kjaganatha.bsky.social
@kjaganatha.bsky.social
Acknowledging that all benchmarks and related comments are based on expression effects of promoter variants. Should’ve clarified that upfront — my apologies!
May 29, 2025 at 9:51 PM
Huge thanks to Illumina, Kyle Farh, Nicole Ersaro, Gherman Novakovsky, and the entire team behind PromoterAI for making this happen. Full paper: www.science.org/doi/10.1126/...
@science.org (10/)
Predicting expression-altering promoter mutations with deep learning
Only a minority of patients with rare genetic diseases are currently diagnosed by exome sequencing, suggesting that additional unrecognized pathogenic variants may reside in non-coding sequence. Here,...
www.science.org
May 29, 2025 at 6:29 PM
The PromoterAI source code is available at github.com/Illumina/Pro.... Precomputed scores for all promoter SNVs are freely available for academic and non-commercial research. (9/)
GitHub - Illumina/PromoterAI
Contribute to Illumina/PromoterAI development by creating an account on GitHub.
github.com
May 29, 2025 at 6:29 PM
In the @genomicsengland.bsky.social cohort, variants prioritized by PromoterAI are enriched in clinically relevant genes. These variants account for 6% of the genetic burden and, when combined with SpliceAI and PrimateAI-3D, match the genetic burden of protein-truncating variants. (8/)
May 29, 2025 at 6:29 PM
PromoterAI effectively identifies pathogenic ClinVar variants that disrupt diverse regulatory motifs — often conserved and supported by ChIP-seq evidence. (7/)
May 29, 2025 at 6:29 PM
In the @ukbiobank.bsky.social cohort, PromoterAI predictions correlate strongly with protein levels and quantitative traits, suggesting that promoter variants contribute meaningfully to phenotypic variation. (6/)
May 29, 2025 at 6:29 PM
PromoterAI’s internal representations reveal three promoter categories: ubiquitously active, bivalent chromatin, and enhancer-like. The enhancer-like category, enriched for TATA boxes, may represent enhancers co-opted as promoters. (5/)
May 29, 2025 at 6:29 PM
PromoterAI achieves the best performance across diverse benchmarks spanning RNA, proteins, QTLs, and MPRA. Basenji2 < Enformer < Borzoi @drkbio.bsky.social. ChromBPNet @anshulkundaje.bsky.social (trained on accessibility) does well in MPRA but not in tissues. Evo2 lags far behind. (4/)
May 29, 2025 at 6:29 PM
Fine-tuning was done using twin networks that contrasted observed expression between ref and alt alleles, enabling the model to attribute differences to the variant rather than unrelated confounders. This was key to generalizing across unseen genes and datasets. (3/)
May 29, 2025 at 6:29 PM
We first trained PromoterAI to predict epigenetic and expression profiles at nucleotide resolution, then fine-tuned it on rare promoter variants associated with aberrant expression. (2/)
May 29, 2025 at 6:29 PM