1197 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.
This paper introduces the EndoMT R package, a curated resource of directionally assigned gene sets designed to enable specific and sensitive transcriptomic enrichment analysis of endothelial-mesenchymal transition across diverse experimental models and disease contexts, addressing the limitations of existing general epithelial-mesenchymal transition frameworks.
This study demonstrates that using matched H&E histology as a training-time supervisory signal fails to consistently improve transcriptomic foundation models because slide-specific batch structures in spot-based spatial transcriptomics limit the shared gene-morphology signal, ultimately reorganizing existing information without recovering lost biological details.
The paper introduces scTAPAS, a bioinformatic framework that reconstructs donor genotypes directly from single-cell RNA sequencing reads, enabling accurate downstream eQTL and HLA-TCR association analyses even in the absence of matched conventional genotyping data.
PIE is a novel model that generalizes cellular perturbation effects across unseen contexts, perturbations, and datasets by reformulating the learning task around population-level effects and leveraging auxiliary biological inputs, achieving state-of-the-art performance in predicting gene expression changes and differentially expressed genes.
This study demonstrates that homology-based variant-effect predictors systematically fail for Cytochrome P450 pharmacogenes because they primarily track protein abundance rather than catalytic activity, necessitating a new modeling approach that incorporates substrate-specific context to accurately predict functional consequences.
EnsPlex is a multi-source framework that integrates diverse structural sampling methods with a topology-aware quality assessment model (FACET) to significantly improve protein complex prediction accuracy and success rates in antigen-antibody systems compared to existing approaches.
This study introduces the MAPLE algorithm to analyze multi-sample spatial transcriptomics data, revealing that idiopathic pulmonary fibrosis progression involves region-specific fibrotic programs and the spatial colocalization of senescent cells with aberrant basaloid niches in human lungs.
This paper introduces QHGM, a novel quantum Hamiltonian-based generative model paired with a scalable variational quantum algorithm (VQ-Net) that significantly outperforms classical methods in inferring gene regulatory networks from single-cell transcriptomics data, as demonstrated by superior accuracy on synthetic benchmarks and biologically relevant insights in glioblastoma.
MetaUmbra is a novel computational tool that enables statistically controlled, genome-level presence inference in metaproteomics by formally evaluating unique and shared peptide evidence against a reference genome panel to resolve taxonomic ambiguity.
This paper establishes that applying standard scRNA-seq preprocessing workflows to data generated by a metacell model results in a Gaussian mixture model in PCA space, a finding derived via random matrix theory that reveals the metacell model's limitations in capturing gene correlations and underestimating variance in real datasets while offering a framework for improving downstream statistical modeling.