SII-GAIR-NLP/RIBOSPAN-10K-15

Many full-length RNAs, particularly mRNAs, exceed the ~1K context lengths used to pretrain representative dense RNA encoders, forcing long transcripts to be truncated and preventing their 5′ UTR, CDS, and 3′ UTR from being modeled jointly at single-nucleotide resolution.

Stack is a large-scale encoder-decoder foundation model for single-cell biology. It introduces a novel tabular attention architecture that enables both intra- and inter-cellular information flow, setting cell-by-gene matrix chunks as the basic input data unit.

Foundation models for genomics and transcriptomics pretrained on 3,000+ human genomes and 850+ diverse species, enabling chromatin accessibility prediction, splice site detection, and promoter classification across multiple model scales (InstaDeep, NVIDIA & TUM, Nature Methods 2023)

General-purpose RNA language model with 650M parameters pretrained on 36M non-coding RNA sequences, achieving strong generalization on structure prediction tasks including secondary structure prediction, splice-site prediction, mean ribosome loading, and ncRNA classification (lbcb-sci, 165+ stars, Apache-2.0)