Pheno‑Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond
Metadata
Show full item recordAuthor
C. Leist, Ivo; Rivas Torrubia, María; Alarcón Riquelme, Marta Eugenia; Barturen Briñas, Guillermo; G. Gut, Ivo; Rueda, ManuelEditorial
BMC
Materia
GA4GH Phenopacket v2 Beacon v2
Date
2024-12-04Referencia bibliográfica
C. Leist, I. et. al. BMC Bioinformatics (2024) 25:373. [https://doi.org/10.1186/s12859-024-05993-2]
Sponsorship
Project 3TR ith funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 831434; European Union’s Horizon 2020 research and innovation programme and EFPIA; Spanish Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias and cofunded with ERDF funds (PI19/01772); Spanish Ministry of Science and Innovation through the Instituto de Salud Carlos III and the 2014–2020 Smart Growth Operating Program; European Regional Development Fund (MINECO/FEDER, BIO2015-71792-P); Generalitat de Catalunya through the Departament de Salut and the Departament d’Empresa i Coneixement; EU/EFPIA Innovative Medicines Initiative Joint Undertaking (PRECISESADS, grant n. 115565) including in-kind contributions from the EFPIA members involvedAbstract
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-Biome dical-Infor matics/ pheno-ranker.