Biology subjects

Hassett, R.

Publications and source records attributed to Hassett, R..

3 recordsLinked to original sources

bioRxiv2026

A comparative analysis of promoter-proximal pausing reveals kinetic and distributional dimensions of variation

Promoter-proximal pausing of RNA polymerase II is a key regulatory checkpoint in metazoan transcription. Despite extensive study of this process, quantitative methods for comparing pausing dynamics across biological contexts have been lacking. Here we introduce a model-based framework for rigorous comparative analysis of both pause-escape kinetics and pause-site distributions. We apply this framework to public datasets together with newly generated PRO-seq and Micro-C data from primate immune cells, enabling a broad comparison of promoter-proximal pausing across conditions, cell types, and species. Analyses of these datasets revealed striking differences across transcriptional perturbations and distinct patterns of variation in pause-escape kinetics and pause-site distributions across cell types and species, with only weak coupling between them. Integration with chromatin and sequence features showed that lower pause-escape rates are associated with stronger promoter-proximal nucleosome occupancy, whereas changes in pause-site dispersion are associated with sequence features such as GC skew. Together, these results reveal kinetic and distributional dimensions of pausing variation across biological contexts.

bioinformatics↗

bioRxiv2025

Variational Inference with Node Embeddings (VINE) for Scalable Bayesian Phylogenetics

Bayesian methods are now widely used in reconstructing both species and cell-lineage phylogenies, but they remain heavily reliant on computationally intensive Markov chain Monte Carlo sampling. Phylogenetic variational inference (VI) circumvents this dependency but so far has been limited in speed and scalability. Here we introduce Variational Inference with Node Embeddings (VO_SCPLOWINEC_SCPLOW), a computational method that combines an embedding of taxa in a high-dimensional space and a distance-based "decoder" with several algorithmic innovations to dramatically improve phylogenetic VI. VO_SCPLOWINEC_SCPLOW supports both standard DNA substitution models and CRISPR barcode-mutation models for inference of cell-lineage trees and tissue-migration histories. In extensive simulation experiments, we show that VO_SCPLOWINEC_SCPLOW can effectively approximate the results of the best available Bayesian methods with speeds orders of magnitude faster. We then apply VO_SCPLOWINEC_SCPLOW to [~]1,000 complete SARS-CoV-2 genomes and [~]900 lung-cancer cell barcodes, showing reductions in compute time from days to hours or minutes.

bioinformatics↗

bioRxiv2025

Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data

Cell-lineage tracing now enables direct study of tissue migration in metastatic cancer, but current reconstruction algorithms are limited by a reliance on strong parsimony assumptions and pre-estimated cell-lineage phylogenies. Here, we introduce a probabilistic modeling and inference framework, called BEAM (Bayesian Evolutionary Analysis of Metastasis), that provides richer information about complex metastatic histories. Based on the flexible BEAST 2 platform for Bayesian phylogenetics, BEAM infers a full posterior distribution over cell-lineage phylogenies and tissue migration graphs, complete with timing information. We show using simulated data that BEAM reliably outperforms current methods for inference of tissue migration graphs, especially for more complex histories. We then apply BEAM to public data sets for lung and prostate cancer, finding support for distinct modes of migration across clones and reseeding of primary tumors. Overall, BEAM serves as a powerful framework for revealing the modes, timing, and directionality of tissue migration in metastatic cancer.

cancer biology↗