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.
The paper introduces wavess 1.2, an enhanced within-host virus sequence simulation framework that incorporates HLA-aware cytotoxic T-lymphocyte responses and variable recombination rates to more accurately model immune-driven viral evolution and diversification.
This study presents the first large-scale analysis of chemical probe literature, revealing that these tools provide crucial early evidence for target-disease associations, uncover novel therapeutic opportunities in underexplored diseases, and strengthen validation beyond correlative data.
This study demonstrates that applying post-hoc explanation tools like SHAP to scarce biological image datasets can reveal hidden biases, such as models overfitting to individual animal characteristics rather than biological patterns, while simultaneously uncovering how to reframe tasks to extract valid biological insights from limited data.
This study introduces a multi-modal framework incorporating a novel Directional Non-negative Matrix Factorization (dNMF) model to demonstrate that genome-wide short tandem repeat (STR) variations offer substantially finer and more biologically interpretable resolution for inferring human population structure compared to traditional single-nucleotide polymorphisms (SNPs).
ProteinConformers is a comprehensive resource that provides 2.7 million geometry-optimized protein conformations with extensive energetic and similarity annotations, addressing critical gaps in conformational coverage, energy profiling, and benchmarking standards for protein dynamics and drug discovery.
This study defines the cell-specific localization of angiotensin receptors in the human lung, identifying AGTR1 as a selective marker for pericytes that regulates their migration and proliferation, with its dysregulation linked to emphysema and aging, thereby offering a new framework for targeting airspace resilience in lung disorders.
This study identifies two distinct topological motifs for inversion bubbles in pangenome graphs and develops a tool to annotate them, revealing that current state-of-the-art pipelines often misrepresent or fail to recover inversions, particularly in real human datasets.
This study demonstrates that DNA foundation models, particularly HyenaDNA, outperform specialized methods in accurately and efficiently predicting transcription factor binding sites across plant genomes using DAP-seq data from *Arabidopsis thaliana* and *Sisymbrium irio*.
This study demonstrates that the quality of both molecular and image data, particularly regarding sparsity, noise, and resolution, is a critical determinant of deep learning performance in predicting spatial transcriptomics from histology images, suggesting that improving data quality offers an essential, orthogonal strategy to model architecture optimization.
This study demonstrates that numerical variability in neuroimaging pipelines can significantly distort structural MRI findings in Parkinson's disease research, prompting the development of a practical framework to quantify and mitigate these errors to prevent false conclusions in existing literature.