💻 Category

bioinformatics

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.

💻 bioinformatics

Comparative Transcriptomic Analysis of Gene Expression Patterns in Neutrophils from Rheumatoid Arthritis and COVID-19

This study compares neutrophil transcriptomic profiles in rheumatoid arthritis and COVID-19, revealing that while rheumatoid arthritis is characterized by a distinct interferon- and antiviral-response signature, it shares partial transcriptional overlap with COVID-19 while also exhibiting specific gene expression divergences that vary depending on the disease stage.

Nawani, S., Alam, M.2026-11-04
💻 bioinformatics

Optimiser Repairs for Living-Cell Self-Driving Laboratories: Performance and Redundancy under Experimental Constraints

This paper evaluates optimiser repairs in living-cell self-driving laboratories through extensive simulations, demonstrating that while specific exclusion and confirmation rules can improve performance under certain conditions, they often reproduce baseline trajectories when constrained by platform limitations, highlighting the critical need to account for explicit availability constraints and experimental regimes.

Brimo, n., Zhang, H., Serdaroglu, D. C.2026-11-04
💻 bioinformatics

scPCIP: cross-species annotation of plant cell types with conserved gene programs

The paper introduces scPCIP, a reference-free method that leverages conserved gene programs across 112,905 author-labeled plant cells to accurately annotate cell types in unseen species, outperforming existing marker-based and deep-learning approaches while revealing shared biological identities across diverse organs and species.

Yang, S., Si, C., Wu, Y., Sun, Z., Yao, C., Sun, X., Wang, L., Li, W., Zhong, Q.2026-11-04
💻 bioinformatics

RAPTORGraph: graph-based pathway modeling for causal discovery in single-cell perturbations

RAPTORGraph is a novel graph-based variational autoencoder that integrates curated biological pathways with optimal-transport alignment to eliminate intervention spillover and enable identifiable causal discovery, thereby improving the prediction of unseen perturbations and non-additive interactions in single-cell experiments.

Adhisantoso, Y. G., Schröder, S. K., Gress, M., Hernaez, M., Voges, J.2026-11-04
💻 bioinformatics

A comparison of two culture and three sequencing methods for detection of the urobiome

This study compares standard urine culture, enhanced quantitative urine culture, and three sequencing methods on pregnant participants' urine samples, revealing that while no single method is universally superior, long-read shotgun metagenomics offers the broadest taxonomic coverage and enhanced culture outperforms sequencing for specific taxa, highlighting the need to align method selection with specific research or clinical objectives.

Khemmani, M., Noronha, M. F., Pham, T., Ferneyhough, B., Roddis, M., Parkinson, N. J., Wolfe, A. J.2026-11-03
💻 bioinformatics

BioResearchBench: 50 Long Horizon Biological Research Tasks

BioResearchBench is a challenging benchmark of 50 long-horizon biological research tasks across 10 subdomains that reveals current frontier AI models struggle with scientific reasoning and experimental judgment despite often executing individual analytical steps correctly, highlighting critical gaps in data interpretation and decision-making for real-world scientific assistance.

Abedrabbo, S., Pryhoda, A., Goodman, K., Taavola, J., Milam, E., Albright, A., Jabal, R., Yang, M., Burrese, C., Tubby (…)2026-11-03
💻 bioinformatics

CNVision: a computational framework for functional interpretation of intragenic copy number variants

CNVision is a containerized computational framework that automates the standardized, transcript-aware functional interpretation of intragenic copy number variants by accurately mapping breakpoints to coding sequences and classifying reading-frame consequences, thereby significantly reducing manual analysis time and improving consistency compared to traditional methods.

Liao, M., Zhuo, X.2026-11-03