1210 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 paper proposes a network-based global analysis of gene-disease associations to create a specificity score that improves the prioritization of genetic variants for rare disease diagnosis.
By combining reverse folding and frustration analysis, this study proposes that certain evolutionary conserved frustration hotspots act as "architectural spandrels"—physical constraints inherent to protein folds that are not directly selected for stability but are later co-opted by evolution to facilitate molecular function.
This study presents a culture-independent, LC-HRMS-based analytical workflow using R to identify and validate a diverse landscape of 59 potential siderophores within the microbiomes of three marine sponge species.
This paper demonstrates that Haar-like wavelets effectively sparsify the phylogenetic covariance matrices of realistic "critical beta-splitting" trees, enabling a biologically meaningful distance metric that identifies significant evolutionary splits responsible for compositional differences between microbial environments.
CoPrimeEEG is a neural reconstruction framework that utilizes co-prime sub-Nyquist sampling and a CRT-guided dual-branch architecture to efficiently reconstruct high-fidelity EEG signals while simultaneously predicting temporal masks and bandpower features.
The paper introduces the Neighborhood Product Co-localization (NPC) score, an efficient and interpretable multivariate metric designed to quantify and localize the spatial co-occurrence of cell types in spatial transcriptomics datasets.
This paper presents an integrated in-silico workflow utilizing machine learning and chemical data resources to enumerate and prioritize thousands of potential insecticide transformation products, thereby expanding the known chemical space of the exposome.
This paper introduces a freely accessible, continuously updated online book that provides reproducible code examples, datasets, and workflows for analyzing spatial transcriptomics data using Bioconductor in R, while also supporting interoperability with Python.
This study demonstrates that tear fluid collected after moderate-intensity aerobic exercise, but not before, significantly suppresses myopia progression in a guinea pig model, revealing a novel exercise-mediated therapeutic mechanism for treating myopia.
The paper introduces DEPower, a web-based tool grounded in the DESeq2 model framework that enables researchers to perform rigorous, analytical power analysis for determining optimal sample sizes in both bulk and single-cell RNA-seq experiments.