Workflows

What is a Workflow?
831 Workflows visible to you, out of a total of 904
Taxonomy classification using Kraken2 and Bracken
Work-in-progress

The aim of this workflow is to handle the routine part of shotgun metagenomics data processing. The workflow is using the tools Kraken2 and Bracken for taxonomy classification and the KrakenTools to evaluate diversity metrics. This workflow was tested on Galaxy Australia. A How-to guide for the workflow can be found at: https://github.com/vmurigneu/kraken_howto_ga_workflows/blob/main/pages/taxonomy_kraken2_wf_guide.md

Type: Galaxy

Creators: Valentine Murigneux, Mike Thang

Submitter: Valentine Murigneux

DOI: 10.48546/workflowhub.workflow.1199.2

Meta-analysis of permeability literature data shows possibilities and limitations of popular methods
Work-in-progress

Article abstract

Permeability is an important molecular property in drug discovery, as it co-determines pharmacokinetics whenever a drug crosses the phospholipid bilayer, e.g., into the cell, in the gastrointestinal tract or across the blood-brain barrier. Many methods for the determination of permeability have been developed, including cell line assays, cell-free model systems like PAMPA mimicking, e.g., gastrointestinal epithelia or the skin, as well as the Black lipid membrane (BLM) and ...

Type: KNIME

Creator: Kateřina Storchmannová

Submitter: Kateřina Storchmannová

ERGA-BGE Genome Report ASM analyses (one-asm WGS Illumina PE + HiC)
Stable

Assembly Evaluation for ERGA-BGE Reports

One Assembly, Illumina WGS reads + HiC reads

The workflow requires the following:

  • Species Taxonomy ID number
  • NCBI Genome assembly accession code
  • BUSCO Lineage
  • WGS accurate reads accession code
  • NCBI HiC reads accession code

The workflow will get the data and process it to generate genome profiling (genomescope, smudgeplot -optional-), assembly stats (gfastats), merqury stats (QV, completeness), BUSCO, snailplot, contamination blobplot, and ...

Type: Galaxy

Creators: Diego De Panis, ERGA

Submitter: Diego De Panis

DOI: 10.48546/workflowhub.workflow.1103.2

ERGA-BGE Genome Report ASM analyses (one-asm HiFi + HiC)
Stable

Assembly Evaluation for ERGA-BGE Reports

One Assembly, HiFi WGS reads + HiC reads

The workflow requires the following:

  • Species Taxonomy ID number
  • NCBI Genome assembly accession code
  • BUSCO Lineage
  • WGS accurate reads accession code
  • NCBI HiC reads accession code

The workflow will get the data and process it to generate genome profiling (genomescope, smudgeplot -optional-), assembly stats (gfastats), merqury stats (QV, completeness), BUSCO, snailplot, contamination blobplot, and HiC ...

Type: Galaxy

Creators: Diego De Panis, ERGA

Submitter: Diego De Panis

DOI: 10.48546/workflowhub.workflow.1104.1

Sample Python Crypt4GH workflow
No description specified

Type: Python

Creators: None

Submitter: Alberto Aguado

M6Allele

M6Allele Pipeline & M6Allele algorithm

Introduction

We have developed an algorithm called M6Allele for identifying allele-specific m6A modifications. To facilitate its usage by researchers, we have also encapsulated our analysis process into a pipeline. You can learn more about the pipeline and the algorithm's usage from the following two modules:

M6Allele Pipeline

PARAMETER INTRODUCTION

  • -g/--gtf : ...

Type: Docker

Creators: Yin Zhang, Lin Tang

Submitter: YIN ZHANG

DOI: 10.48546/workflowhub.workflow.1223.1

deepvariant-nextflow
Stable

Nextflow Pipeline for DeepVariant

This repository contains a Nextflow pipeline for Google’s DeepVariant, optimised for execution on NCI Gadi.

Quickstart Guide

  1. Edit the pipeline_params.yml file to include:
  • samples: a list of samples, where each sample includes the sample name, BAM file path (ensure corresponding .bai is in the same directory), path to an optional regions-of-interest BED file (set to '' if not required), and the model type.
  • ref: path to the reference FASTA (ensure ...

Type: Nextflow

Creators: Kisaru Liyanage, Matthew Downton

Submitter: Kisaru Liyanage

Genome-assessment-post-assembly
Stable

Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics, with updates November 2024.

Workflow inputs: reads as fastqsanger.gz (not fastq.gz), and primary assembly.fasta. (To change reads format: click on the pencil icon next to the file in the Galaxy history, then "Datatypes", then set "New type" as fastqsanger.gz). Note: the reads should be those that were used for the assembly (i.e., the filtered/cleaned reads), not the raw reads.

What it does: ...

Type: Galaxy

Creators: Kate Farquharson, Gareth Price, Simon Tang, Anna Syme

Submitters: Johan Gustafsson, Anna Syme

DOI: 10.48546/workflowhub.workflow.403.7

MGnify genomes catalogue pipeline
Stable

MGnify genomes catalogue pipeline

MGnify A pipeline to perform taxonomic and functional annotation and to generate a catalogue from a set of isolate and/or metagenome-assembled genomes (MAGs) using the workflow described in the following publication:

Gurbich TA, Almeida A, Beracochea M, Burdett T, Burgin J, Cochrane G, Raj S, Richardson L, Rogers AB, Sakharova E, Salazar GA and Finn RD. (2023) [MGnify Genomes: A Resource for Biome-specific Microbial Genome ...

Type: Nextflow

Creators: Ekaterina Sakharova, Tatiana Gurbich, Martin Beracochea

Submitter: Martin Beracochea

EnrichDO: a Global Weighted Model for Disease Ontology Enrichment Analysis

EnrichDO

EnrichDO is a double weighted iterative model by integrating the DO graph topology on a global scale. It was based on the latest annotations of the human genome with DO terms, and double weighted the annotated protein-coding genes. On one hand, to reinforce the saliency of direct gene-DO annotations, different initial weights were assigned to directly annotated genes and indirectly annotated genes, respectively. On the other hand, to detect locally most significant node between ...

Type: R markdown

Creator: Liang Cheng

Submitter: Liang Cheng

DOI: 10.48546/workflowhub.workflow.1221.1

Powered by
 (v.1.16.0)
About WorkflowHub | Acknowledgements | Credits | Terms & Conditions | Privacy Policy | Cite us | Contact us
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH