Workflows

What is a Workflow?
355 Workflows visible to you, out of a total of 386
Stable

Description

The Settlement Delineation and Analysis (SDA) workflows generates a settlement network from geospatial settlement data. It can process geotiff and shapefile inputs and was originally designed to operate on the World Settlement Footprint dataset. Through multiple workflow stages, a settlement network is constructed, contracted (i.e. clustered) and ultimately analysed with centrality measures. The output shapefile stores the ...

Type: Docker

Creator: Lorenz Gruber

Submitter: Lorenz Gruber

DOI: 10.48546/workflowhub.workflow.1308.2

Stable

cfDNA-Flow

1. Overview

cfDNA-Flow facilitates the accurate and reproducible analysis of cfDNA WGS data. It offers various preprocessing options to accommodate different experimental setups and research needs in the field of liquid biopsies.

2. Preprocessing options

2.1 Trimming Options

cfDNA-Flow provides the flexibility to either trim or not trim the input reads based on the user's requirements. Trimming removes low-quality bases, which can impact downstream analyses.

2.2 Reference

...

Type: Snakemake

Creators: Ivna Ivankovic, Todor Gitchev, Zsolt Balázs

Submitter: Zsolt Balázs

Stable

Workflow (hybrid) metagenomic assembly and binning

  • Workflow Illumina Quality: https://workflowhub.eu/workflows/336?version=1
  • FastQC (control)
  • fastp (quality trimming)
  • kraken2 (taxonomy)
  • bbmap contamination filter
  • Workflow Longread Quality:
  • NanoPlot (control)
  • filtlong (quality trimming)
  • kraken2 (taxonomy)
  • minimap2 contamination filter
  • Kraken2 taxonomic classification of FASTQ reads
  • SPAdes/Flye (Assembly)
  • Pilon/Medaka/PyPolCA (Assembly polishing)
  • QUAST (Assembly ...

Type: Common Workflow Language

Creators: Bart Nijsse, Jasper Koehorst, Changlin Ke

Submitter: Bart Nijsse

DOI: 10.48546/workflowhub.workflow.367.2

Work-in-progress

🧬 Click-qPCR 🧬

An ultra-simple tool for interactive qPCR data analysis developed with R and Shiny.

日本語版のユーザーガイドはこちら (Read this document in Japanese)

Overview

Click-qPCR is a user-friendly Shiny web application designed for the straightforward analysis of real-time quantitative PCR (qPCR) data.

This tool is readily accessible via a web browser at , requiring no local installation for end-users.

It allows users to upload their Cq (quantification cycle) values, perform ΔCq ...

Type: Unrecognized workflow type

Creators: Azusa Kubota, Atsushi Tajima

Submitter: Azusa Kubota

EC-Earth3 workflow with wrappers running in MeluXina with Autosubmit v3.15.14, used to assess the effects of task aggregation on queueing times. Workflow configuration is based on the Auto-EC-Earth3's testing suite [1].

In order to reduce the size of the workflow, the /tmp directory has been deleted. Additionally, the experiment has been cleaned up with the Autosubmit clean command. The ...

Stable

The Spatial Transcriptomics analysis workflow for Xenium data from the PATH2XNAT project tested on the non-diseased lung dataset from 10X genomics. The analysis workflow written in R and executed in the interactive RStudio environment consists of visualizations, clustering, feature selection and cluster annotation.

Training materials elaborating on this analysis workflows can be found in this GitHub repository: https://github.com/HCGB-IGTP/PATH2XNAT/tree/main.

This workflow was developed in the ...

Type: Galaxy

Creators: None

Submitter: Myrthe van Baardwijk

Stable

Colorectal-cancer-detection-using-ColoPola-dataset

Methods

We trained and tested three models from scratch (CNN, CNN_2 and EfficientFormerV2) and two pretrained models (DenseNet121 and EfficientNetV2-m) to classify the colorectal cancer using the ColoPola dataset.

ColoPola dataset

The dataset consists of 572 slices (specimens) with 20,592 images. There are 284 cancer samples and 288 normal samples. This dataset can download from Zenodo repository. ...

Type: Python

Creators: Thi-Thu-Hien Pham, Thao-Vi Nguyen, The-Hiep Nguyen, Quoc-Hung Phan, Thanh-Hai Le

Submitter: Thanh-Hai Le

DOI: 10.48546/workflowhub.workflow.1797.2

PanGIA: A universal framework for identifying association between ncRNAs and diseases

PanGIA is a deep learning model for predicting ncRNA-disease associations.

Model Architecture

Installation

conda create -n pangia python=3.11 
conda activate pangia 
pip install -r requirements.txt 

Prepare Datasets

The raw data can be downloaded from the following sources:

  • miRNA: The associations between miRNAs and diseases were obtained from the HMDD v4.0 ...

Type: Python

Creators: None

Submitter: qiankunzizairen Liu

GENome EXogenous (GENEX) sequence detection

This is a computational workflow for detecting coordinates of microbial-like or human-like sequences in eukaryotic and procaryotic reference genomes. The workflow accepts a reference genome in FASTA-format and outputs coordinates of microbial-like (human-like) regions in BED-format. The workflow builds a Bowtie2 index of the reference genome and aligns pre-computed microbial (GTDB v.214 or NCBI RefSeq release 213) or human (hg38) pseudo-reads to the ...

Overview

Developmental version of MSC: This github page contains developmental version of R package for Multi-scale clustering (MSC) to perform single-cell transcriptome clustering. The manuscript is currently under review.

Installation:

MEGENA needs to be installed, prior to MSC installation: library(devtools); install_github("songw01/MEGENA");

For installation for developmental github version: library(devtools); install_github("songlabcodes/MSC");

Vignettes [PBMC 8k ...

Type: R markdown

Creator: Won-Min Song

Submitter: Won-Min Song

DOI: 10.48546/workflowhub.workflow.1875.1

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