855 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 paper introduces UnBlender, a pipeline designed to enable respiratory scientists to perform customizable cell type deconvolution on bulk RNA-seq data while routinely validating the accuracy of the estimated cell type proportions to ensure reproducible and reliable conclusions.
This paper presents an interpretable machine learning framework that combines dimensionality reduction with a multi-output random forest model to accurately infer dog breed identity and ancestry from genome-wide SNP data, achieving 91.7% accuracy on the Dog Aging Project dataset while identifying biologically relevant genetic loci associated with breed-specific traits.
This paper introduces ROTS 2.0, an enhanced open-source framework available in R and Python that extends the reproducibility-optimized test statistic to diverse high-throughput omics study designs, including multi-group comparisons and survival analysis, to improve the reliability of feature selection in complex experimental settings.
This paper demonstrates that a correctness-gated coding agent can significantly outperform the established RapidNJ baseline in computational phylogenetics by producing SwiftNJ, an optimized Neighbour Joining implementation that achieves a 0.565 geometric-mean runtime ratio while maintaining exact correctness against reference standards.
Leviathan is an open-source software package that enables ultra-fast, memory-efficient, and accurate taxonomic and functional profiling of metagenomes and metatranscriptomes at genome and pangenome resolution by combining alignment-free taxonomic methods with DNA-space pseudo-alignment to bypass computationally expensive translated-search steps.
This paper presents a minimal, multi-scale computational framework that leverages protein dynamics and recurrent disruption mechanisms to systematically identify and prioritize antimicrobial targets for rational polypharmacology, aiming to enhance therapeutic efficacy while minimizing toxicity and mutational escape.
The paper introduces gTranslate, a computationally efficient machine learning tool that accurately predicts translation tables for prokaryotic genomes without prior taxonomic classification, achieving over 99.99% accuracy and enabling the discovery of novel genetic code variations in specific bacterial lineages.
This study presents a computational framework leveraging the Evo2 DNA foundation model to prioritize noncoding regulatory variants in colorectal cancer by quantifying their impact on promoter sequences, successfully identifying high-impact candidates enriched in cancer-relevant pathways and GWAS loci without relying on supervised training or predefined annotations.
SQANTI-browser is a novel visualization framework that integrates SQANTI3-classified long-read transcriptome data into the UCSC Genome Browser, enabling interactive filtering, evidence-guided curation, and the resolution of alignment artifacts to rescue actionable novel isoforms across diverse datasets.
CARIBOU is a multi-agent AI framework designed for autonomous, iterative, and reproducible bioinformatics analysis within institutional high-performance computing environments, utilizing researcher-editable blueprints and persistent executable states to overcome the limitations of static code generation in processing large-scale single-cell and spatial omics datasets.