1210 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 study benchmarks seven variant callers on simulated *Mycobacterium tuberculosis* data to identify FreeBayes as the most effective tool for detecting low-frequency variants and introduces a new error model that significantly reduces false positives while preserving true variants.
Clair3 v2 is a computationally efficient variant calling method that leverages the lightweight ONT move table and dwelling time information to significantly improve SNP and Indel accuracy, particularly in complex genomic regions, while maintaining negligible runtime overhead compared to existing signal-aware approaches.
IntelliFold-2 is an open-source biomolecular structure prediction model that surpasses AlphaFold 3 in accuracy and robustness—particularly for antibody-antigen and protein-ligand interactions—by introducing architectural refinements such as latent space scaling, stochastic atomization, and policy-guided diffusion optimization.
This paper introduces the CellLink corpus, a manually annotated collection of over 22,000 cell population mentions from biomedical literature that enables systematic analysis of naming patterns, improves named entity recognition and linking via machine learning, and facilitates the expansion and refinement of the Cell Ontology.
This study presents a hybrid machine learning framework that successfully identified and experimentally validated two small molecules, berberrubine and PE859, as effective inhibitors of TDP-43 aggregation with therapeutic potential for neurodegenerative diseases.
CodonRL is a demonstration-guided reinforcement learning framework that overcomes the challenges of large search spaces and delayed rewards in multi-objective codon optimization, outperforming state-of-the-art methods like GEMORNA by significantly improving translation efficiency, RNA stability, and compositional properties across human proteins.
This study developed a high-performing machine learning model that predicts *Mycobacterium tuberculosis* bedaquiline resistance phenotypes associated with Rv0678 variants by integrating five key sequence, biochemical, and structural features, offering a potential tool to guide clinical management of rifampicin-resistant tuberculosis.
Theseus is a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm that leverages diagonal transitions and sparse data strategies to significantly outperform existing methods in speed while maintaining optimality for applications like multiple sequence alignment and pangenome read mapping.
GeneReL is an integrated platform that leverages a tiered large language model pipeline and a community-driven curation system to extract and validate high-confidence gene regulatory interactions in *Arabidopsis thaliana* from scientific literature.
This study demonstrates that cigarette smoke increases colorectal cancer risk by inducing gut microbiota dysbiosis and metabolic changes, which in turn suppresses key tumor suppressor genes like *CPT2* to promote tumor development.