# BERT-based RNA language model

RNA homology search has long traded sensitivity against speed: sequence methods are fast but miss low-identity homologs, while covariance models are sensitive but costly. RNAflash splits the task in two, first retrieving candidates from a pre-encoded database with a contrastively trained RNA language-model encoder, then reranking and aligning them with a cross-encoder guided by predicted secondary structure. On held-out Rfam families its retrieval recall exceeds sequence-based methods; on RNAcentral the resulting alignments show stronger coupling and covariation signals than sequence-based methods and Infernal, while search and alignment run 3.7-fold faster than non-iterative Riboseek and over 100-fold faster than Infernal and RNAcmap.

RNA homology search has long traded sensitivity against speed: sequence methods are fast but miss low-identity homologs, while covariance models are sensitive but costly. RNAflash splits the task in two, first retrieving candidates from a pre-encoded database with a contrastively trained RNA language-model encoder, then reranking and aligning them with a cross-encoder guided by predicted secondary structure. On held-out Rfam families its retrieval recall exceeds sequence-based methods; on RNAcentral the resulting alignments show stronger coupling and covariation signals than sequence-based methods and Infernal, while search and alignment run 3.7-fold faster than non-iterative Riboseek and over 100-fold faster than Infernal and RNAcmap.