An international group of scientists and data scientists developing deep learning models that predict where flipons — DNA sequences capable of switching to alternative, non-B conformations — are located, and under which cellular conditions they form.
Browse models How flipons are predictedHSE University, Russia · InsideOutBio, USA. Materials are published for information purposes.
Flipons are genomic elements that adopt left-handed Z-DNA, G-quadruplex, triplex (H-DNA) and cruciform conformations under torsional stress, acting as conformational switches in transcription, replication, repair and immune signalling. Their functional impact is condition-dependent: the same sequence may remain B-form in one cell type and flip in another, which makes experimental mapping expensive and inherently incomplete.
flipon.ai closes this gap computationally. We build sequence-based and multimodal deep learning models trained on high-throughput experimental maps (Z-DNA ChIP-seq, G4-seq and BG4 CUT&Tag, permanganate and S1-nuclease footprints), and we release them together with the genome-wide predictions they generate.
Sequence-based and multimodal models for Z-DNA, G-quadruplexes, H-DNA and cruciforms. Each comes with the publication behind it, the trained weights, and the genome-wide predictions it produced.
A curated bibliography of the group’s work on non-B DNA prediction, interpretable deep learning for genomics, and the biological consequences of flipon variation.
Everything on this site — code, trained weights, whole-genome tracks, and the publications behind them — is intended to be used, checked and extended by the community.
Z-DNA, G-quadruplexes, H-DNA and cruciforms — the conformational switches that act in transcription, replication, repair and immune signalling.
The same sequence stays B-form in one cell type and flips in another, so predictions are conditioned on open chromatin rather than given as a single static annotation.
Models ship with their weights and with the genome-wide tracks they produced, versioned against the release, so a published analysis can be reproduced against the exact predictions it used.
We welcome contributions, benchmark submissions and requests for cell types to be added to the AlphaFlipon release.