonomy assignation

Assemble long reads with Flye, then view assembly statistics and assembly graph

Racon polish with long reads, x4

Assembly of bacterial paired-end short read data with generation of quality metrics and reports

This workflow is composed with the XCMS tool R package (Smith, C.A. 2006) able to extract, filter, align and fill gapand the possibility to annotate isotopes, adducts and fragments using the CAMERA R package (Kuhl, C 2012). https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-preprocessing/tutorial.html

This workflow is composed with the XCMS tool R package (Smith, C.A. 2006) able to extract and the metaMS R package (Wehrens, R 2014) for the field of untargeted metabolomics. https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gcms/tutorial.html

Importing single-end multiplexed data (not demultiplexed yet)

Importing paired-end multiplexed data (not demultiplexed yet)

Importing demultiplexed data (single-end)

Importing demultiplexed data (paired-end)

Use DADA2 for sequence quality control. DADA2 is a pipeline for detecting and correcting (where possible) Illumina amplicon sequence data. As implemented in the q2-dada2 plugin, this quality control process will additionally filter any phiX reads (commonly present in marker gene Illumina sequence data) that are identified in the sequencing data, and will filter chimeric sequences.

Use DADA2 for sequence quality control. DADA2 is a pipeline for detecting and correcting (where possible) Illumina amplicon sequence data. As implemented in the q2-dada2 plugin, this quality control process will additionally filter any phiX reads (commonly present in marker gene Illumina sequence data) that are identified in the sequencing data, and will filter chimeric sequences.

This workflow - Reconstruct phylogeny (insert fragments in a reference) - Alpha rarefaction analysis - Taxonomic analysis

The first step in hypothesis testing in microbial ecology is typically to look at within- (alpha) and between-sample (beta) diversity. We can calculate diversity metrics, apply appropriate statistical tests, and visualize the data using the q2-diversity plugin.

dada2 amplicon analysis for paired end data The workflow has three main outputs: - the sequence table (output of makeSequenceTable) - the taxonomy (output of assignTaxonomy) - the counts which allow to track the number of sequences in the samples through the steps (output of sequence counts)

Automated inference of stable isotope incorporation rates in proteins for functional metaproteomics

Clinical Metaproteomics 4: Quantitation

Perform dcTMD free energy simulations and calculations

MMGBSA simulation and calculation

Virtual screening of the SARS-CoV-2 main protease with rDock and pose scoring

Workflow for variant analysis against a reference genome in GenBank format

This workflow takes a VCF dataset of variants produced by any of the variant calling workflows in https://github.com/galaxyproject/iwc/tree/main/workflows/sars-cov-2-variant-calling and generates tabular lists of variants by Samples and by Variant, and an overview plot of variants and their allele-frequencies.

This workflow performs segmentation and counting of cell nuclei using fluorescence microscopy images. The segmentation step is performed using Otsu thresholding (Otsu, 1979). The workflow is based on the tutorial: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/imaging-introduction/tutorial.html

Downloads fastq files for sequencing run accessions provided in a text file using fasterq-dump. Creates one job per listed run accession.

This workflow takes as input a SRA_manifest from SRA Run Selector and will generate one fastq file or fastq pair of file for each experiment (concatenated multiple runs if necessary). Output will be relabelled to match the column specified by the user.

A workflow for the analysis of pox virus genomes sequenced as half-genomes (for ITR resolution) in a tiled-amplicon approach

This workflow processes the CMO fastqs with CITE-seq-Count and include the translation step required for cellPlex processing. In parallel it processes the Gene Expresion fastqs with STARsolo, filter cells with DropletUtils and reformat all outputs to be easily used by the function 'Read10X' from Seurat.

This workflow processes the Gene Expresion fastqs with STARsolo, filter cells with DropletUtils and reformat all outputs to be easily used by the function 'Read10X' from Seurat.

Run velocyto to get loom with counts of spliced and unspliced. It will extract the 'barcodes' from the bundled outputs.

Run velocyto to get loom with counts of spliced and unspliced

Run baredSC in 1 dimension in logNorm for 1 to N gaussians and combine models.

Run baredSC in 2 dimensions in logNorm for 1 to N gaussians and combine models.

This workflows performs paired end read mapping with bwa-mem followed by sensitive variant calling across a wide range of AFs with lofreq

This workflow takes a VCF dataset of variants produced by any of the *-variant-calling workflows in https://github.com/galaxyproject/iwc/tree/main/workflows/sars-cov-2-variant-calling and generates tabular lists of variants by Samples and by Variant, and an overview plot of variants and their allele-frequencies.

Build a consensus sequence from FILTER PASS variants with intrasample allele-frequency above a configurable consensus threshold. Hard-mask regions with low coverage (but not consensus variants within them) and ambiguous sites.

The workflow for Illumina-sequenced ARTIC data builds on the RNASeq workflow for paired-end data using the same steps for mapping and variant calling, but adds extra logic for trimming ARTIC primer sequences off reads with the ivar package. In addition, this workflow uses ivar also to identify amplicons affected by ARTIC primer-binding site mutations and tries to exclude reads derived from such tainted amplicons when calculating allele-frequencies of other variants.

Find and annotate variants in ampliconic SARS-CoV-2 Illumina sequencing data and classify samples with pangolin and nextclade

This workflows performs single end read mapping with bowtie2 followed by sensitive variant calling across a wide range of AFs with lofreq

This workflow for ONT-sequenced ARTIC data is modeled after the alignment/variant-calling steps of the [ARTIC pipeline](https://artic.readthedocs.io/en/latest/). 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 higher error rate than Illumina-sequenced reads and are therefor plagued more by false-positive variant calls, this workflow does make no attempt to handle amplicons affected by potential primer-binding site mutations.

This workflow takes as input a collection of paired fastq. It will remove bad quality and adapters with cutadapt. Map with Bowtie2 end-to-end. Will remove reads on MT and unconcordant pairs and pairs with mapping quality below 30 and PCR duplicates. Will compute the pile-up on 5' +- 100bp. Will call peaks and count the number of reads falling in the 1kb region centered on the summit. Will compute 2 normalization for coverage: normalized by million reads and normalized by million reads in peaks. Will plot the number of reads for each fragment length.

This workflow takes as input a collection of fastqs (single reads). Remove adapters with cutadapt, map with bowtie2. Keep MAPQ30. MACS2 for bam with fixed extension or model.

This workflow takes as input SR BAM from ChIP-seq. It calls peaks on each replicate and intersect them. In parallel, each BAM is subsetted to smallest number of reads. Peaks are called using all subsets combined. Only peaks called using a combination of all subsets which have summits intersecting the intersection of at least x replicates will be kept.

This workflow takes as input PE BAM from ChIP-seq. It calls peaks on each replicate and intersect them. In parallel, each BAM is subsetted to smallest number of reads. Peaks are called using all subsets combined. Only peaks called using a combination of all subsets which have summits intersecting the intersection of at least x replicates will be kept.

This workflow takes as input BAM from ATAC-seq or CUT&RUN. It calls peaks on each replicate and intersect them. In parallel, each BAM is subsetted to smallest number of reads. Peaks are called using all subsets combined. Only peaks called using a combination of all subsets which have summits intersecting the intersection of at least x replicates will be kept.

This workflow takes as input a collection of paired fastq. It uses HiCUP to go from fastq to validPair file using the middle of the fragment as coordinates. The pairs are filtered for MAPQ and sorted by cooler to generate a tabix dataset. Cooler is used to generate a balanced cool file to the desired resolution.

This workflow take as input a collection of paired fastq. It uses HiCUP to go from fastq to validPair file. The pairs are filtered for MAPQ and for the region captured. Then, they are sorted by cooler to generate a tabix dataset. Cooler is used to generate a balanced cool file to the desired resolution.

This workflow uses as input a collection of juicer medium tabix files and a genome name. It builds balanced cool file to the desired resolution.

This workflow takes as input a collection of paired fastq. It uses HiCUP to go from fastq to validPair file. First truncate the fastq using the cutting sequence to guess the fill-in. Then map the truncated fastq. Then asign to fragment and filter the self-ligated and dandling ends or internal (it can also filter for the size). Then it removes the duplicates. Convert the output to be compatible with juicebox or cooler using the middle of the fragment as coordinates. Finally filter for mapping quality

This workflow take as input a collection of paired fastq. Remove adapters with cutadapt, map pairs with bowtie2 allowing dovetail. Keep MAPQ30 and concordant pairs. BAM to BED. MACS2 with "ATAC" parameters.

This workflow takes as input a collection of paired fastqs. Remove adapters with cutadapt, map pairs with bowtie2. Keep MAPQ30 and concordant pairs. MACS2 for paired bam.

We assume the identifiers of the input list are like: sample_name_replicateID. The identifiers of the output list will be: sample_name

Purge contigs marked as duplicates by purge_dups (could be haplotypic duplication or overlap duplication). This workflow is the 6th workflow of the VGP pipeline. It is meant to be run after one of the contigging steps (Workflow 3, 4, or 5)

Scaffolding using HiC data with YAHS.

Create Meryl Database used for the estimation of assembly parameters and quality control with Merqury. Part of the VGP pipeline.

This workflow takes as input a list of paired-end fastqs. Adapters and bad quality bases are removed with cutadapt. Reads are mapped with STAR with ENCODE parameters and genes are counted simultaneously as well as normalized coverage (per million mapped reads) on uniquely mapped reads. The counts are reprocessed to be similar to HTSeq-count output. FPKM are computed with cufflinks and/or with StringTie. The unstranded normalized coverage is computed with bedtools.

This workflow takes as input a list of single-reads fastqs. Adapters and bad quality bases are removed with cutadapt. Reads are mapped with STAR with ENCODE parameters and genes are counted simultaneously as well as normalized coverage (per million mapped reads) on uniquely mapped reads. The counts are reprocessed to be similar to HTSeq-count output. FPKM are computed with cufflinks and/or with StringTie. The unstranded normalized coverage is computed with bedtools.

This workflow takes a collection of BAM (output of STAR) and a gtf. It extends the input gtf using de novo annotation.