1210 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.
This study systematically benchmarks seven actively maintained Nanopore long-read tandem repeat genotyping tools across accuracy, usability, and diverse genomic contexts, revealing that no single tool is universally superior and that sequence-level evaluation is essential for selecting the right method for population studies and clinical diagnostics.
The paper introduces scUnify, a unified Python framework that streamlines zero-shot inference for single-cell foundation models by automating environment management and preprocessing while enabling scalable, multi-GPU distributed execution and standardized evaluation.
This study evaluates the STUNT meta-learning framework for microbiome-based disease classification and finds that while its self-supervised embeddings offer marginal benefits under extreme data scarcity, they ultimately hinder performance with more samples by creating an information bottleneck that limits access to task-specific signals, suggesting that intrinsic biological signal strength is the primary driver of classification success.
The paper introduces DartUniFrac, a GPU-accelerated algorithm that leverages weighted Jaccard similarity and sketching techniques to enable statistically accurate, near-optimal microbiome analysis at a scale of millions of samples and billions of taxa, achieving speeds up to three orders of magnitude faster than existing methods.
RankMap is a fast and robust R package that utilizes rank-based gene expression representations and elastic net-regularized multinomial regression to provide scalable, accurate cell type annotation for both single-cell and spatial transcriptomics datasets, outperforming existing methods in efficiency and handling platform-specific biases.
This paper introduces a deep representation learning framework that integrates foundation models for transcriptomics and pathology to robustly align multi-modal spatial transcriptomics data across diverse patient cohorts, significantly outperforming conventional batch-correction methods in clustering cells by type rather than dataset-specific conditions.
This study demonstrates that while state-of-the-art AI methods can generate geometrically plausible antibody-antigen structures, their internal confidence scores fail to reliably distinguish correct binding pairs from incorrect ones, highlighting a critical need for explicit negative controls and realistic benchmarking in therapeutic discovery.
This paper demonstrates that training sparse autoencoders on hidden representations of single-cell foundation models reveals interpretable biological and technical features, enabling targeted interventions to reduce technical artifacts while preserving core biological signals.
This paper introduces AlterNet, a novel pipeline that infers gene regulatory networks at the transcript level by treating transcription factor isoforms as distinct regulators, thereby uncovering biologically relevant interactions in heart tissue that are missed by conventional gene-level methods.
The paper introduces ETSAM, a fine-tuned two-stage SAM2-based deep learning model trained on diverse experimental and simulated cryo-ET data that achieves state-of-the-art, robust segmentation of cell membranes despite the technique's inherent low signal-to-noise ratio and missing wedge artifacts.