1012 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.
GPSNorm is a novel Bayesian spatial normalization framework that jointly models technical variation, spatial structure, and biological signal using a hierarchical Gaussian process approach to propagate normalization uncertainty into downstream differential expression analysis, thereby improving the robustness and interpretability of spatial transcriptomics data.
The paper introduces SyntenyPair Explorer, a lightweight, installation-free, browser-based tool that enables interactive visualization and exploration of pairwise genome synteny using standard comparative genomics file formats without requiring local software or server infrastructure.
This paper presents "deepthought," a modular microscopy acquisition stack that standardizes common components like device access and storage while isolating application-specific interpretation and targeting logic, thereby enabling autonomous, analysis-in-the-loop imaging across diverse biological scenarios such as high-throughput screening and long-term live-cell tracking.
Using density functional theory calculations, this study proposes a novel, catalyst-free radical chemistry pathway for the formation of glycine dipeptides under prebiotic conditions, characterized by low electronic energy barriers and supported by detailed analyses of intermediate radical properties.
The paper introduces Tx2Mol, a transcriptome-guided framework that translates gene-expression signatures into chemically plausible and biologically relevant de novo molecules, demonstrating superior performance over existing baselines in preserving phenotypic responses across bulk, single-cell, and patient-derived disease contexts.
The paper introduces pyfraglib, a comprehensive Python-based platform that integrates cfDNA fragment extraction, statistical modeling, cohort-level comparative analysis, and in silico simulation to enable end-to-end fragmentomics workflows, which were successfully validated on both simulated data and real-world central nervous system lymphoma samples to identify prognostic subgroups.
This paper presents a publicly available, web-based automated pipeline that integrates whole-genome sequencing data with comprehensive hazard characterization tools to provide a transparent, reproducible, and rule-based risk assessment workflow for microbial pesticides, offering significant improvements over existing regulatory methods.
This paper introduces the R-package `sbivar`, which provides a suite of modified statistical tests and variance estimators to rigorously assess bivariate spatial associations between multi-omics modalities with disjoint coordinates while properly accounting for spatial autocorrelation and high-dimensional computational challenges.
This study reveals that diverse gut bacteriophages have convergently evolved cysteine-enriched major capsid proteins with buried, highly conserved residues, suggesting a structural adaptation that stabilizes capsids against the hostile gut environment.
This study reveals that current missense pathogenicity predictors, trained primarily on loss-of-function variants, systematically under-score activating cancer drivers due to their distinct structural and evolutionary features, prompting the authors to provide an openly licensed benchmark, per-gene calibration maps, and a recalibrated framework (OncoCal) to improve somatic variant interpretation.