MultiNano is a deep learning framework designed for predicting m6A RNA modifications using raw electrical signals from Oxford Nanopore sequencing. It provides high accuracy across species and conditions, offering a user-friendly pipeline for researchers. MultiNano supports both training from scratch and direct prediction modes.

Python toolkit for parsing, processing, and analysis of Illumina methylation array IDAT files

Comprehensive tool for visualizing genome-wide cytosine data.

A methylation array analysis pipeline tailored for discovering rare methylation events with interactive data visualization

adaptable and intrepretable,multi-task learning based gene-level methylation estimations

Re-implementation of J. Beaulaurier et al's SMSN strategy, using the default PacBio tools

Snakemake workflow for the processing of EMseq data

The results of this study were presented as a poster at The International Symposium on Health Informatics and Bioinformatics (HIBIT Bioinformatics)

snakemake pipeline based on nanopolish

Tool to refine promoter regions based on PacBio methylation information

A Snakemake workflow for creating graph visualizations of DNA methylation data.

Data preprocessing and training of a Deep Learning graph architecture for the classification of tumor types, with focus on the most impactful genomic and clinical differences through model explainability. Use cases tested: Lung cancer (LUAD / LUSC) and Kidney cancer (KIRC / KICH / KIRP). Project for the 'AI for Bioinformatics' course.