Pheno-Ranker:用于比较GA4GH标准和其他标准中存储的表型数据的工具包。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-12-04 DOI:10.1186/s12859-024-05993-2
Ivo C Leist, María Rivas-Torrubia, Marta E Alarcón-Riquelme, Guillermo Barturen, Precisesads Clinical Consortium, Ivo G Gut, Manuel Rueda
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引用次数: 0

摘要

背景:表型数据比较对于疾病关联研究、患者分层和基因型-表型相关分析至关重要。为了支持这些工作,全球基因组学与健康联盟(GA4GH)建立了Phenopackets v2和Beacon v2标准,用于存储、共享和发现基因组和表型数据。这些标准为组织生物数据提供了一致的框架,简化了它们向计算机友好格式的转换。然而,使用基于ga4gh的格式匹配参与者仍然具有挑战性,因为当前的方法不完全兼容,限制了它们的有效性。结果:在这里,我们介绍了Pheno-Ranker,一个用于个体水平表型数据比较的开源软件工具包。作为输入,它接受来自Beacon v2和Phenopackets v2数据模型的JSON/YAML数据交换格式,以及以JSON、YAML或CSV格式编码的任何数据结构。在内部,分层数据结构被平面化到一维,然后通过单热编码进行转换。这允许在队列内进行有效的两两(全部对全部)比较,或在队列中匹配患者的概况。用户可以通过包括或排除术语、对变量应用权重以及通过z分数和p值获得统计显著性来灵活地改进他们的比较。输出由文本文件组成,可以使用无监督学习技术(如聚类或多维缩放(MDS))和图形分析进一步分析文本文件。Pheno-Ranker的性能已经通过模拟和合成数据进行了验证,显示了其在各种健康数据场景下的准确性、稳健性和效率。PRECISESADS研究的一个真实数据用例突出了其在临床研究中的实用性。结论:Pheno-Ranker是一个用户友好的轻量级软件,用于Beacon v2和Phenopackets v2格式表型数据的语义相似性分析,可扩展到其他数据类型。它支持比较HPO或OMIM术语以外的各种变量,同时保留完整的上下文。该软件被设计为一个命令行工具,具有用于CSV导入、数据模拟、汇总统计绘图和QR码生成的附加实用程序。对于交互式分析,它还包括一个基于web的用户界面,它是用R Shiny构建的。在线文档的链接,包括谷歌Colab教程,以及该工具的源代码可以在项目主页上找到:https://github.com/CNAG-Biomedical-Informatics/pheno-ranker。
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Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.

Background: Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype-phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness.

Results: Here, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient's profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker's performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research.

Conclusions: Pheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool's source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker .

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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