VAMB (Nature Biotechnology 2021)
github.com/rasmussenlab/vambDeep learning tool for metagenomic binning using a variational autoencoder over co-abundance and tetranucleotide k-mer composition to reconstruct non-redundant microbial genome bins (MAGs) directly from metagenomes without reference databases, generating higher-quality bins than composition- or abundance-only methods at scale; underpins large-scale microbiome catalogs including the human gut Unified Human Gastrointestinal Genome collection (UHGG) and remains actively maintained (Rasmussen Lab, Technical University of Denmark, 335+ stars, MIT License)
Sourced from
- Awesome AI for Science — github.com/rasmussenlab/vamb
- GitHub — github.com/rasmussenlab/vamb
Related resources
Gene expression prediction
Arc Institute's 40B-parameter genome foundation model trained on 9 trillion nucleotides from all domains of life, supporting 1M base pair context for generalist DNA/RNA/protein prediction and design (Nature 2026)
Deep learning-based variant caller
Google DeepMind's unified DNA sequence foundation model predicting molecular consequences of genetic variants from single-base resolution up to 1 megabase context, jointly outputting thousands of regulatory tracks (RNA expression, splicing, chromatin accessibility, TF binding, contact maps) for human and mouse genomes via a Python client and non-commercial API (2025)
Deep probabilistic framework for single-cell and spatial omics analysis, integrating scVI, scANVI, totalVI and other VAE-based models for batch correction, cell annotation, multi-omics integration, and RNA velocity (scverse/NumFOCUS, Nature Methods 2018/2024)
Single-cell analysis with transformers