Workflows

What is a Workflow?
70 Workflows visible to you, out of a total of 70

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

About this workflow:

  • Inputs: transdecoder-peptides.fasta, transdecoder-nucleotides.fasta
  • Runs many steps ...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.880.1

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

About this workflow:

  • Input: merged_transcriptomes.fasta.
  • Runs TransDecoder to produce longest_transcripts.fasta ...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.879.1

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

About this workflow:

  • Inputs: multiple transcriptome.gtfs from different tissues, genome.fasta, coding_seqs.fasta, ...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.878.1

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

About this workflow:

  • Run this workflow per tissue.
  • Inputs: masked_genome.fasta and the trimmed RNAseq reads ...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.877.1

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

About this workflow:

  • Repeat this workflow separately for datasets from different tissues.
  • Inputs = collections ...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.876.1

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

Workflow information:

  • Input = genome.fasta.
  • Outputs = masked_genome.fasta and table of repeats found.

...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.875.2

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

  • Repeat masking
  • RNAseq QC and read trimming
  • Find transcripts
  • Combine transcripts
  • Extract transcripts
  • Convert formats
  • Fgenesh annotation

Type: Galaxy

Creator: Luke Silver

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.881.1

Parabricks-Genomics-nf is a GPU-enabled pipeline for alignment and germline short variant calling for short read sequencing data. The pipeline utilises NVIDIA's Clara Parabricks toolkit to dramatically speed up the execution of best practice bioinformatics tools. Currently, this pipeline is configured specifically for NCI's Gadi HPC.

NVIDIA's Clara Parabricks can deliver a significant ...

Type: Nextflow

Creator: Georgina Samaha

Submitter: Georgina Samaha

DOI: 10.48546/workflowhub.workflow.836.1

Stable

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, Gareth Price, Anna Syme

Submitters: Johan Gustafsson, Anna Syme

DOI: 10.48546/workflowhub.workflow.403.4

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

Powered by
(v.1.14.1)
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH