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.
This paper reveals that the widespread use of invalid statistical tests ignoring cross-validation fold dependence in biomedical machine learning leads to inflated false positive rates, prompting the authors to propose the SHARP test as a robust solution and provide new reporting guidelines for valid model comparison.
This paper proposes a collision-aware method-of-moments estimator that enables accurate RNA-seq quantification using shorter, non-unique UMIs, thereby reducing sequencing and synthesis costs without compromising biological insights.
GlyComboCLI is a flexible, open-source command-line tool that automates glycan composition assignment for mass spectrometry data, enabling FAIR-compliant, scalable bioinformatic workflows that significantly reduce manual interpretation while ensuring reproducible results.
The paper introduces geneML, a fast and open-source deep learning tool that significantly improves the accuracy, sensitivity, and biological completeness of gene and alternative transcript prediction across diverse fungal genomes compared to existing methods like BRAKER3 and AUGUSTUS.
The paper introduces NMFBatch, a unified statistical framework that simultaneously corrects discrete batch effects and continuous signal drift while directly handling missing values in large-scale and single-cell mass spectrometry proteomics, thereby preserving biological structure and reducing information loss compared to existing methods.
ParaDISM is an open-source pipeline that enhances the precision of short-read alignment and variant calling in highly homologous genomic regions by utilizing multiple sequence alignments to identify disambiguating positions and iteratively refining reference sequences, thereby significantly reducing misalignment artifacts and false variant calls compared to standard aligners.
zFISHer is an open-source, napari-based application that automates the 3D registration, detection, and colocalization analysis of sequential multiplexed FISH data while providing interactive curation tools to overcome the labor-intensive bottlenecks of manual analysis.
OmniCellAgent is a multi-agent AI framework that autonomously retrieves and integrates diverse single-cell RNA sequencing datasets with biomedical prior knowledge to generate evidence-based hypotheses and accelerate omics-driven scientific discovery for non-computational researchers.
By constructing phylogenetic trees from over 100,000 influenza sequences, this study estimates site-specific neutral mutation rates and fitness effects across the viral proteome, revealing significant variation among mutation types, strong cross-viral correlations with SARS-CoV-2 and HIV, and providing a comprehensive, interactive resource for understanding how mutation and selection shape influenza evolution in nature.
The authors developed CharacTERT, a machine learning tool that integrates sequence and structural features to accurately classify hTERT missense variants associated with Telomere Biology Disorders, outperforming existing predictors and providing a comprehensive mutational landscape via a freely accessible web server.