1178 papers
Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.
On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.
Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.
This study identifies the essential metabolic enzyme PabB as a promising drug target for tuberculosis and utilizes computational pathway analysis and molecular docking to screen FDA-approved drugs, ultimately highlighting Ligand 5 and Ligand 27 as potential candidates for drug repurposing.
This study analyzes 1.7 million microscopy papers from 2010 to 2025 to reveal a growing reliance on image-analysis software—led by ImageJ/Fiji with increasing field-specific adoption of tools like QuPath and napari—while highlighting that citation counts often underestimate actual usage and urging researchers to explicitly cite and report their software methods to improve reproducibility.
This paper introduces a scalable and reproducible pipeline that integrates GWAS summary statistics with functional annotations to generate curated, balanced, and annotation-rich datasets for systematically studying variant associations with complex disorders.
The paper introduces Auto-AVS, an open-source computational framework that simulates 3D autophagic vacuoles and virtual TEM sectioning to validate stereological assumptions, revealing that while body-size estimates remain consistent, discrepancies in body-number distributions highlight the critical influence of spatial clustering and vacuole geometry on 2D microscopy inference.
The paper introduces DeepMAST, an evidence-governed multi-agent AI system that integrates spatial transcriptomics analysis with literature-grounded retrieval to automate target prioritization, successfully identifying ICAM-1 as a validated therapeutic target for idiopathic pulmonary fibrosis.
This paper introduces POISK, a large-scale patent-derived antibody dataset, to demonstrate that while current structure prediction models can statistically enrich binder identification over random selection, they lack the reliability for single-candidate screening and accurate epitope localization, necessitating their use alongside orthogonal experimental methods.
This study presents the largest integrated single-cell RNA sequencing atlas of cutaneous T cell lymphoma to date, utilizing rigorous statistical methods to overcome prior limitations and identify molecular subtypes and microenvironmental remodeling with significant prognostic value.
The paper introduces corrdeconv, a method that corrects the fundamental misinterpretation of disease-induced cell state shifts as abundance changes in bulk and spatial omics data, thereby overturning existing benchmarks and outperforming all published deconvolution methods by eliminating the majority of erroneous disease effect claims.
The authors present BUDDY, an interactive web platform that streamlines the functional modeling of human missense variants in *Saccharomyces cerevisiae* by integrating bioinformatic tools, structural visualization, and AI-assisted experimental design, thereby accelerating variant analysis and making it accessible to users without prior bioinformatics training.
This paper presents a systematic benchmarking study of 12 differential rhythmicity methods using a synthetic RNA-seq model to provide practical guidelines for biologists to select the most appropriate tools for analyzing time-series transcriptomic data based on specific experimental conditions and analytical goals.