Aksu, E. D. | Search bioRxiv

bioRxiv2025

Context-aware sequence-to-activity model of human gene regulation

Sequence-to-function models have been very successful in predicting gene expression, chromatin accessibility, and epigenetic marks from DNA sequences alone. However, current state-of-the-art models have a fundamental limitation: they cannot extrapolate beyond the cell types and conditions included in their training dataset. Here, we introduce a new approach that is designed to overcome this limitation: Corgi, a new context-aware sequence-to-function model that accurately predicts genome-wide gene expression and epigenetic signals, even in previously unseen cell types. We designed an architecture that strives to emulate the cell: Corgi integrates DNA sequence and trans-regulator expression to predict the coverage of multiple assays including chromatin accessibility, histone modifications, and gene expression. We define trans-regulators as transcription factors, histone modifiers, transcriptional coactivators, and RNA binding proteins, which directly modulate chromatin states, gene expression, and mRNA decay. Trained on a diverse set of bulk and single cell human datasets, Corgi has robust predictive performance, approaching experimental-level accuracy in gene expression predictions in previously unseen cell types, while also setting a new state-of-the-art level for joint cross-sequence and cross-cell type epigenetic track prediction. Corgi can be used in practice to impute context-specific assays such as DNA accessibility and histone ChIP-seq, using only RNA-seq data.

genomics↗

bioRxiv2024

Transfer learning and DNA language models enhance transcription factor binding predictions

Identification of in vivo transcription factor (TF) binding sites is crucial to understand gene regulatory networks, but the lack of scalability in the methods for their experimental identification directs researchers towards computational models. TF binding site prediction models are often specific for a given TF, which also hinders the generalizability of models to previously unseen TFs. Here, we present an approach to predict in vivo TF binding sites using DNA accessibility, TF RNA expression and TF binding motifs. Our novel method leverages DNA language model embeddings and transfer learning to improve its accuracy and generalizability, achieving a mean area under the precision-recall curve (AUPR) of 0.51 in held-out cell types and chromosomes in the ENCODE-DREAM in vivo TFBS prediction challenge, outperforming the top-ranked methods. Furthermore, we show that prediction accuracy increases when TFs are highly active and exhibit cell-type specific expression. We finally test our models in an independent dataset on previously unseen TFs, and report a mean AUPR of 0.36, which is state-of-the-art in a cross-TF, cross-cell type and cross-chromosomal setting.

bioinformatics↗