1018 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 SELFormerMM, a multimodal framework that integrates SELFIES, structural graphs, textual descriptions, and knowledge graph data to generate superior molecular representations for drug discovery, outperforming existing unimodal models across various property prediction tasks.
This paper introduces the Super Bloom Filter, a novel variant that combines minimizer-based super-k-mer grouping with the findere scheme to significantly improve cache locality, reduce memory transfers, and minimize false positives for streaming k-mer queries in bioinformatics applications.
PhyloRNA is a curated meta-database that bridges RNA secondary structures with comprehensive phylogenetic annotations and structural descriptors, enabling large-scale comparative and evolutionary analyses of RNA molecules.
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.