Workflows
What is a Workflow?Filters
Short paired-end read analysis to provide quality analysis, read cleaning and taxonomy assignation directly from raw reads
Type: Galaxy
Creators: ABRomics , Pierre Marin, Clea Siguret, abromics-consortium
Submitter: WorkflowHub Bot
This workflow processes the CMO fastqs with CITE-seq-Count and include the translation step required for cellPlex processing. In parallel it processes the Gene Expresion fastqs with STARsolo, filter cells with DropletUtils and reformat all outputs to be easily used by the function 'Read10X' from Seurat.
Type: Galaxy
Creators: Lucille Delisle, Mehmet Tekman, Hans-Rudolf Hotz, Daniel Blankenberg, Wendi Bacon
Submitter: WorkflowHub Bot
Complete ChIP-seq analysis for single-end sequencing data. Processes raw FASTQ files through adapter removal (cutadapt), alignment to reference genome (Bowtie2), and quality filtering (MAPQ >= 30). Peak calling with MACS2 uses either a fixed extension parameter or built-in model to identify protein-DNA binding sites. Generates alignment files, peak calls, and quality metrics for downstream analysis.
Complete ChIP-seq analysis for paired-end sequencing data. Processes raw FASTQ files through adapter removal (cutadapt), alignment to reference genome (Bowtie2), and stringent quality filtering (MAPQ >= 30, concordant pairs only). Peak calling with MACS2 optimized for paired-end reads identifies protein-DNA binding sites. Generates alignment files, peak calls, and quality metrics for downstream analysis.
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
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 ...
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 ...
Find and annotate variants in ampliconic SARS-CoV-2 Illumina sequencing data and classify samples with pangolin and nextclade
COVID-19: variation analysis on WGS PE data
This workflows performs paired end read mapping with bwa-mem followed by sensitive variant calling across a wide range of AFs with lofreq and variant annotation with snpEff 4.5covid19.
Variant calling and consensus sequence generation for batches of Illumina PE sequenced viruses with uncomplicated and stable genome structure (like e.g. Morbilliviruses).
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