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 TCMCard, a high-confidence digital infrastructure that utilizes a Multi-Dimensional Evidence Integration framework to filter low-quality data and provide a reliable, interactive platform for analyzing the synergistic mechanisms of Traditional Chinese Medicine.
This study addresses the lack of curated trnL reference databases by systematically comparing OBITools3/ecoPCR, RESCRIPt, and MetaCurator to generate and evaluate high-quality plant DNA metabarcoding resources, demonstrating that the optimal curation tool varies depending on the specific trnL region analyzed.
The paper introduces Immunotype, a deep learning-based ensemble predictor that accurately determines HLA class I allotypes directly from complex mass spectrometry-based immunopeptidomics data, thereby enabling rapid and cost-effective HLA typing for large-scale immunotherapy research.
This study systematically benchmarks six ambient RNA removal tools across diverse single-cell platforms, revealing that while CellBender and SoupX offer reliable denoising, tools like scAR and CellClear critically fail by generating artificial counts and spurious cell types, thereby establishing count matrix integrity as a paramount criterion for tool selection.
This paper presents an open-source, statistically rigorous workflow for analyzing complex mass spectrometry imaging experiments, demonstrating through case studies and simulations how critical decisions regarding signal processing, region selection, and statistical modeling impact the detection of differentially abundant analytes.
This paper extends contrastive principal component analysis by introducing kernel-weighted spatial (k-ρPCA) and functional (f-ρPCA) methods that unify spatial and functional data analysis within a single mathematical framework, demonstrated through applications in genomics.
DIANA is a multi-task neural network that leverages unitig abundances from ancient metagenomic data to accurately predict sample metadata and generalize to unseen categories, offering a robust, reference-free solution for quality control and discovery in the field.
By integrating human-virus protein-protein interaction maps with residue-resolved contact data, this study reveals that positive and negative selection signatures exhibit distinct spatial patterns across virus-targeted host proteins, with positively selected residues clustering more prominently on interfaces shared between viral and endogenous partners, thereby highlighting these "mimic-targeted" sites as focal points of adaptive evolution.
CoPhaser is a versatile, biologically informed variational autoencoder that disentangles context-dependent periodic trajectories from other sources of cellular variability in single-cell RNA sequencing data, enabling the accurate reconstruction and analysis of diverse biological cycles such as the cell cycle, circadian rhythms, and developmental clocks across various tissues and disease states.
This paper introduces a GPU-accelerated quaternion Fourier transform framework that encodes DNA as a quaternion-valued signal to enable alignment-free genomic analysis, revealing universal structural periodicities like the helical repeat and species-specific features such as nucleosome positioning through multi-channel spectral fingerprints while achieving whole-genome processing speeds of under one second on commodity hardware.