1199 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 Genomic Annotation Infrastructure (GAIn) is a platform that enables transparent, reproducible, and scalable genomic variant annotation through declarative pipelines, public resource repositories, and flexible web and command-line interfaces that support custom extensions and automated re-annotation.
This study reveals that in the multimodal protein language model ESM3, distinct physical modalities (sequence, structure, secondary structure, and solvent accessibility) initially occupy separate subspaces before fusing into a shared low-dimensional representation between layers 25 and 35, while functional annotations remain orthogonal throughout, with this fusion process being a learned, universal property independent of protein length but delayed by structural disorder.
ProtBLIP2-SST is a novel two-stage framework that integrates protein sequence and 3D structural information via a structure-aware language model and a Q-Former projector to enable large language models to generate flexible, open-ended functional captions, thereby overcoming the limitations of traditional rigid gene ontology classification.
EcoMorph is a modular system that leverages the prompt-based Segment Anything Model 3 (SAM3) to accurately quantify morphological traits like area, size, and abundance across diverse ecological contexts without requiring taxon-specific training, thereby enabling high-throughput ecological research from heterogeneous image sources.
The paper introduces CellTok, a novel approach that tokenizes continuous single-cell transcriptomic profiles into discrete sequences compatible with pretrained large language models, thereby enabling a unified framework for jointly processing cellular data, biological context, and textual instructions to perform diverse tasks like cell identification, disease inference, and state generation.
The paper introduces scDiagnostics, an open-source R package designed to systematically detect complex or misleading cell type annotations in single-cell transcriptomics data, thereby addressing a critical gap in current analysis workflows by ensuring the reliability of downstream interpretations.
The paper introduces **onsite**, an open-source Python framework that integrates an alanine-decoy strategy to standardize false localization rate estimation across multiple phosphosite localization algorithms, demonstrating superior scalability and accuracy on large-scale mass spectrometry datasets.
PKProbDesign is a novel sampling-based framework that addresses the challenge of RNA inverse folding for pseudoknotted structures by directly optimizing thermodynamic folding probabilities through scaffold decomposition and conditional ensemble evaluation, outperforming existing methods like DesiRNA and MODENA on benchmark targets.
By integrating single-cell and spatial transcriptomics with mitochondrial variant-based lineage tracing, this study reveals that osteosarcoma progression involves a stepwise transcriptional cascade from COL3A1 progenitors to metastatic THY1 cells, a fate switch strictly licensed by spatial co-localization with dysfunctional PLVAP endothelia to form a PTN/NOTCH-enriched premetastatic niche, thereby establishing a "time-space-lineage" framework where microenvironmental signals drive irreversible clonal commitment.
This paper introduces ProtAug, a framework for pLM-guided protein sequence data augmentation that systematically demonstrates how user-controlled variation levels can consistently improve downstream prediction performance across diverse tasks, revealing that while preserving biological plausibility is beneficial at low-to-moderate variation, high-variation augmentation can also drive gains through regularization effects.