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.
The paper demonstrates that the AI foundational model Boltz-2 significantly outperforms existing rescoring strategies in virtual screening, achieving twice the success rate on challenging datasets and enabling accurate, efficient ranking of millions of compounds on commodity hardware.
The paper introduces SIR, a nonparametric framework combining 2D isotonic regression with a wild bootstrap procedure to provide statistically rigorous, reproducible drug synergy testing that outperforms existing methods in concordance, robustness, and missing data prediction.
This paper introduces CosMxScope, an open-source Python framework that bridges the gap between CosMx Spatial Molecular Imager data and digital pathology tools by stitching image tiles, converting spatial coordinates into GeoJSON for QuPath compatibility, and enabling interactive visualization of gene expression alongside cell morphology.
The Track Hub Quickload Translator is a freely available Python-based web application that converts data between UCSC Genome Browser track hubs and Integrated Genome Browser Quickload formats, enabling researchers to visualize tens of thousands of published genome assemblies in either browser.
The paper introduces VaLPAS, a Python-based framework that leverages variation in multi-omics data to predict the functions of unknown proteins by identifying associations with molecules of known function, thereby addressing the "functional dark matter" in protein space.
This paper presents MOAflow, a re-engineered, containerized Nextflow pipeline that enhances the scalability, reproducibility, and portability of MOA-seq data analysis while maintaining consistency with original results.
This study utilizes AI protein structure predictions to demonstrate that the hydrophobic ligand-binding cavity in vertebrate TLR2 is an evolutionarily conserved feature essential for pathogen recognition, while revealing that similar cavities in invertebrate TLRs and other LRR domains arose through independent convergent evolution rather than shared ancestry.
CLOP-DiT is a novel three-stage pipeline that leverages contrastive language-omics pretraining and conditional diffusion transformers to generate realistic, text-guided single-cell transcriptomic profiles from structured biological metadata, demonstrating the feasibility of controlled cell-state simulation while transparently acknowledging current limitations in reproducing cross-dataset variability.
This study computationally designed and evaluated a synthetic GluN1-mimetic peptide that demonstrates significantly stronger predicted binding affinity to pathogenic autoantibodies compared to a scrambled control, establishing a scalable framework for developing peptide decoys to treat anti-NMDAR autoimmune encephalitis.
This paper introduces Cellector, a computational tool designed to accurately detect rare foreign genotype cells in single-cell RNA sequencing data, enabling the identification of measurable residual disease in leukemia patients post-transplant and the analysis of microchimerism with high sensitivity.