Workflows

What is a Workflow?
736 Workflows visible to you, out of a total of 791

Kmer counting step, can run alone or as part of a combined workflow for large genome assembly.

  • What it does: Estimates genome size and heterozygosity based on counts of kmers
  • Inputs: One set of short reads: e.g. R1.fq.gz
  • Outputs: GenomeScope graphs
  • Tools used: Meryl, GenomeScope
  • Input parameters: None required
  • Workflow steps: The tool meryl counts kmers in the input reads (k=21), then converts this into a histogram. GenomeScope: runs a model on the histogram; reports estimates. k-mer ...

Type: Galaxy

Creator: Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.223.1

Data QC step, can run alone or as part of a combined workflow for large genome assembly.

  • What it does: Reports statistics from sequencing reads.
  • Inputs: long reads (fastq.gz format), short reads (R1 and R2) (fastq.gz format).
  • Outputs: For long reads: a nanoplot report (the HTML report summarizes all the information). For short reads: a MultiQC report.
  • Tools used: Nanoplot, FastQC, MultiQC.
  • Input parameters: None required.
  • Workflow steps: Long reads are analysed by Nanoplot; Short reads ...

Type: Galaxy

Creator: Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.222.1

Assembly polishing subworkflow: Racon polishing with short reads

Inputs: short reads and assembly (usually pre-polished with other tools first, e.g. Racon + long reads; Medaka)

Workflow steps:

  • minimap2: short reads (R1 only) are mapped to the assembly => overlaps.paf. Minimap2 setting is for short reads.
  • overlaps + short reads + assembly => Racon => polished assembly 1
  • using polished assembly 1 as input; repeat minimap2 + racon => polished assembly 2
  • Racon short-read polished ...

Type: Galaxy

Creator: Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.228.1

Assembly polishing; can run alone or as part of a combined workflow for large genome assembly.

  • What it does: Polishes (corrects) an assembly, using long reads (with the tools Racon and Medaka) and short reads (with the tool Racon). (Note: medaka is only for nanopore reads, not PacBio reads).
  • Inputs: assembly to be polished: assembly.fasta; long reads - the same set used in the assembly (e.g. may be raw or filtered) fastq.gz format; short reads, R1 only, in fastq.gz format
  • Outputs: ...

Type: Galaxy

Creator: Anna Syme

Submitter: Anna Syme

DOI: 10.48546/workflowhub.workflow.226.1

This notebook is about pre-processing the Auditory Brainstem Response (ABR) raw data files provided by Ingham et. al to create a data set for Deep Learning models.

The unprocessed ABR data files are available at Dryad.

Since the ABR raw data are available as zip-archives, these have to be unzipped and the extracted raw data files parsed so that the time ...

Type: Jupyter

Creator: Elida Schneltzer

Submitter: Elida Schneltzer

Workflow for quality assessment of paired reads and classification using NGTax 2.0 and functional annotation using picrust2. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps:

  • FastQC (read quality control)
  • NGTax 2.0
  • Picrust 2
  • Export module for ngtax

Type: Common Workflow Language

Creators: Bart Nijsse, Jasper Koehorst

Submitter: Jasper Koehorst

DOI: 10.48546/workflowhub.workflow.154.2

Work-in-progress

This is an experimental KNIME workflow of using the BioExcel building blocks to implement the Protein MD Setup tutorial for molecular dynamics with GROMACS.

Note that this workflow won't import in KNIME without the experimental KNIME nodes for BioBB - contact Adam Hospital for details.

Stable

This PyCOMPSs workflow tutorial aims to illustrate the process of setting up a simulation system containing a protein, step by step, using the BioExcel Building Blocks library (biobb) in PyCOMPSs for execution on HPC. Three variants of the MD Setup workflows are included, supporting a list of structures, a list of mutations, or a cumulative set of mutations.

Stable

Analysis of RNA-seq data starting from BAM and focusing on mRNA, lncRNA and miRNA

Type: Galaxy

Creators: None

Submitter: Bianca Pasat

Stable

This workflow is based on the idea of comparing different gene sets through their semantic interpretation. In many cases, the user studies a specific phenotype (e.g. disease) by analyzing lists of genes resulting from different samples or patients. Their pathway analysis could result in different semantic networks, revealing mechanistic and phenotypic divergence between these gene sets. The workflow of BioTranslator Comparative Analysis compares quantitatively the outputs of pathway analysis, ...

Type: Galaxy

Creators: None

Submitter: thodk

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