Bioinformatics (Oxford, England)最新文献

Summary: Uchimata is a toolkit for visualization of 3D structures of genomes. It consists of two packages: a Javascript library facilitating the rendering of 3D models of genomes, and a Python widget for visualization in Jupyter Notebooks. Main features include an expressive way to specify visual encodings, and filtering of 3D genome structures based on genomic semantics and spatial aspects. Uchimata is designed to be highly integratable with biological tooling available in Python.

Availability and implementation: Uchimata is released under the MIT License. The Javascript library is available on NPM, while the widget is available as a Python package hosted on PyPI. The source code for both is available publicly on Github (https://github.com/hms-dbmi/uchimata and https://github.com/hms-dbmi/uchimata-py) and Zenodo (https://doi.org/10.5281/zenodo.17831959 and https://doi.org/10.5281/zenodo.17832045). The documentation with examples is hosted at https://hms-dbmi.github.io/uchimata/.

Availability and implementation: PG image and source code are available in github and dockerhub under LGPL-2.1.

Motivation: Identifying pairwise associations between genomic loci is an important challenge for which large and diverse collections of epigenomic and transcription factor (TF) binding data can potentially be informative.

Results: We developed Learning Evidence of Pairwise Association from Epigenomic and TF binding data (LEPAE). LEPAE uses neural networks to quantify evidence of association for pairs of genomic windows from large-scale epigenomic and TF binding data along with distance information. We applied LEPAE using thousands of human datasets. We show using additional data that LEPAE captures biologically meaningful pairwise relationships between genomic loci, and we expect LEPAE scores to be a resource.

Availability and implementation: The LEPAE scores and the software are available at https://github.com/ernstlab/LEPAE.

Motivation: Live-cell imaging-based drug screening increases the likelihood of identifying effective and safe drugs by providing dynamic, high-content, and physiologically relevant data. As a result, it improves the success rate of drug development and facilitates the translation of benchside discoveries to bedside applications. Despite these advantages, no comprehensive metrics currently exist to evaluate dose-time-dependent drug responses. To address this gap, we established a systematic framework to assess drug effects across a range of concentrations and exposure durations simultaneously. This metric enables more accurate evaluation of drug responses measured by live-cell imaging.

Results: We employed treatment concentrations ranging from 0 to 10 μM and performed live-cell imaging-based measurements over a 120-h incubation period. To analyze the experimental data, we developed VUScope, a new mathematical model combining the 4-parameter logistic curve and a logistic function to characterize dose-time-dependent responses. This enabled us to calculate the Growth Rate Inhibition Volume Under the dose-time-response Surface (GRIVUS), which serves as a critical metric for assessing dynamic drug responses. Furthermore, our mathematical model allowed us to predict long-term treatment responses based on short-term drug responses. We validated the predictive capabilities of our model using independent datasets and observed that VUScope enhances prediction accuracy and offers deeper insights into drug effects than previously possible. By integrating VUScope into high-throughput drug screening platforms, we can further improve the efficacy of drug development and treatment selection.

Availability and implementation: We have made VUScope more accessible to users conducting pharmacological studies by uploading a detailed description, example datasets, and the source code to vuscope.albi.hhu.de, https://github.com/AlBi-HHU/VUScope, and https://doi.org/10.5281/zenodo.17610533.

Motivation: Knowledge graphs (KGs) are powerful tools for structuring and analyzing biological information due to their ability to represent data and improve queries across heterogeneous datasets. However, constructing KGs from unstructured literature remains challenging due to the cost and expertise required for manual curation. Prior works have explored text-mining techniques to automate this process, but have limitations that impact their ability to capture complex relationships fully. Traditional text-mining methods struggle with understanding context across sentences. Additionally, these methods lack expert-level background knowledge, making it difficult to infer relationships that require awareness of concepts indirectly described in the text. Large Language Models (LLMs) present an opportunity to overcome these challenges. LLMs are trained on diverse literature, equipping them with contextual knowledge that enables more accurate information extraction.

Results: We present textToKnowledgeGraph, an artificial intelligence tool using LLMs to extract interactions from individual publications directly in Biological Expression Language (BEL). BEL was chosen for its compact, detailed representation of biological relationships, enabling structured, computationally accessible encoding. This work makes several contributions. (i) Development of the open-source Python textToKnowledgeGraph package (pypi.org/project/texttoknowledgegraph) for BEL extraction from scientific articles, usable from the command line and within other projects, (ii) an interactive application within Cytoscape Web to simplify extraction and exploration, (iii) a dataset of extractions that have been both computationally and manually reviewed to support future fine-tuning efforts.

Availability and implementation: https://github.com/ndexbio/llm-text-to-knowledge-graph.

Motivation: Dendritic spines, postsynaptic structures characterized by their complex shapes, provide the essential structural foundation for synaptic function. Their shape is dynamic, undergoing alterations in various conditions, notably during neurodegenerative disorders like Alzheimer's disease. The dramatically increasing prevalence of such diseases highlights an urgent need for effective treatments. A key strategy in developing these treatments involves evaluating how dendritic spine morphology responds to potential therapeutic compounds. Although a link between spine shape and function is recognized, its precise nature is still not fully elucidated. Consequently, advancing our understanding of dendritic spines in both health and disease necessitates the urgent development of more effective methods for assessing their morphology.

Results: This study introduces qualitatively new 3D dendritic shape descriptors based on spherical harmonics and Zernike moments and proposes a bases on them clustering approach for grouping dendritic spines with similar shapes applied to 3D polygonal spines meshes acquired from Z-stack dendrite images. By integrating these methods, we achieve improved differentiation between normal and pathological spines represented by the Alzheimer's disease in vitro model, offering a more precise representation of morphological diversity. Additionally, the proposed spherical harmonics approach enables dendritic spine reconstruction from vector-based shape representations, providing a novel tool for studying structural changes associated with neurodegeneration and possibilities for synthetic dendritic spines dataset generation.

Availability and implementation: The software used for experiments is public and available at https://github.com/Biomed-imaging-lab/SpineTool with the DOI: 10.5281/zenodo.17359066. Descriptors codebase is available at https://github.com/Biomed-imaging-lab/Spine-Shape-Descriptors with the DOI: 10.5281/zenodo.17302859.

Motivation: Capturing spatial structure is fundamental to the analysis of spatial transcriptomics data. However, most existing methods focus on clustering within individual tissue slices and often ignore the high inter-slice similarity inherent in multi-slice datasets.

Results: To address this limitation, we propose STransfer, a novel transfer learning framework that combines graph convolutional networks (GCNs) with positive pointwise mutual information (PPMI) to model both local and global spatial dependencies. An attention-based module is introduced to fuse features from multiple graphs into unified node representations, facilitating the learning of low-dimensional embeddings that jointly encode gene expression and spatial context. By transferring knowledge from labeled slices to adjacent unlabeled ones, STransfer significantly enhances clustering accuracy while reducing manual annotation costs. Extensive experiments demonstrate that STransfer consistently outperforms state-of-the-art methods in both spatial modeling and cross-slice transfer performance.

Availability and implementation: The code for STransfer has been uploaded to GitHub: https://github.com/Saki-JSU/Publications/tree/main/STransfer.

Drug repositioning offers a cost-effective alternative to traditional drug development by identifying new uses for existing drugs. Recent advances leverage Graph Neural Networks (GNNs) to model complex biological data, showing promise in predicting novel drug-disease associations; however, these frameworks often lack explainability, a critical factor for validating predictions and understanding drug mechanisms. Here, we introduce Drug-Based Reasoning Explainer (DBR-X), an explainable GNN model that integrates a link-prediction module with a path-identification module to generate interpretable and faithful explanations. When benchmarked against other GNN-based link-prediction frameworks, DBR-X achieves superior performance in identifying known drug-disease associations, demonstrating higher accuracy across all evaluation metrics. The quality of DBR-X biological explanations was evaluated through multiple complementary approaches, including comparison with manually curated drug mechanisms, assessment of explanation faithfulness using deletion and insertion studies, and measurement of stability under graph perturbations. Together, these results show that DBR-X advances the state of the art in drug repositioning while providing multi-hop mechanistic explanations that can facilitate the translation of computational predictions into clinical applications. Availability and implementation: DBR-X package is freely accessible from online repository https://github.com/SuLab/DBR-X.

Summary: DNAvi is a Python-based tool for rapid grouped analysis and visualization of cell-free DNA fragment size profiles directly from electrophoresis data, overcoming the need for sequencing in basic fragmentomic screenings. It enables normalization, statistical comparison, and publication-ready plotting of multiple samples, supporting quality control and exploratory fragmentomics in clinical and research workflows.

Availability and implementation: DNAvi is implemented in Python and freely available on GitHub at https://github.com/anjahess/DNAvi under a GNU General Public License v3.0, along with source code, documentation, and examples. An archived version is available under https://doi.org/10.5281/zenodo.18401705.

Motivation: The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines represent the gold standard for clinical variant interpretation. Despite the widespread adoption of ACMG/AMP guidelines, a comprehensive comparison of the software tools designed to implement them has been lacking. This represents a significant gap, as clinicians require evidence-based guidance on which tools to use in their practice.

Results: We benchmarked four ACMG/AMP-based tools (Franklin, InterVar, TAPES, Genebe) selected from 22 tools, and compared their performance with LIRICAL, a top-performing phenotype-driven tool, using 151 expert-curated datasets from Mendelian disorders. Selection criteria included free availability, VCF compatibility, operational reliability, and not being disease-specific. Our evaluation framework assessed top-N accuracy (N = 1, 5, 10, 20, 50), retention rates, precision, recall, F1 scores, and area under the curve (AUC). Statistical validation employed bootstrap confidence intervals (n = 1000) and Friedman tests. LIRICAL (68.21%) and Franklin (61.59%) demonstrated superior top-10 variant prioritization accuracy in Mendelian disorders, significantly outperforming other tools (P = .0000). Results demonstrate that tools with advanced phenotypic integration significantly outperform those relying primarily on genomic features.

Availability and implementation: All data and source code required to reproduce the findings of this study are openly available in the Code Ocean repository at https://doi.org/10.24433/CO.6562438.v1.