Workflows

What is a Workflow?
829 Workflows visible to you, out of a total of 902
CroMaSt: A workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment
Stable

CroMaSt: A workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment

CroMaSt (Cross Mapper of domain Structural instances) is an automated iterative workflow to clarify the assignment of protein domains to a given domain type of interest, based on their 3D structure and by cross-mapping of domain structural instances between domain databases. CroMaSt (for Cross-Mapper of domain Structural instances) ...

Type: Common Workflow Language

Creators: Hrishikesh Dhondge, Isaure Chauvot de Beauchêne, Marie-Dominique Devignes

Submitter: Hrishikesh Dhondge

DOI: 10.48546/workflowhub.workflow.390.2

Nucleoli segmentation using CellProfiler (EOSC-Life D6)
Stable

This workflow has been created as part of Demonstrator 6 of the project EOSC-Life (within WP3) and is focused on reusing publicly available RNAi screens to gain insights into the nucleolus biology. The workflow downloads images from the Image Data Resource (IDR), performs object segmentation (of nuclei and nucleoli) and feature extraction of the images and objects identified.

Tutorial: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/tutorial-CP/tutorial.html

Type: Galaxy

Creators: None

Submitter: Beatriz Serrano-Solano

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Gene similariy anaylsis across physiological systems in IMPC phenotype data

Gene similariy anaylsis across physiological systems in IMPC phenotype data

A Jupyter Notebook tool for analysing user specified genes across the different physiological systems in IMPC data.

Input

The tool takes as input a list of gene ids (MGI ids or Gene Symbol ids). The elemnts in the list could be separated by a comma, semicolumn, tab or newline.

Operation

The program will create an heatmap representing the number of phenotypes and the mp term list for each gene contained in ...

Type: Jupyter

Creators: Andrea Furlani, Philipp Gormanns

Submitter: Andrea Furlani

Performance evaluation of Katdetectr and other kataegis detection packages
Stable

Introduction

This repository contains all the custom scripts used in the evaluation and comparison of Katdetectr as described in the corresponding Technical Note (under submission).

Usage

All required files were deposited on Zenodo. These can directly be downloaded using zen4R and be used as input.

# Increase the timeout (due to some large files). 
options(timeout=5000)
...

Type: R markdown

Creators: Daan Hazelaar, Job van Riet

Submitter: Daan Hazelaar

DOI: 10.48546/workflowhub.workflow.500.1

Genome-wide alternative splicing analysis v.2
Stable

Genome-wide alternative splicing analysis v.2

Type: Galaxy

Creator: Cristóbal Gallardo

Submitter: Cristóbal Gallardo

DOI: 10.48546/workflowhub.workflow.482.3

Load ome.zarr Image with labels from a public S3 repository, analyze using StarDist and compare results

The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the Image Data Resource.

This original image was converted into the Zarr format. The analysis results produced by the authors of the paper were converted into labels and linked to the Zarr file which was placed into a public ...

Type: Unrecognized workflow type

Creators: Jean-Marie Burel, Petr Walczysko

Submitter: Jean-Marie Burel

DOI: 10.48546/workflowhub.workflow.496.1

Load ome.zarr Image with labels from public S3 repositories, analyze in parallel using Cellpose and compare results
Stable

Learning objectives

  • Read data to analyse from an object store.
  • Analyse data in parallel using Dask.
  • Show how to use public resources to train neural network.
  • Load labels associated to the original data
  • Compare results with ground truth.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image ...

Type: Unrecognized workflow type

Creators: Jean-Marie Burel, Petr Walczysko

Submitter: Jean-Marie Burel

DOI: 10.48546/workflowhub.workflow.495.1

Validate a tool against IDR data: Load Image with labels from IDR, re-analyze using Cellpose
Stable

IDR is based on OMERO and thus all what we show in this notebook can be easily adjusted for use against another OMERO server, e.g. your institutional OMERO server instance.

The main objective of this notebook is to demonstrate how public resources such as the IDR can be used to train your neural network or validate software tools.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August ...

Type: Jupyter

Creators: Jean-Marie Burel, Petr Walczysko

Submitter: Jean-Marie Burel

DOI: 10.48546/workflowhub.workflow.489.1

Use Public Resources to answer a biological question
Stable

Learning Objectives

  • How to access genomic resource via its Python API
  • How to access image resource via its Python API
  • Relate image data to genomic data

Diabetes related genes expressed in pancreas

This notebook looks at the question Which diabetes related genes are expressed in the pancreas? Tissue and disease can be modified.

Steps:

  • Query humanmine.org, an integrated database of Homo sapiens genomic data using the intermine API to find the ...

Type: Jupyter

Creators: Jean-Marie Burel, Petr Walczysko

Submitter: Jean-Marie Burel

DOI: 10.48546/workflowhub.workflow.494.1

Validate a tool against IDR data: Load Image with labels from IDR, re-analyze using StarDist
Stable

The notebook shows how to load an IDR image with labels.

The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the Image Data Resource.

In this notebook, the image is loaded together with the labels and analyzed using StarDist. The StarDist analysis produces a segmentation, which is then viewed side-by-side with the original segmentations ...

Type: Unrecognized workflow type

Creators: Jean-Marie Burel, Petr Walczysko

Submitter: Jean-Marie Burel

DOI: 10.48546/workflowhub.workflow.493.1

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