850 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 scDiagnostics, an open-source R package designed to systematically detect complex or misleading cell type annotations in single-cell transcriptomics data, thereby addressing a critical gap in current analysis workflows by ensuring the reliability of downstream interpretations.
The paper introduces **onsite**, an open-source Python framework that integrates an alanine-decoy strategy to standardize false localization rate estimation across multiple phosphosite localization algorithms, demonstrating superior scalability and accuracy on large-scale mass spectrometry datasets.
PKProbDesign is a novel sampling-based framework that addresses the challenge of RNA inverse folding for pseudoknotted structures by directly optimizing thermodynamic folding probabilities through scaffold decomposition and conditional ensemble evaluation, outperforming existing methods like DesiRNA and MODENA on benchmark targets.
By integrating single-cell and spatial transcriptomics with mitochondrial variant-based lineage tracing, this study reveals that osteosarcoma progression involves a stepwise transcriptional cascade from COL3A1 progenitors to metastatic THY1 cells, a fate switch strictly licensed by spatial co-localization with dysfunctional PLVAP endothelia to form a PTN/NOTCH-enriched premetastatic niche, thereby establishing a "time-space-lineage" framework where microenvironmental signals drive irreversible clonal commitment.
This paper introduces ProtAug, a framework for pLM-guided protein sequence data augmentation that systematically demonstrates how user-controlled variation levels can consistently improve downstream prediction performance across diverse tasks, revealing that while preserving biological plausibility is beneficial at low-to-moderate variation, high-variation augmentation can also drive gains through regularization effects.
This paper introduces a novel, scalable, and globally accessible core genome MLST and LIN coding scheme for *Streptococcus pyogenes*, hosted by PubMLST, to enhance the detection of outbreaks and facilitate international collaboration in tracking genetic variants.
The paper introduces GBZ-base and GAF-base, SQLite-backed indexed file formats that enable efficient, disk-based extraction of local pangenome subgraphs and their corresponding alignments, addressing the limitations of existing batch-processing formats for interactive applications.
Panomap is the first signal-space mapper that utilizes pangenome variation graphs to eliminate reference bias and improve mapping accuracy for diverse nanopore samples while maintaining a compact, scalable index.
This paper introduces a continuous-time protein evolution model parameterized by a generative Potts model to simulate epistatic effects, revealing that while epistasis slows overall evolution and causes systematic underestimation of evolutionary distances, it does not alter average site-specific rates due to context-dependent rate heterogeneity.
This study demonstrates that refining plasma spectral libraries by excluding non-representative peptides significantly enhances the precision, accuracy, and computational efficiency of data-independent acquisition (DIA) proteomics relative quantification without compromising protein identification.