Evidence map›Paper›PMID 39633268›Full record

ArticleBMC bioinformatics2024

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.

Ivo C Leist, María Rivas-Torrubia, Marta E Alarcón-Riquelme, Guillermo Barturen, Precisesads Clinical Consortium, Ivo G Gut, Manuel Rueda

Abstract read
In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Bioinformatics advances · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ivo C LeistCentro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028, Barcelona, Spain.
María Rivas-TorrubiaPfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research, Granada, Spain.
Marta E Alarcón-RiquelmePfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research, Granada, Spain.
Guillermo BarturenPfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research, Granada, Spain.
Precisesads Clinical ConsortiumPfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research, Granada, Spain.
Ivo G GutCentro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028, Barcelona, Spain.
Manuel RuedaCentro Nacional de Análisis Genómico, C/Baldiri Reixac 4, 08028, Barcelona, Spain. manuel.rueda@cnag.eu.

Funding

EU/EFPIA Innovative Medicines Initiative Joint Undertaking (PRECISESADS) 115565Innovative Medicines Initiative 2 Joint Undertaking (JU) 831434Spanish Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias and cofunded with ERDF funds PI19/01772Spanish Ministry of Science and Innovation through the Instituto de Salud Carlos III and the 2014-2020 Smart Growth Operating Program, to the EMBL partnership and institutional co-financing with the European Regional Development Fund MINECO/FEDER, BIO2015-71792-P
6 · The paper itself

Abstract

backgroundPhenotypic 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.

resultsHere, 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.

conclusionsPheno-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 .

Indexed as

GenomicsPhenotypeSoftwareComputational BiologyHumansInformation Storage and RetrievalBeacon v2GA4GHGenomicsHealth data modelPhenopacket v2Semantic similarity

Identifiers

PMID39633268
PMCPMC11616229

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.