1018 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.
PepCABO is a novel latent-space Bayesian optimization framework that leverages a dual variational autoencoder with contrastive alignment to efficiently discover high-affinity peptide-MHC binders by enabling structured knowledge transfer across alleles and improving sample efficiency in both low- and high-budget experimental settings.
This paper introduces an extension to the ColBuilder framework that enables the generation of atomistic collagen fibril models incorporating non-enzymatic advanced glycation end-product (AGE) crosslinks, providing validated force-field parameters and demonstrating their distinct structural and mechanical impacts compared to enzymatic crosslinks.
The paper introduces PBML, a novel algorithm that efficiently identifies biologically significant, long-range shared ancestry tracts (kL-SMEMs) in large, diverse haplotype panels by filtering out uninformative short matches on a single compressed PBWT index, achieving substantial speed and memory improvements over existing methods.
This paper proposes a Bayesian AMMI-based simulation framework that generates interpretable genotype-by-environment interaction effects using high-throughput environmental covariance matrices, demonstrating its ability to capture directional relationships and improve genomic selection strategies under complex environmental conditions compared to traditional simulation methods.
This paper introduces OpenFold-TRT, a set of accelerations combining TensorRT and MMseqs2-GPU that enables high-throughput, accurate protein structure prediction across diverse hardware—from compact ARM systems to large-scale datacenters—achieving up to 131x faster inference than AlphaFold2 without compromising accuracy.
This study reveals that *Aedes aegypti* mosquitoes employ distinct, concentration-dependent transcriptomic strategies to resist type I and type II pyrethroids, ranging from cuticle thickening and metabolic detoxification for permethrin to mitochondrial and oxidative stress adaptations for lambda-cyhalothrin, highlighting the need to consider both insecticide type and dosage in vector control programs.
The paper introduces stMCP, a Model Context Protocol framework that enables accessible, reproducible, and privacy-preserving spatial transcriptomics analysis by allowing large language models to orchestrate local computational tools through natural language, thereby empowering biologists to independently explore complex datasets without the costs and risks of uploading massive data to cloud-based AI services.
This study introduces the Off-target Probe Tracker (OPT) tool to identify and validate off-target probe binding in 10x Genomics Xenium panels, demonstrating that such non-specific interactions can significantly distort spatial gene expression profiles and compromise data accuracy.
This paper evaluates recent deep-learning methods for RNA structure prediction and finds that while they perform well on RNAs resembling known structures, they struggle to generalize to novel folds and currently lack reliable accuracy estimates for model selection.
This paper introduces PROPTIMUS RAPHAN, a computationally efficient method that optimizes protein structures by dividing them into overlapping substructures to achieve linear scaling and results comparable to traditional constrained alpha-carbon optimization in significantly less time.