Aggregating and analysing gene panel data.
PanelAppRex is an R project aimed at aggregating and analysing gene panel data. Our repository integrates data that is used genomic testing and the virtual gene panels. This dataset facilitates research and development by providing insights into disease-gene correlations and enhancing variant classification methodologies.
PanelAppRex skips the hard work by collecting information about genes and diseases into a simple dataset.
All panels are merged into single tables for your use:
- Simplified (Panel ID, Gene)
- Complex (Panel id, Gene, confidence_level, mode_of_inheritance, name, disease_group, disease_sub_group, status)
You can import either the TSV format or Rds format.
Download or clone this repo, then find your preferred format in /data/.
The following code is shown in minimal_example.R:
# TSV format
path_data <- "../data"
path_PanelAppData_genes_combined_core <- paste0(path_data, "/PanelAppData_combined_core")
path_PanelAppData_genes_combined_minimal <- paste0(path_data, "/PanelAppData_combined_minimal")
df_core <- read.table(file= paste0(path_PanelAppData_genes_combined_core, ".tsv"), sep = "\t")
df_minimal <- read.table(file= paste0(path_PanelAppData_genes_combined_minimal, ".tsv"), sep = "\t")
# Rds format
path_data <- "../data"
path_PanelAppData_genes_combined_Rds <- paste0(path_data, "/PanelAppData_genes_combined_Rds")
df_core <- readRDS(file= path_PanelAppData_genes_combined_Rds)
Simplified (Panel ID, Gene)
$ head PanelAppData_combined_minimal.tsv
id Gene SYMBOL
1 ABL1 ABL1
1 ACTA2 ACTA2
1 ADAMTSL4 ADAMTSL4
1 ARIH1 ARIH1
1 BGN BGN
1 COL1A1 COL1A1
1 COL1A2 COL1A2
1 COL3A1 COL3A1
1 COL5A1 COL5A1
Complex (Panel id, Gene, confidence_level, mode_of_inheritance, name, disease_group, disease_sub_group, status)
$ head ./PanelAppData_combined_core.tsv
id Gene confidence_level mode_of_inheritance name disease_group disease_sub_group status
1 ABL1 3 MONOALLELIC, autosomal or pseudoautosomal, NOT imprinted Thoracic aortic aneurysm or dissection Cardiovascular disorders Connective tissue disorders and aortopathies public
1 ACTA2 3 MONOALLELIC, autosomal or pseudoautosomal, NOT imprinted Thoracic aortic aneurysm or dissection Cardiovascular disorders Connective tissue disorders and aortopathies public
1 ADAMTSL4 3 BIALLELIC, autosomal or pseudoautosomal Thoracic aortic aneurysm or dissection Cardiovascular disorders Connective tissue disorders and aortopathies public
1 ARIH1 3 MONOALLELIC, autosomal or pseudoautosomal, NOT imprinted Thoracic aortic aneurysm or dissection Cardiovascular disorders Connective tissue disorders and aortopathies public
1 BGN 3 X-LINKED: hemizygous mutation in males, monoallelic mutations in females may cause disease (may be less severe, later onset than males) Thoracic aortic aneurysm or dissection Cardiovascular disorders Connective tissue disorders and aortopathies public
$ head PanelAppData_combined_meta_names.tsv
panel_id name
1 Thoracic aortic aneurysm or dissection
3 Stickler syndrome
5 Currarino triad
6 Familial hypercholesterolaemia
...
The contents of panels themselves vary in number of genes. Some genes are found across many different panels. If you are performing WGS analysis and score a variant due to it being a "disease gene", consider that some genes are repeated in many panels and may be unfairly biased.
An example is a panel of 572 PID genes which are well established as consensus in the community. Another example is a panel with 1675 genes which are associated with unexplained death in infancy and sudden unexplained death in childhood.
PanelAppRex is made with care but is produced for research. Clinical applications should refer to accredited clinical sources. A widely used source of disease-gene panels is the Genomics England PanelApp, accessible here. PanelApp hosts comprehensive gene panels related to genomic tests covered by the NHS, as well as data from historic genomic projects.
The goal of PanelAppRex is to prepare a single dataset that is flexible and instantly usable for human genetic analysis.
Contributions to PanelAppRex are welcome. Please submit a pull request with your updates.
The PanelAppRex core model contained 58,592 entries containing annotation fields, including the gene name, disease-gene panel ID, disease-related features, confidence measurements. Data from gnomAD v4 comprised 807,162 individuals, including 730,947 exomes and 76,215 genomes. This dataset provided 786,500,648 single nucleotide variants and 122,583,462 indels, with variant type counts of 9,643,254 synonymous, 16,412,219 missense, 726,924 nonsense, 1,186,588 frameshift and 542,514 canonical splice site variants. ClinVar data were obtained from the variant summary dataset available from the NCBI FTP site, and included 6,845,091 entries, which were processed into 91,319 gene classification groups and a total of 38,983 gene classifications. Data from Ensembl was sourced for validation of identifiers such as gene IDs and Human Genome Organisation Gene Nomenclature Committee (HGNC) symbols. Disease interactions were compared against GE’s PanelApp.
ClinVar asks its users who distribute or copy data to provide attribution to them as a data source in publications and websites. The use of data from UniProt was based on Creative Commons Attribution 4.0 International (CC BY 4.0). We acknowledge GE for providing publicly accessible PanelApp data. The use of data from GE PanelApp was for research purposes and is not for healthcare or commercial services. The use of GE PanelApp source code was under the Apache License 2.0. The experimental (RAG) resource includes machine-assisted annotations generated using the OpenAI API according to their terms of use. We do not access OMIM data but instead direct readers using gene identifiers linked to OMIM.org record pages for further reading and acknowledge this resource. Ensembl data and code are available without restriction and software is provided under the Apache License 2.0. Ensembl and its satellite sites conform to the EBI terms of use; as such we acknowledge attribution to EMBL-EBI for the use of its data resources and tools.
Quant Group, Simon Boutry, Ali Saadat, Sinisa Savic, Luregn J Schlapbach, Jacques Fellay, Dylan Lawless, PanelAppRex aggregates disease gene panels and facilitates sophisticated search, Bioinformatics Advances, 2026, vbag115, https://doi.org/10.1093/bioadv/vbag115.
This project is licensed under the MIT License - see the LICENSE.md file for details.