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BGC Atlas v2 expands the collection of predicted biosynthetic gene clusters to over 16 million, providing taxonomic and environmental context for natural product discovery.

The paper classifies archaeal proteomes at the domain level, revealing that structural diversity is conserved across archaea and that unclassified proteins are due to classification limitations, not structural novelty.

This paper introduces SCENE, a probabilistic model for embedding cells and genes in scRNA-seq data, achieving state-of-the-art performance and interpretable visualization.

DELPHAI predicts heterogeneous perturbation responses by jointly training a fitness network and optimal transport network to identify differentially expressed genes and depleted cell lineages.

LRP2 is a proteogenomics pipeline that integrates long-read RNA sequencing and mass spectrometry to detect novel protein isoforms and improve their translation inference.

The paper traces the evolutionary history of nitric oxide signaling components across metazoan species using phylogenomic analysis, revealing conserved and divergent patterns in gene family evolution.

MMAD-Risk predicts age-dependent disease risks from plasma proteomes using a multivariate mixed AFT model, outperforming Cox regression on UK Biobank data.

Lacuna identifies cryptic binding pockets in proteins by analyzing conformational ensembles, achieving high recovery rates on benchmark datasets.

The paper analyzes cophylogeny simulators to compare their assumptions and structural biases in generating synthetic host-symbiont data.

LazypipeX improves virus discovery in mNGS data by offering customizable and efficient annotation strategies that reduce execution time while maintaining accuracy.

The paper introduces C2BM, a deep learning model that improves SNP prioritization by integrating genomic annotations and trait relationships, showing better predictions when related trait labels are available.

The paper develops a framework to rerank protein-protein interactions using structural and biological evidence, improving prediction accuracy over structural-only methods.

SPHERE enables the use of sensitive datasets in AI and open science by creating synthetic twins that preserve statistical properties and privacy.

fDOG-Assembly identifies orthologs in unannotated genomes, outperforming traditional methods by not relying on pre-annotated proteomes and recovering missed orthologs.

iDriver improves cancer driver discovery by accounting for patient-specific mutation burden, identifying known and novel drivers across coding and noncoding regions and outperforming existing methods.

This paper develops a method for microscale white matter tractography using S-OCT and validates it on simulated and mouse brain data, showing improved ODF estimation and tractography performance.

The paper models the impacts of imports and hybridisation in honeybees, finding that increased imports accelerate introgression and reduce fitness, with spatial spread and slow purging of introgressed material.

Locat identifies localized marker genes by testing both enrichment within and depletion outside cell populations in single-cell transcriptomics.

The paper identifies noncoding regulatory mutations in neuroblastomas that alter gene expression and contribute to tumor development through ASE analysis and integration with genomic data.

The paper models protein sequence evolution using an Ornstein-Uhlenbeck process in a latent space to improve contact prediction by integrating directed evolution data with natural homologs.