Prostate cancer classification workflow

This workflow segments tissue regions and classifies prostate cancer on H&E whole slide images, using AI. It consists of three steps:

1. low-resolution tissue segmentation to select areas for further processing;

2. high-resolution tissue segmentation to refine borders - it uses step 1 as input;

3. high-resolution normal/cancer classification - it uses step 1 as input.

Tissue segmentation workflow

This workflow performs tissue segmentation on H&E whole slide images using AI.

This workflow assembles short reads by running the obsolete Velvet assembler. It performs the same assembly on every sample with different k-mer length settings, and reports the longest contig in each case.

Demo de-novo assembly workflow

This workflow assembles short reads by running the obsolete Velvet assembler. It performs the same assembly on every sample with different k-mer length settings, and reports the longest contig in each case.

CUT&RUN data analysis workflow. It uses fastQC, trim adapters with Trim Galore!, map with Bowtie2, filter the BAM and mark duplicates. Peaks are called with MACS2 and peaks from both replicates are intersected and then intersected to the ChIP peaks. Finally MEME ChIP is run to get the motifs.

Associated Tutorial

This workflows is part of the tutorial CUT&RUN data analysis, ...

Reference-based RNA-Seq data analysis

Associated Tutorial

This workflows is part of the tutorial Reference-based RNA-Seq data analysis, available in the GTN

Features

• Includes Galaxy Workflow Tests
• Uses ...

Reference-based RNA-Seq data analysis

Associated Tutorial

This workflows is part of the tutorial Reference-based RNA-Seq data analysis, available in the GTN

Features

• Includes Galaxy Workflow Tests

Thanks to...

Workflow Author(s): Bérénice Batut, ...