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.
OpenAntigens is a free, no-login database that streamlines antigen construct design for the human secreted and cell-surface proteome by integrating diverse structural, topological, and comparative data into a unified, interactive workflow to generate reproducible and cross-reactivity-aware design suggestions.
This paper argues that incomplete reference profiles render bulk deconvolution targets non-identifiable rather than merely difficult to estimate, demonstrating that standard point estimates often lack coverage and can reverse biological conclusions, while proposing a new framework that prioritizes certified precision, coverage, and false-certification metrics over simple shrinkage.
The paper introduces MESIC, a method that integrates curated gene knowledge into a semantic coordinate system to map single-cell expression data into interpretable components, enabling biologically meaningful clustering and annotation of cell types without relying on post-hoc interpretation.
The paper introduces LDDM, a unified 3D generative model that overcomes synthetic accessibility challenges in drug discovery by enabling the rapid design of novel, target-specific small molecules and peptides, which were experimentally validated to achieve high binding affinity and structural accuracy across five protein targets.
This study demonstrates that in virtual spatial transcriptomics, pretrained gene representations primarily enhance the prediction of a gene's mean expression across tissues rather than its spatial variation, revealing that cross-gene generalization is driven more by broad expression transfer than by the recovery of specific spatial patterns.
This paper systematically evaluates DrugCLIP's contrastive learning embeddings for structure-based virtual screening, revealing that while its pocket embeddings offer fast and robust structural similarity search and its ligand embeddings capture pocket-aware chemical patterns with strong generalization to unseen targets, performance is significantly limited by conformational variations and inaccuracies in predicted protein pockets.
The paper introduces fDOG-Assembly, a novel tool that enables targeted, feature architecture-aware ortholog searches directly in unannotated genome assemblies, thereby overcoming the limitations of traditional methods that rely on pre-annotated proteomes and revealing new biological insights such as widespread -lactam biosynthesis potential in springtails.
SixPack-AbScan is a free, species-agnostic web server that aids researchers working with non-model organisms in identifying potential antibody cross-reactivity by computationally screening for epitope sequence conservation, thereby prioritizing candidates for experimental validation.
The paper introduces TriGer, a triple-graph framework that overcomes the limitations of fragmented pairwise associations in multi-omics data by identifying interpretable, coordinated many-to-many modules across molecular layers, demonstrating its effectiveness in recovering structured signals in simulations and revealing disease-specific biological patterns in inflammatory bowel disease and colorectal cancer.
FORGE is a configurable, containerized Nextflow pipeline that automates end-to-end single-nucleus multiome analysis by integrating gene expression and chromatin accessibility data for regulatory network inference and differential testing, thereby enhancing scalability, reproducibility, and biological interpretation across diverse datasets.