molpathR

molpathR is a unified molecular pathology data platform that ingests heterogeneous clinical and genomic data sources (VCF, BAM, FASTQ, XML reports, PDF reports, clinical information systems, survival data), builds a queryable in-memory database, and provides an interactive Shiny application for clinical exploration and visualization.

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("cttir/molpathR")

Quick example

library(molpathR)

# Load example database with synthetic data
db <- mp_example_db(n_patients = 50, seed = 42)
db
#> 
#> ── molpath_db ──────────────────────────────────────────────────────────────────
#> ℹ patients: 50 records x 5 columns
#> ℹ samples: 116 records x 5 columns
#> ℹ variants: 2151 records x 10 columns
#> ℹ reports: 116 records x 5 columns
#> ℹ clinical: 195 records x 5 columns
#> ℹ survival: 50 records x 5 columns
#> ℹ Sample date range: 2021-04-01 to 2025-06-26
#> ℹ Overall completeness: "93.7%"
#> ℹ Created: "2026-06-22 13:47:32"
#> ℹ Source files: 0

# Query pathogenic TP53 variants
tp53 <- mp_query_variants(db, genes = "TP53", classification = "Pathogenic")
head(tp53[, c("sample_id", "gene", "variant", "classification", "vaf")])
#> # A tibble: 6 × 5
#>   sample_id     gene  variant             classification   vaf
#>   <chr>         <chr> <chr>               <chr>          <dbl>
#> 1 SAM-2021-0010 TP53  TP53 deletion       Pathogenic     0.420
#> 2 SAM-2021-0011 TP53  TP53-UNKNOWN fusion Pathogenic     0.464
#> 3 SAM-2022-0013 TP53  TP53-UNKNOWN fusion Pathogenic     0.322
#> 4 SAM-2021-0017 TP53  TP53 loss           Pathogenic     0.214
#> 5 SAM-2022-0018 TP53  p.R282W             Pathogenic     0.250
#> 6 SAM-2023-0020 TP53  p.R248W             Pathogenic     0.213

# Survival analysis by diagnosis
mp_plot_survival(db, group_by = "diagnosis", type = "os")

Launch the Shiny app

mp_run_app(db)

Features

• Parsers for VCF, FASTQ, BAM, XML reports, PDF reports, clinical systems, and survival data
• Relational in-memory database linking patients, samples, variants, reports, clinical, and survival data
• Query engine with tidy evaluation and free-text search
• Publication-ready plots: variant landscapes, mutation spectra, survival curves, cohort overviews
• Interactive Shiny application with 6 tabs for clinical exploration

Documentation

• Getting Started
• Data Import Guide
• Visualization Guide
• Shiny Application

License

MIT License. See LICENSE.md for details.

Citation

If you use this software, please cite it as:

Heller, R. (2026). molpathR: Molecular pathology data platform for clinical NGS integration (Version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21889946

DOI: 10.5281/zenodo.21889946

Use of LLM tools

Where applicable, LLM tools were used for narrowly defined, non-authorial tasks: copyediting, prose refinement, Markdown/LaTeX formatting, boilerplate scaffolding (such as CI configurations and build scripts), and code refactoring. Tools included Chat AI, the KISSKI (GWDG) service, and self-hosted Mistral Small (24B, Apache-2.0) served through a local vLLM architecture on an Ubuntu Linux workstation with Intel processors and NVIDIA GPUs. The self-hosted model ran locally, with no data sent to third parties.

All scientific claims, methodological choices, analyses, interpretations and conclusions remain the authors' own. Where applicable, LLM-generated material must be reviewed and revised before inclusion, and references must be verified against their DOI, arXiv ID or ISBN.

Local software setup: vLLM with Python/PyTorch, plus R and RStudio.