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.
The paper introduces the Explainable Prototype Booster (EP-Booster), a method that integrates biological prior knowledge into foundation models to refine latent representations for accurate and interpretable gene expression prediction from histological images, thereby overcoming the cost and time limitations of spatial transcriptomics.
PixelDeck is an open-source, local-first browser application that streamlines the organization, deduplication, and interactive browsing of large biomedical image and video collections on standard hardware through a modular architecture featuring recursive import, SHA-256 duplicate detection, and asynchronous processing.
The paper introduces Tiberius, a deep learning-based ab initio gene predictor that achieves state-of-the-art accuracy and significantly faster runtimes across diverse eukaryotic clades by training lineage-specific models, effectively addressing current bottlenecks in genome annotation.
This paper adapts the kallisto-bustools suite to enable efficient, accurate, and uniform pre-processing of bacterial single-cell RNA-seq data, addressing challenges posed by operons and short gene lengths to establish a scalable foundation for microbial transcriptomics.
The paper introduces COSMOS+, an interpretable framework that integrates data-driven multi-omics factor analysis with mechanistic prior knowledge to model causal signal propagation and generate actionable hypotheses about disease drivers, demonstrated through applications in breast cancer resistance models and patient cohorts.
Verticall is a fast, non-parametric, open-source tool designed to efficiently detect recombination and generate recombination-free phylogenies in large-scale bacterial genomic datasets (ranging from hundreds to thousands of genomes), demonstrating superior or comparable performance to existing methods like Gubbins and ClonalFrameML across diverse evolutionary scales.
The paper introduces Mievformer, a Transformer-based masked self-supervised learning framework that effectively couples cellular and microenvironmental heterogeneity in spatial omics data by learning probabilistic representations of cell states conditioned on their spatial context, thereby outperforming existing methods in niche clustering and enabling the discovery of biologically significant cell subpopulations and gene-expression signatures.
This study proposes a "genomic dialects" framework demonstrating that species-specific codon usage bias patterns, which reflect translational fidelity and protein stability constraints rather than true phylogeny, are significantly shaped by amino acid physicochemical properties and the second codon base classification.
This paper introduces "iterative sampling," a deterministic and scalable framework implemented in the memerna tool that exhaustively enumerates RNA secondary structures in increasing order of free energy, thereby overcoming the limitations of stochastic methods to enable efficient modeling of cotranscriptional folding and the identification of kinetic traps.
SpaceBender is a novel, open-source denoising method for spatial transcriptomics that leverages spatial-specific information to remove RNA diffusion noise, thereby outperforming existing tools and revealing enhanced biological insights across various data resolutions.