857 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 analyzing naturally occurring doublets in single-cell RNA sequencing data, this study reveals that physical T cell-macrophage interactions in ovarian cancer drive therapeutic resistance through M2-polarized macrophage-induced T cell exhaustion, whereas M1-polarized interactions in sensitive patients support effective antigen presentation without exhaustion.
This paper proposes and validates a formalized, phase-gated scientific protocol for language models that decomposes research into procedural, integrity, and governance layers, demonstrating through six end-to-end projects that such constraints enable AI agents to produce rigorous, evidence-backed, and auditable scientific outputs across diverse domains while mitigating integrity risks compared to unconstrained approaches.
PopGenAgent is a turnkey, report-oriented system that integrates curated population-genomics toolchains with validated execution templates and provenance-aware reporting to significantly reduce manual scripting overhead while ensuring reproducibility and step-level inspectability across diverse analyses.
MiGenPro is an interoperable, FAIR-compliant linked data workflow that integrates phenotypic and genomic information to train and validate robust machine learning models for predicting diverse microbial traits from annotated genomes.
MetaTree is an open-source, installation-free web platform designed to facilitate interactive visualization and statistical comparison of hierarchical quantitative data, such as microbiome profiles, by anchoring multiple samples and groups to a shared reference hierarchy to enable topology-consistent, publication-ready analysis.
STCS is a platform-agnostic, reference-free framework that reconstructs coherent single-cell transcriptomes from high-density spatial sequencing data by integrating nuclei segmentation with a joint transcriptomic-spatial distance model, thereby overcoming the fundamental bottleneck of cell-level analysis in technologies like Visium HD and Stereo-seq.
snputils is a high-performance, open-source Python library designed to unify the efficient I/O, transformation, and statistical analysis of genomic and population genetic data within a single, reproducible framework that addresses the limitations of existing fragmented tools.
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.