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.

💻 bioinformatics

VICAST: An Integrated Toolkit for Viral Genome Annotation Curation and Low-Frequency Variant Analysis in Passage Studies

VICAST is an integrated software toolkit designed to streamline viral passage studies by combining semi-automated genome annotation with manual curation and optimized low-frequency variant calling, offering superior speed and accuracy compared to existing tools for diverse viral families.

Handley, S. A., Chica Cardenas, L. A., Mihindukulasuriya, K. A.2026-03-18
💻 bioinformatics

Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response

This study develops a phenotype-structured mathematical model to demonstrate how intra-tumor proliferation heterogeneity and life-history trade-offs drive eco-evolutionary dynamics, revealing that while all treatments slow tumor growth, they induce distinct evolutionary trajectories by selectively enriching either fast- or slow-proliferating clones depending on the specific therapeutic targeting strategy.

Schmalenstroer, L., Rockne, R. C., Farahpour, F.2026-03-17
💻 bioinformatics

Integrated Artificial Intelligence and Quantum Chemistry Approach for the Rational Design of Novel Antibacterial Agents against Ralstonia solanacearum.

This study presents an integrated artificial intelligence and quantum chemistry framework to rationally design and computationally validate "Solres," a novel antibacterial agent targeting key virulence proteins in *Ralstonia solanacearum* to combat antimicrobial resistance in agriculture.

Gulumbe, D. A., Tiwari, G., Lohar, T., Nikam, R., Kumar, A., Giri, S.2026-03-17
💻 bioinformatics

Learning Universal Representations of Intermolecular Interactions with ATOMICA

The paper introduces ATOMICA, a geometric deep learning model trained on over two million complexes to generate universal, multiscale atomic representations of intermolecular interfaces across five molecular modalities, demonstrating superior performance in structure-function benchmarks and successfully predicting functional ligands for previously uncharacterized "dark" protein pockets.

Fang, A., Desgagne, M., Zhang, Z., Zhou, A., Loscalzo, J., Pentelute, B. L., Zitnik, M.2026-03-16
💻 bioinformatics

Metagenomic-scale analysis of the predicted protein structure universe

This study presents AFESM, a comprehensive dataset of 820 million predicted protein structures derived from AlphaFold2 and ESMfold, which reveals millions of structural clusters, uncovers novel domain folds and combinations, and highlights the critical role of metagenomic data and prediction quality in expanding our understanding of the protein structure universe.

Yeo, J., Han, Y., Bordin, N., Lau, A. M., Kandathil, S. M., Kim, H., Levy Karin, E., Mirdita, M., Jones, D. T., Orengo (…)2026-03-16
💻 bioinformatics

BiOS: An Open-Source Framework for the Integration of Heterogeneous Biodiversity Data

The Biodiversity Observatory System (BiOS) is an open-source, modular framework that overcomes data heterogeneity and fragmentation in biodiversity research by decoupling backend management from frontend presentation to provide both programmatic API access and an intuitive web interface for integrating diverse datasets under FAIR principles.

Roldan, A., Duran, T. G., Far, A. J., Capa, M., Arboleda, E., Cancellario, T.2026-03-16