Workflows

What is a Workflow?
30 Workflows visible to you, out of a total of 30
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

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

Inputs required: assembled-genome.fasta, hard-repeat-masked-genome.fasta, and (because this workflow maps known mRNA ...

Type: Galaxy

Creator: Luke Silver

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.881.5

Genome assembly workflow for nanopore reads, for TSI

Input:

  • Nanopore reads (can be in format: fastq, fastq.gz, fastqsanger, or fastqsanger.gz)

Optional settings to specify when the workflow is run:

  • [1] how many input files to split the original input into (to speed up the workflow). default = 0. example: set to 2000 to split a 60 GB read file into 2000 files of ~ 30 MB.
  • [2] filtering: min average read quality score. default = 10
  • [3] filtering: min read length. default = 200
  • [4] ...

Type: Galaxy

Creator: Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.1114.1

Scaffolding using HiC data with YAHS

This workflow has been created from a Vertebrate Genomes Project (VGP) scaffolding workflow.

Some minor changes have been made to better fit with TSI project data:

  • optional inputs of SAK info ...

Type: Galaxy

Creators: VGP Project, VGP, Galaxy

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.1054.1

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 = soft_masked_genome.fasta, hard_masked_genome.fasta, ...

Type: Galaxy

Creators: Luke Silver, Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.875.3

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

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