Library

Single-nucleus RNA sequencing of the Alzheimer's disease middle temporal gyrus revealed sex-specific microglial signatures and identified MERTK genetic variation as a female-specific risk factor.

Using a novel machine learning framework called symclatron, researchers found that 15 to 23 percent of uncultivated bacteria and archaea across half of all known phyla likely engage in symbiotic lifestyles.

This paper proposes a theoretical three-stage model of lifespan aging driven by increasing biological entropy and introduces a conceptual Multiscale Entropic Aging Index (MEAI) to quantify systemic disorder across molecular, cellular, and tissue scales.

OrthoFinder v3 introduces a scalable, two-step phylogenetic algorithm that increases orthogroup inference accuracy by 5-7% and can process over 4,000 genomes, overcoming the quadratic time complexity bottleneck of previous methods.

The authors present a near-perfect telomere-to-telomere diploid genome benchmark for HG002 that adds 15.3 percent of previously unmapped genomic sequence and shows de novo assembly outperforms standard variant calling by an order of magnitude.

ESMDynamic is a deep learning model that predicts protein residue-residue contact dynamics directly from single sequences, matching or outperforming state-of-the-art ensemble methods like AlphaFlow while requiring orders-of-magnitude less computation.

The authors developed hoodscanR, a Bioconductor package that generates probabilistic, cell-level neighborhood profiles for spatial transcriptomics data to characterize mixed tissue environments.

Integrating multi-omics data with artificial intelligence accelerates precision drug discovery by shifting from single-target approaches to network-based models, significantly reducing development timelines and improving target identification.

Incorporating microbial genomic data into groundwater denitrification models via dynamic flux balance analysis and deep learning accurately predicts nitrate depletion and nitrite transients, outperforming conventional geochemistry-only models.

Toll-like receptors (TLRs) function as highly regulated, multi-layered information-processing hubs controlled by post-translational modifications, epigenetics, and metabolism, the dysregulation of which directly drives diseases like lupus and sepsis.

The Single-Cell Pediatric Cancer Atlas (ScPCA) Portal provides uniformly processed single-cell and single-nucleus RNA sequencing data for over 700 pediatric tumor samples across 55 cancer types.

After twenty years of development, the igraph 1.0 library has been released, providing a fast C-based core with Python, R, and Mathematica interfaces capable of analyzing networks with billions of edges.

A comprehensive benchmark of nanopore DNA methylation tools reveals that older models like Dorado v4r1 and RockFish are best for CpG sites, while newer Dorado v5 models excel at non-CpG and 6mA detection.

By representing phylogenetic trees in a tropical geometric space called "palm tree space," researchers achieved faster geodesic computations and better performance in statistical tasks like PCA compared to the standard BHV tree space.

By combining confirmed case counts and genetic sequencing data from US states between January 2021 and March 2022, researchers developed a model to jointly estimate the effective reproduction numbers and frequencies of co-circulating SARS-CoV-2 variants.

Co-Scientist, a multi-agent AI system built on Gemini 2.0, autonomously generates and refines novel scientific hypotheses that were successfully validated in wet-lab experiments for acute myeloid leukemia and liver fibrosis.

IQ-TREE 3 is a major update to a popular phylogenetic software tool, introducing advanced evolutionary models, sequence simulation, and tools for analyzing massive datasets like pandemic viral genomes.

BioMaster is a multi-agent framework utilizing Retrieval-Augmented Generation and role-based agents to automate complex bioinformatics workflows like RNA-seq and Hi-C processing more accurately than existing methods.

This review provides a comprehensive overview of large language models in bioinformatics, detailing their core components like tokenization and transformer architectures, and their applications across genomics, proteomics, and drug discovery.