N6-methyladenosine (m6A) is the most abundant internal modification in eukaryotic mRNA. However, most existing predictors use adenosine-centered formulations that are computationally inefficient and prone to false positives. Here we present m6A-FORM, a transformer-based foundation model for RNA methylation that uses MeRIP-seq peaks as methylation-enriched priors and is pretrained on approximately 22 million peak-derived sequences from 143 human MeRIP-seq studies. After fine-tuning with high-confidence single-nucleotide m6A annotations from m6A-Atlas v2.0 and GLORI, m6A-FORM-sites achieves state-of-the-art m6A site prediction performance, with a PR-AUC of 0.635 and ROC-AUC of 0.988, improving PR-AUC by at least 0.14 over existing methods while enabling substantially faster inference. Task-specific adaptation further supports prediction of binding sites for 19 m6A-associated regulators and identification of YTHDF2-bound m6A sites associated with mRNA degradation. Applying m6A-FORM across 67 datasets from 24 human tissues identifies 19,631 tissue-conserved sites with distinct localization, clustering, methylation, expression, RBP-interaction, and decay-associated signatures.

Accurate evaluation of genome assemblies within highly repetitive regions, such as centromeres, remains a major open challenge in genomics. Conventional benchmarking relies on sequence alignment, which becomes problematic in regions of high homogeneity and divergence. Here, we framed centromere assembly evaluation as a comparative distribution problem in a compact centeny representation by computing genomic distances between functional motifs, rather than relying on nucleotide sequence. Our distribution-based metric assesses agreement between a query and a target chromosome by comparing their centromeric inter-motif distances rendered by KL divergence. When applied genome-wide to currently available human telomere-to-telomere (T2T) genomes, this approach yields an accuracy ranking for the entire assembly and for each individual chromosome. Altogether, we present a rapid and robust scoring system based on genomes numerical rendering of inter-motif distances, that provides a quantitative standard of assembly integrity in repetitive DNA regions and establishes a bona fide framework for chromosome-level genome-to-genome comparison.

Tissue graph counterfactuals ask how a cell's expression would change under altered spatial neighbor contexts. Such queries are central to predicting cell behavior in tissues, but lack a unified definition, with existing methods targeting specific intervention types or treating cells as i.i.d. In this work, we first formalize tissue graph counterfactuals as a class of spatial interventions that either rewire connections between cells (edge perturbation) or modify the expression of their neighbors (node perturbation). We then introduce Cellina ( this https URL ) - a framework that uses supervised disentanglement to decompose a cell's intrinsic state from its spatial context, using the latter as a conditioning input for counterfactual predictions. Across benchmarks spanning over 2.5 million spatially-resolved cells in colorectal cancer and mouse brain, Cellina outperforms spatially-informed and non-spatial competitors in in-silico graph perturbations, disentanglement, and scalability. Additionally, we show that Cellina reveals biologically distinct cancer subdomains in an unsupervised manner and enables targeted neighbor perturbation simulations.

Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis. Existing unbalanced Optimal Transport (OT) methods treat mass as a continuous fluid, performing inference at the population level. However, this macroscopic view often fails to capture the discrete, jump-like nature of birth-death events at single-cell resolution, which is essential for understanding lineage branching and fate decisions. We present Unbalanced Schrödinger Bridge (USB), a simulation-free framework for learning underlying dynamics that effectively integrates both stochastic and unbalanced effects which also models the discrete, jump-like birth-death dynamics at single-cell resolution. Theoretically, USB provides a tractable solution to the Branching Schrödinger Bridge (BSB) problem, offering a rigorous microscopic interpretation where individual cells undergo both Brownian motion and discrete birth-death jumps. Technically, the method implements an efficient solver by introducing a simulation-free training objective that effectively scales to high-dimensional omics data. Empirically, we demonstrate on both simulated and real-world datasets that USB not only achieves trajectory reconstruction performance better than or comparable to deterministic baselines but also uniquely enables realistic discrete simulation of birth-death dynamics at single-cell resolution.