1205 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.
RiboBA is a novel bias-aware probabilistic framework that improves the robust identification of non-canonical open reading frames across diverse ribosome profiling protocols by explicitly modeling and correcting for protocol-induced artifacts, thereby achieving superior accuracy and sensitivity compared to existing methods.
This study presents an AI-driven decision-support tool that utilizes respiratory microbiome profiles and machine learning, specifically XGBoost, to accurately triage COVID-19 patients by identifying dysbiotic microbial signatures associated with severe outcomes.
The paper introduces DOTSeq, a comprehensive statistical framework that enables genome-wide detection of differential ORF usage and translation efficiency in both bulk and single-cell ribosome profiling experiments, overcoming the limitations of gene-level analysis to uncover translational control events at the ORF level.
This paper addresses the lack of a standardized definition for "sequences of concern" in nucleic acid synthesis by developing and validating a science-based rubric through stakeholder review and testing on a large dataset, which significantly reduces categorization disputes and provides a concrete foundation for future biosecurity screening standards.
This study evaluates eight differential abundance analysis methods using permutation-based null hypothesis testing across multiple datasets, revealing that while complex compositional and negative binomial models often produce biased p-values, simpler classical tests like the t-test and Wilcoxon test demonstrate superior robustness and reliability.
Ryder is a flexible Python package that normalizes diverse epigenomic sequencing data by leveraging stable internal reference regions to correct technical variability, thereby enhancing the accuracy of genome-wide signal alignment and the detection of genuine biological changes.
This paper introduces HKS, a data structure built on the Spectral Burrows-Wheeler Transform that enables exact, lossless hierarchical annotation of variable-length k-mers by resolving multi-matches through a user-defined category hierarchy and enhancing specificity with a context-aware smoothing algorithm, achieving high accuracy in genomic feature assignment while maintaining performance comparable to existing tools like Kraken2.
The paper introduces HARVEST, an agentic AI pipeline that autonomously extracts 3.36 million previously inaccessible bioactivity records from pharmaceutical patents, revealing significant generalization gaps in current AI models when evaluated on this newly recovered data.
The paper introduces PREMISE, a high-performance, quality-aware probabilistic framework that utilizes alignment-based Expectation-Maximization to overcome the limitations of k-mer methods, enabling accurate identification of viral subtypes, estimation of relative abundances, and detection of complex events like reassortment and recombination in Influenza A viruses from metagenomic sequencing data.
The paper introduces Zigo, a novel machine learning-based method that accurately and efficiently infers genetic sex from standard VCF files using a reference-free, threshold-free polynomial equation derived from X-chromosome genotype distributions, demonstrating robust performance across diverse genomic data types and limited variant sets.