1012 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 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.
This paper introduces MAG-E, a simulation-based framework for end-to-end evaluation of metagenome-assembled genome (MAG) pipelines, which reveals that while metaSPAdes and COMEBin generally outperform alternatives in the human gut microbiome, current tools struggle with prophages and shared contigs, and quality control metrics like CheckM2 often misestimate genome quality.
PoolParty is a Python package that streamlines the design of complex DNA sequence libraries through a flexible, graph-based API featuring over 50 built-in operations and comprehensive design documentation.
The paper introduces the Inner Ear Knowledge Base (IEKB), an open-access resource that unifies curated gene-phenotype-disease associations, cochlear interactions, Bayesian candidate prioritization, explainable support relations, and a multi-entity scientific network to advance inner-ear genetics research through automated curation and interactive exploration tools.
The authors present GMIP-PLSR, a Nextflow-based pipeline that integrates GWAS with multi-omics data using Partial Least Squares Regression to overcome multicollinearity issues in existing tools like PoPS, thereby significantly improving gene prioritization and biological insights for complex traits such as NAFLD.