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:

• Quality plots (FastQC)
• NG-TAX 2 High-throughput Amplicon Analysis
• PICRUSt 2 - Function prediction from marker gene sequences
• Export module for ngtax

Workflow for quality assessment and taxonomic classification of amplicon long read sequences. In addition files are exported to their respective subfolders for easier data management in a later stage.

Inputs are expected to be basecalled fastq files

Steps:

• NanoPlot read quality control, before and after filtering
• fastplong read quality and length filtering
• Emu abundance; species-level taxonomic abundance for full-length 16S read

Workflow (hybrid) metagenomic assembly and binning

• Workflow Illumina Quality:
• Sequali (control)
• hostile contamination filter
• fastp (quality trimming)
• Workflow Longread Quality:
• NanoPlot (control)
• fastplong (quality trimming)
• hostile contamination filter
• Kraken2 taxonomic classification of FASTQ reads
• SPAdes/Flye (Assembly)
• Medaka/PyPolCA (Assembly polishing)
• QUAST (Assembly quality report)

(optional)

• Workflow binnning
• Metabat2/MaxBin2/SemiBin
• Binette
• BUSCO

...

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 ...

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

...

🧬 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 ...

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 ...

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 ...

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. ...

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 ...