Scrambling in the Tree of Life
Self-supervised deep learning for comparative genomic rearrangement analysis
OIST Genomics & Regulatory Systems Unit — Feb 2026 – Present Supervised by Prof. Nicholas Luscombe and Dr. Charles Plessy
As genomes diverge over evolutionary time, they undergo large-scale structural rearrangements — translocations, inversions, duplications, fissions, and fusions. This project asks whether a self-supervised deep learning model can learn a compact, biologically meaningful latent geometry of these rearrangements directly from raw pairwise alignment structure, without labelled topology annotations.
Approach
Pairwise whole-genome alignments are encoded as point clouds — unordered sets of aligned blocks, each featurised by genomic coordinates, strand orientation, and alignment quality. A Set Transformer models pairwise interactions between blocks via self-attention, and a Pooling by Multihead Attention (PMA) module aggregates the variable-size set into a fixed-dimension latent vector. The model is trained with the VICReg self-supervised objective, enforcing invariance between views, variance regularization to prevent representational collapse, and covariance penalties to decorrelate latent dimensions.
- Set Transformer + PMA pooling on 5D genomic point clouds
- VICReg self-supervised loss
- PyTorch DDP across 4x A100 GPUs on OIST's Saion HPC cluster
- BF16 mixed precision, Flash Attention, torch.compile
Status
Currently validating whether learned clusters reflect genomic topology versus domain identity, and preparing a submission to a NeurIPS 2026 workshop on machine learning for genomics.