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 from explicitly identified records, and provides an interactive Shiny application for exploratory cohort analysis. Raw FASTQ/BAM readers expose sequences and alignments separately; they do not perform variant calling or clinical interpretation. Unknown patient links and annotations remain unknown. The package has not been qualified for diagnostic decision making.
Use R 4.1 or later. On Windows and macOS, install the CRAN binaries from R:
install.packages("pdftools", repos = "https://cloud.r-project.org")
For a source installation of pdftools, install its system library
first:
# Debian or Ubuntu
sudo apt-get install libpoppler-cpp-dev
# macOS with Homebrew, only when building pdftools from source
brew install poppler
See the pdftools installation requirements for other platforms. These commands assume R and the platform’s R compilation toolchain are already installed. Then install the development source:
install.packages("remotes", repos = "https://cloud.r-project.org")
# remotes::install_github("cttir/molpathR")
install.packages("molpathR", repos = c('https://cttir.r-universe.dev', 'https://cloud.r-project.org'))
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: "2026-11-03 12:42:46"
#> 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")
mp_run_app(db)
mp_validate_db(db) reports incomplete patient/sample links, including
missing parent tables. The constructor rejects malformed schemas,
conflicting primary identities, impossible event codes and nonfinite
measures. mp_save_db() verifies a staged file before replacement;
mp_load_db() validates its contents. Resolve reported linkage issues
before interpreting a cohort. Use one writer per saved file.
The data import guide contains a runnable synthetic pathology/clinical/survival import and explains VCF and raw-read boundaries. Optional Bioconductor packages provide BAM/FASTQ readers; strict text FASTQ and VCF paths are available as documented.
Run citation("molpathR") and record the source version or commit used.
This is a development source milestone (0.1.0.9001), not a new release
or Zenodo deposit. The previously advertised draft DOI did not resolve
during the maintenance audit; use the repository citation until a public
deposit is verified.
MIT License. See LICENSE.md for details.
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.
Editable R artwork and regeneration instructions are in tools/branding.