850 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.
By reanalyzing public datasets with stringent statistical controls to establish a high-confidence Human Methylation Atlas of 1,828 sites, the authors developed and validated a transfer-learning-based deep learning model (AHLF-Methylation) that significantly improves the detection and localization of methylated peptides.
VirProtRAG is a retrieval-augmented generation framework that enhances viral protein function annotation by integrating hybrid literature retrieval and evidence-based re-ranking to produce verifiable, high-quality functional insights and expand existing expert curation.
The paper introduces MSpangenome, a genealogy-aware framework that bridges coalescent simulations and pangenome graph construction to generate realistic population pangenomes with known evolutionary ground truth, thereby enabling rigorous benchmarking of graph-based genomic tools.
Using the POInT tool to analyze gene tree discordance, this study provides evidence that "late" diploidization involving post-allopolyploidy genetic exchanges occurred in the teleost, Paramecium, and bakers yeast lineages, challenging the view that diploidization is always a rapid process.
The paper introduces synpact, a memory-efficient and fast PacBio HiFi read mapper written in Rust that utilizes a hierarchy of locally-consistent syncmer blocks to achieve accuracy comparable to state-of-the-art tools like minimap2 while significantly reducing peak memory usage and improving runtime on large or repetitive genomes.
This study utilizes AlphaMissense to map pathogenicity patterns across the GLUT transporter family, revealing that missense mutations are most detrimental in transmembrane domains, pore-lining residues, and the central cavity, while variations in pathogenicity across the family suggest the influence of functional redundancy and physiological essentiality.
ProteoDUDes is an open-source tool that enhances the accuracy of taxonomic profiling in metaproteomics by processing results from popular annotation tools to significantly reduce false positive rates, thereby enabling more reliable identification of functionally active organisms in complex samples.
The paper introduces GAZE, a physics-informed graph neural network that integrates gene expression, enzyme functional embeddings, and metabolite descriptors to achieve accurate zero-shot prediction of metabolite concentrations across diverse biological contexts without requiring metabolite-specific training.
WattmaMod is a novel deep learning framework that leverages self-supervised pretraining, wavelet-guided encoding, and dynamic cross-attention to enable robust, high-resolution, and extensible profiling of diverse RNA modifications from nanopore direct RNA sequencing data, even with limited labeled resources.
MCD Stitcher is an open-source Python tool that converts proprietary Imaging Mass Cytometry (.mcd) files into interoperable OME-TIFF formats with automated whole-slide stitching, enabling large-scale spatial analysis on standard workstations without vendor-specific software.