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747 Workflows visible to you, out of a total of 808
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

Assembly-Hifi-only-VGP3/main

Contiging Solo:

Generate assembly based on PacBio Hifi Reads.

Inputs

  1. Hifi long reads [fastq]
  2. K-mer database [meryldb]
  3. Genome profile summary generated by Genomescope [txt]
  4. Homozygous Read Coverage. Optional, use if you think the estimation from Genomescope is inacurate.
  5. Genomescope Model Parameters generated by Genomescope [tabular]
  6. Database for busco lineage (recommended: latest)
  7. Busco lineage (recommended: vertebrata)
  8. Name of first assembly
  9. Name of second ...

Type: Galaxy

Creator: Galaxy, VGP

Submitter: WorkflowHub Bot

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

clinicalmp-data-interpretation/main

This workflow will perform taxonomic and functional annotations using Unipept and statistical analysis using MSstatsTMT.

Type: Galaxy

Creator: GalaxyP

Submitter: WorkflowHub Bot

clinicalmp-verification/main

In proteomics research, verifying detected peptides is essential for ensuring data accuracy and biological relevance. This tutorial continues from the clinical metaproteomics discovery workflow, focusing on verifying identified microbial peptides using the PepQuery tool.

Type: Galaxy

Creator: Pratik Jagtap

Submitter: WorkflowHub Bot

nf-core/metapep

From metagenomes to peptides

Type: Nextflow

Creators: Sabrina Krakau, Leon Kuchenbecker and Till Englert

Submitter: WorkflowHub Bot

clinicalmp-database-generation/main

The workflow begins with the Database Generation process. The Galaxy-P team has developed a workflow that collects protein sequences from known disease-causing microorganisms to build a comprehensive database. This extensive database is then refined into a smaller, more relevant dataset using the Metanovo tool.

Type: Galaxy

Creator: Subina Mehta

Submitter: WorkflowHub Bot

Test at SC24
No description specified

Type: COMPSs

Creator: Daniele Lezzi

Submitter: Daniele Lezzi

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