856 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.
FoundedPBI is an ensemble deep learning framework that leverages genomic foundation models and novel long-context aggregation strategies to accurately predict phage-bacterium interactions from DNA sequences, significantly outperforming existing state-of-the-art methods on benchmark datasets.
This study utilizes structural modeling and computational analysis of 107 consecutive enzyme pairs in *E. coli* to demonstrate that, despite a tendency for these enzymes to interact, their catalytic sites are not systematically positioned in spatially optimized configurations to facilitate metabolite transfer.
The paper introduces Tripso, a self-supervised transformer model that moves beyond single latent representations to learn interpretable, gene program-centric embeddings of cell states, thereby enabling the discovery of biologically meaningful patterns and actionable hypotheses across development, disease, and experimental systems.
This paper introduces Signature Distance (SD), a structural generalization of energy distance that compares empirical distributions via sorted pointwise distance profiles to detect local density and topological changes, offering a differentiable and computationally efficient metric for improved generative model evaluation, hypothesis testing, and data augmentation in high-dimensional biological data.
This paper introduces Chromatix, an open-source, GPU-accelerated, differentiable wave-optics library built on JAX that standardizes optical simulations to overcome reusability and performance limitations in computational microscopy, achieving significant speedups across applications like holography and phase retrieval.
The paper introduces cellSight, an automated, user-friendly workflow designed to streamline single-cell RNA-sequencing analysis by reducing manual effort, enhancing reproducibility, and accelerating biological discoveries.
The paper introduces Cenote-Taker 3, a high-performance, open-source command-line tool that significantly outperforms existing methods in speed and accuracy for discovering and annotating viral genomes and prophages from sequencing data, while also offering complementary results to other leading tools like geNomad.
This study presents a fully computational pipeline that successfully designed and atomistically validated a high-affinity VHH nanobody targeting the PI/RuvC interface of SpCas9, establishing a stable, non-inhibitory bivalent hub architecture for recruiting secondary effectors to enhance CRISPR-Cas9 functionality.
This study benchmarks four generative AI tools to design de novo peptides targeting BMPR1A for cartilage regeneration, identifying a top-performing PepMLM candidate through a rigorous multi-metric computational framework and establishing a reproducible pipeline for future experimental validation.
CBIcall is an open-source, configuration-driven framework that ensures reproducible and standardized variant calling across diverse computing environments by validating parameters and dispatching workflows via a single YAML file, as demonstrated in large-scale cohort analyses like the EU HEREDITARY project.