VPMBench - A test bench for variant prioritization methods
VPMBench automates the evaluation of variant prioritization methods by using a pipeline in which the methods are integrated as plugins. Thus, you can include new methods as plugins without changing the pipeline code. Stop reinventing the wheel by developing a bunch of R/Python/Bash scripts to evaluate your prioritization method and use VPMBench to focus your time on compiling suitable evaluation input data and interpreting the results.
Look how easy it is to use:
from vpmbench.api import run_pipeline from vpmbench.logging import enable_logging from vpmbench.metrics import Specificity, Sensitivity from vpmbench.summaries import ROCCurve, ConfusionMatrix from vpmbench.utils import plot_roc_curves, report_metrics, plot_confusion_matrices # Specifying pipeline inputs all_plugins = lambda plugin: True evaluation_input_data = "ClinVar_GRCh37_500benign_500pathogenic.vcf" summaries = [ConfusionMatrix, ROCCurve] metrics = [Specificity, Sensitivity] enable_logging() # Run the vpmbench pipeline report = run_pipeline(with_data=evaluation_input_data, reporting=summaries + metrics, using=all_plugins) # Plot the summaries and report the metrics plot_confusion_matrices(report) plot_roc_curves(report) report_metrics(report)
Supported input formats: Clinvar-VCF, VariSNP
Integration of new prioritization methods as Plugins via Docker or Python
Prebuild plugins for fathmm-MKL and CADD
Automatic calculation of performance summaries (confusion matrices, ROC curves) and metrics (Sensitivity, Specificity)
Easy to extend to support additional input formats, performance summaries, and metrics
Clone the repository and install the VPMBench Python library via:
$ pip install .
Install VPMBench with plugins via the installation script:
If you are having issues, please let me know. You can write me a mail: email@example.com
Copyright (c) 2021, Andreas Ruscheinski
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- VPMBench - A test bench for variant prioritization methods
- Core Concepts
- Experimental Features
- Developer Interface