Workflows

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555 Workflows visible to you, out of a total of 598
Assembly-decontamination-VGP9/main
No description specified

Type: Galaxy

Creator: Nadolina Brajuka

Submitter: WorkflowHub Bot

Genome-assessment-post-assembly

Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics. Updates November 2023. Inputs: reads as fastqsanger.gz (not fastq.gz), and assembly.fasta. New default settings for BUSCO: lineage = eukaryota; for Quast: lineage = eukaryotes, genome = large. Reports assembly stats into a table called metrics.tsv, including selected metrics from Fasta Stats, and read coverage; reports BUSCO versions and dependencies; and displays these tables in the workflow ...

Type: Galaxy

Creators: Gareth Price, Anna Syme

Submitter: Johan Gustafsson

gene2phylo
Stable

gene2phylo

gene2phylo is a snakemake pipeline for batch phylogenetic analysis of a given set of input genes.

Contents

Setup

The pipeline is written in Snakemake and uses conda to install the necessary tools.

It is strongly recommended to install conda using Mambaforge. See details here ...

Type: Unrecognized workflow type

Creators: None

Submitter: Oliver White

ERGA DataQC Illumina v2309 (WF0)
Stable

The workflow takes a paired-reads collection (like illumina WGS or HiC), runs FastQC and SeqKit, trims with Fastp, and creates a MultiQC report. The main outputs are a paired collection of trimmed reads, a report with raw and trimmed reads stats, and a table with raw reads stats.

Type: Galaxy

Creators: Diego De Panis, ERGA

Submitter: Diego De Panis

DOI: 10.48546/workflowhub.workflow.601.1

skim2rrna
Stable

skim2rrna

skim2rrna is a snakemake pipeline for the batch assembly, annotation, and phylogenetic analysis of ribosomal genes from low coverage genome skims. The pipeline was designed to work with sequence data from museum collections. However, it should also work with genome skims from recently collected samples.

Contents

Type: Snakemake

Creators: None

Submitter: Oliver White

skim2mt
Stable

skim2mt

skim2mt is a snakemake pipeline for the batch assembly, annotation, and phylogenetic analysis of mitochondrial genomes from low coverage genome skims. The pipeline was designed to work with sequence data from museum collections. However, it should also work with genome skims from recently collected samples.

Contents

Type: Snakemake

Creators: None

Submitter: Oliver White

dada2/main

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)

Type: Galaxy

Creators: Matthias Bernt, UFZ Leipzig

Submitter: WorkflowHub Bot

ERGA ONT+Illumina Assembly+QC NextDenovo+HyPo v2403 (WF2)
Work-in-progress

The workflow takes raw ONT reads and trimmed Illumina WGS paired reads collections, the ONT raw stats table (calculated from WF1) and the estimated genome size (calculated from WF1) to run NextDenovo and subsequently polish the assembly with HyPo. It produces collapsed assemblies (unpolished and polished) and runs all the QC analyses (gfastats, BUSCO, and Merqury).

Type: Galaxy

Creators: Diego De Panis, ERGA

Submitter: Diego De Panis

ERGA ONT+Illumina Assembly+QC Flye+HyPo v2403 (WF2)
Stable

The workflow takes raw ONT reads and trimmed Illumina WGS paired reads collections, and the estimated genome size and Max depth (both calculated from WF1) to run Flye and subsequently polish the assembly with HyPo. It produces collapsed assemblies (unpolished and polished) and runs all the QC analyses (gfastats, BUSCO, and Merqury).

Type: Galaxy

Creators: Diego De Panis, ERGA

Submitter: Diego De Panis

score-assemblies
Stable

score-assemblies

A Snakemake-wrapper for evaluating de novo bacterial genome assemblies, e.g. from Oxford Nanopore (ONT) or Illumina sequencing.

The workflow includes the following programs:

...

Type: Snakemake

Creator: Peter Menzel

Submitter: Peter Menzel

DOI: 10.48546/workflowhub.workflow.786.1

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