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.
This paper introduces a geometry-aware framework that distinguishes interface-associated enrichment from true spatial localization in ligand-receptor interactions, revealing that tumor cell communication is better characterized as a continuum of spatial constraint rather than discrete communication regimes.
This study demonstrates that simultaneously correcting for both expression-based surrogate variables and genotype-based principal components in differential expression analysis significantly improves cross-dataset replicability and biological recall of known disease genes compared to using either method alone, establishing a combined correction framework as a standard practice for transcriptomic studies with matched genotype data.
This paper introduces *sosta*, an open-source Bioconductor package that enables the direct analysis of multicellular anatomical structures in spatial omics data, shifting the focus from single-cell arrangements to biologically relevant tissue-level organization.
This paper proposes a novel four-layer integrative framework that constructs gene regulatory networks directly from DNA sequences by leveraging information entropy, evolutionary conservation, and deep learning embeddings to bridge nucleotide-level constraints with network-level regulatory logic.
This paper argues that co-expression network analysis offers a more effective and parsimonious framework than traditional differential expression analysis for characterizing microglia, as it reveals context-dependent, concurrent molecular programs that better explain the cell type's functional heterogeneity.
The paper introduces Locat, a novel framework that identifies highly specific marker genes in single-cell transcriptomics by jointly testing for expression enrichment within compact cellular regions and depletion elsewhere, thereby enabling robust, interpretable, and batch-free cross-condition comparisons of cell populations and developmental trajectories.
This paper introduces CASPA, an automated, end-to-end pipeline for single-cell proteomics that overcomes existing analytical limitations through adaptive quality control, entropy-guided batch correction, and a refined large language model framework for context-aware cell type annotation, achieving high accuracy validated against orthogonal ground truth across diverse biological datasets.
This study demonstrates that ultra low-coverage whole genome sequencing (ulcWGS) serves as a cost-effective and reliable alternative to high-coverage methods for accurately estimating canine inbreeding levels across diverse breeds.
REBEL is a framework that ensures long-term, FAIR-compliant reproducibility in bioinformatics by using deep code inspection and curated knowledge to resolve missing system dependencies and generate self-contained, deterministic environments without requiring containerization expertise.
This study utilizes flow molecular dynamics simulations to demonstrate how hydrodynamic forces drive von Willebrand factor from an autoinhibited compact state to an activated extended conformation, revealing the specific roles of glycan-modulated autoinhibitory modules in mechanosensitive regulation.