💻 Category

bioinformatics

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.

💻 bioinformatics

Phenotypic reversion and target prioritization for cellular inflammation via representation learning with foundation models

This study presents a proof-of-concept framework using single-cell foundation models and a large-scale Perturb-seq dataset to successfully prioritize genetic targets for reversing cellular inflammation, demonstrating that incorporating disease-relevant proinflammatory conditions significantly improves the identification of biologically relevant therapeutic targets compared to basal conditions alone.

Wong, D. R., Piper, M., Qiao, J., Russo, M., Jean, P., Clevert, D.-A., Arroyo, J., Pashos, E.2026-03-06
💻 bioinformatics

GraTools, an user-friendly tool for exploring and manipulating pangenome variation graphs

GraTools is a fast, user-friendly, and open-source command-line tool that streamlines the manipulation and analysis of pangenome variation graphs directly from GFA files by enabling efficient subgraph extraction, sequence retrieval, and diverse genomic analyses through a modular architecture that integrates with existing bioinformatics workflows.

Ravel, S., Marthe, N., Carrette, C., Mohamed, M., Sabot, F., Tranchant-Dubreuil, C.2026-03-05
💻 bioinformatics

Machine Learning Ensemble Reveals Distinct Molecular Pathways of Retinal Damage in Spaceflown Mice

By applying a machine learning ensemble to retinal gene expression data from spaceflown mice, this study reveals that oxidative lipid peroxidation and apoptotic cell death are distinct molecular pathways driving spaceflight-associated neuro-ocular syndrome, offering a framework for developing biomarkers and therapeutic targets to protect astronaut vision.

Casaletto, J. A., Scott, R. T., Rathod, A., Jain, A., Chandar, A., Adapala, A., Prajapati, A., Nautiyal, A., Jayaraman (…)2026-03-05
💻 bioinformatics

Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution

This paper demonstrates that a parameter-efficient nested birth-death process model, grounded in molecular evolutionary theory, achieves competitive performance with massive neural networks in modeling protein evolution, suggesting that incorporating CTMC-based structures into future neural phylogenetic approaches could yield more realistic and efficient models.

Large, A., Holmes, I.2026-03-05
💻 bioinformatics

Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing

The paper introduces SCOTCH, a platform-independent pipeline that leverages long-read single-cell RNA sequencing to robustly characterize gene expression at the isoform level by modeling non-overlapping sub-exons, thereby improving the quantification of known isoforms and the reconstruction of novel, cell-type-specific transcripts compared to existing methods.

Xu, Z., Qu, H.-Q., Mu, S., Kao, C., Hakonarson, H., Wang, K.2026-03-04
💻 bioinformatics

Towards Useful and Private Synthetic Omics: Community Benchmarking of Generative Models for Transcriptomics Data

This paper presents a community benchmark of 11 generative models for synthetic bulk RNA-seq data, revealing that while deep learning models offer high utility, they often face greater privacy risks compared to differentially private or simpler statistical approaches, highlighting the need to balance utility, biological fidelity, and privacy based on specific use cases.

Öztürk, H., Afonja, T., Jälkö, J., Binkyte, R., Rodriguez-Mier, P., Lobentanzer, S., Wicks, A., Kreuer, J., Ouaari (…)2026-03-04