Biology subjects

Cole, C. B.

Publications and source records attributed to Cole, C. B..

5 recordsLinked to original sources

bioRxiv2025

Enigma: An Efficient Model for Deciphering Regulatory Genomics

Genomic sequence-to-function models have emerged as powerful tools for deciphering cis-regulatory grammar to advance our understanding of disease biology and guide therapeutic development. Recent advances have been driven by multi-task training of large transformer-based models on thousands of genome tracks. However, these performance gains have come at significant computational cost for both training and inference, hindering large-scale applications and slowing future model development. Here, rather than continuing to scale model size and add more training tracks, we focus on architectural efficiency and train on a substantially smaller, curated set of genome tracks. Our model Enigma achieves competitive performance with current state-of-the-art models at single-base resolution while substantially reducing computational cost. On zero-shot variant effect prediction benchmarks, Enigma outperforms the leading open-source model, the Borzoi ensemble, while using 10.9% of its compute and improving resolution from 32 bases to a single base. Compared to AlphaGenome, Enigma achieves 90.4 - 97.3% of its performance using 7.5% of its estimated compute. These improvements in efficiency can facilitate further development of models for regulatory genomics. We demonstrate this by fine-tuning Enigma on predicting three new molecular phenotypes -- ChIP-seq, RNA half-life, and translation efficiency -- achieving or exceeding the performance of state-of-the-art task-specific models. We are providing Enigma for non-commercial use to benefit the broader research field. [Formula]Code: https://github.com/deepgenomics/enigma

genomics↗

bioRxiv2025

Sequence based prediction of cell type specific microRNA binding and mRNA degradation for therapeutic discovery

MicroRNAs and RNA binding proteins are crucial elements of post-transcriptional gene regulation, which governs the fate of mRNA molecules in the cell. However, the landscape of these regulatory interactions, particularly across different mammalian cell types, remains underexplored. We describe REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation directly from RNA sequence. REPRESS was trained on AGO2-CLIP, miR-eCLIP and Degradome-Seq data profiling millions of microRNA binding and mRNA degradation sites across multiple cell types in human and mouse. It reveals biology that other state-of-the-art methods did not, such as identifying repressive non-canonical miRNA target sites and decoding the regulatory effects of sequence context and miRNA binding site multiplicity. REPRESS outperforms other advanced methods and neural architectures on a comprehensive suite of seven orthogonal tasks, including identifying genetic variants that affect microRNA binding, predicting out-of-distribution data from massively parallel reporter assays, and predicting canonical and non-canonical miRNA mediated repression. To demonstrate the general utility of REPRESS, we show that it provides insights into novel biology and the design of RNA therapeutics. Code is available at : https://github.com/deepgenomics/repress

genomics↗

bioRxiv2023

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics. We describe a foundation model for RNA biology, "BigRNA", which was trained on thousands of genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence. Unlike approaches that are restricted to missense variants, BigRNA can identify pathogenic non-coding variant effects across diverse mechanisms, including polyadenylation, exon skipping and intron retention. BigRNA accurately predicted the effects of steric blocking oligonucleotides (SBOs) on increasing the expression of 4 out of 4 genes, and on splicing for 18 out of 18 exons across 14 genes, including those involved in Wilson disease and spinal muscular atrophy. We anticipate that BigRNA and foundation models like it will have widespread applications in the field of personalized RNA therapeutics.

bioinformatics↗

bioRxiv2020

Ancient admixture into Africa from the ancestors of non-Africans

Genetic diversity across human populations has been shaped by demographic history, making it possible to infer past demographic events from extant genomes. However, demographic inference in the ancient past is difficult, particularly around the out-of-Africa event in the Late Middle Paleolithic, a period of profound importance to our species history. Here we present SMCSMC, a Bayesian method for inference of time-varying population sizes and directional migration rates under the coalescent-with-recombination model, to study ancient demographic events. We find evidence for substantial migration from the ancestors of present-day Eurasians into African groups between 40 and 70 thousand years ago, predating the divergence of Eastern and Western Eurasian lineages. This event accounts for previously unexplained genetic diversity in African populations, and supports the existence of novel population substructure in the Late Middle Paleolithic. Our results indicate that our species demographic history around the out-of-Africa event is more complex than previously appreciated.

genomics↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗