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.
EXTRARNAS is a reproducible, Docker-based Java framework that automates the extraction of RNA structural annotations using multiple tools and standardizes their heterogeneous outputs into BPSEQ and the new BPSEQE formats to facilitate consistent comparison of canonical and non-canonical interactions.
The paper introduces dnoise, a fast open-source Rust tool that significantly reduces the storage footprint of Bruker timsTOF native data by removing redundant points while preserving analytical accuracy in both DDA and DIA workflows.
This paper presents MetaDome 2027, a comprehensively updated resource that aggregates missense variant evidence across homologous human protein domains with GRCh38 support, significantly expanding domain coverage and providing critical pathogenic evidence to reclassify variants of uncertain significance in clinical genetics.
scPyviewer is a Python-native, web-based interactive viewer that enables non-programmers to explore AnnData single-cell datasets directly without Seurat conversion, offering feature parity with existing R Shiny tools while demonstrating superior rendering speed, lower memory usage, and the ability to handle large-scale datasets that cause R-based alternatives to fail.
The paper introduces C-PLANK, a deep learning framework trained on cellular chemoproteomics data that outperforms existing models in predicting fragment-protein interactions and successfully identified a novel SIRT3 agonist by integrating physicochemical embeddings with a systems-level biological plausibility metric.
RegimeFormer is a large-scale protein model that, through its associated RegimeAtlas database of over 200 million sequences, establishes global perturbation regimes to predict residue-level mutation effects and improve downstream biological modeling, particularly for unseen proteins and families.
This paper introduces the Dynamic Hierarchical Interleaved Bloom Filter, a scalable and updatable indexing structure that extends the state-of-the-art HIBF with partial rebuilding to enable efficient large-scale sequence search, demonstrating the ability to index over 100 TB of RNA-Seq data and insert new samples 24 to 65 times faster than competing tools.
Vipsania is the first unsupervised deep learning tool that accurately predicts protein-coding gene structures across diverse eukaryotic genomes by learning directly from unannotated sequences, thereby overcoming the limitations of supervised methods that require high-quality training data and struggle with distant or basal clades.
PhageTransformer is a deep learning model that overcomes the limitations of existing tools by providing scalable, accurate, and rapid host predictions for bacteriophages, as demonstrated by its superior performance on thousands of independent phage-host pairs.
The paper introduces morphOTU, an AI-driven framework that derives operational biodiversity units directly from specimen images using self-supervised learning and hierarchical clustering, enabling accurate quantification of species diversity and discovery of morphological patterns even in the absence of formal taxonomic labels or extensive training data.