kmer counting - meryl - upgraded 2.0
Visit source
Download RO-Crate
Run on GalaxyAssociated Tutorial
This workflows is part of the tutorial Large genome assembly and polishing, available in the GTN
Features
- Includes a Galaxy Workflow Report
Thanks to...
Workflow Author(s): Anna Syme
Tutorial Author(s): Anna Syme
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| Illumina reads R1 | #main/Illumina reads R1 | n/a |
|
Steps
| ID | Name | Description |
|---|---|---|
| 1 | Meryl - count kmers | toolshed.g2.bx.psu.edu/repos/iuc/meryl/meryl/1.3+galaxy6 |
| 2 | Meryl - generate histogram | toolshed.g2.bx.psu.edu/repos/iuc/meryl/meryl/1.3+galaxy6 |
| 3 | Genomescope | toolshed.g2.bx.psu.edu/repos/iuc/genomescope/genomescope/2.0+galaxy2 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| GenomeScope on input dataset(s) Linear plot | #main/GenomeScope on input dataset(s) Linear plot | n/a |
|
| GenomeScope on input dataset(s) Log plot | #main/GenomeScope on input dataset(s) Log plot | n/a |
|
| GenomeScope on input dataset(s) Transformed linear plot | #main/GenomeScope on input dataset(s) Transformed linear plot | n/a |
|
| GenomeScope on input dataset(s) Transformed log plot | #main/GenomeScope on input dataset(s) Transformed log plot | n/a |
|
| Meryl on input dataset(s): read-db.meryldb | #main/Meryl on input dataset(s): read-db.meryldb | n/a |
|
| _anonymous_output_1 | #main/_anonymous_output_1 | n/a |
|
| _anonymous_output_2 | #main/_anonymous_output_2 | n/a |
|
| _anonymous_output_3 | #main/_anonymous_output_3 | n/a |
|
| _anonymous_output_4 | #main/_anonymous_output_4 | n/a |
|
Version History
master
Creators and Submitter
Views: 1376 Downloads: 175 Runs: 0
Created: 2nd Jun 2025 at 11:00
AttributionsNone
Related items
Teams: Galaxy Training Network
Organizations: Galaxy
I'm a bot managed by @hexylena to upload workflows from the Galaxy Training Network to the WorkflowHub. If you have any issues with my behaviour please let her know by filing an issue.
Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational biological research.
- Accessible: Users can easily run tools without writing code or using the CLI; all via a user-friendly web interface.
- Reproducible: Galaxy captures all the metadata from an analysis, making it completely reproducible.
- Transparent: Users share and publish analyses via interactive pages that can enhance analyses with user annotations.
- Scalable: Galaxy can run ...
Teams: Galaxy Training Network, usegalaxy-eu, Intergalactic Workflow Commission (IWC)
Web page: https://galaxyproject.org/
The Galaxy Training Network (GTN) is a collection of hands-on tutorials that are designed to be interactive and are built around Galaxy.
These tutorials can be used for learning and teaching how to use Galaxy for general data analysis, as well as a wide array of hands-on tutorials covering specific domains such as assembly, RNA-Seq analysis, deep learning, climate analysis, and more!
