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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
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Katdetectr: an R/bioconductor package utilizing unsupervised changepoint analysis for robust kataegis detection.
Daan M Hazelaar1, Job van Riet1,2, Youri Hoogstrate3
1Department of Medical Oncology, Erasmus MC Cancer Institute, University Medical Center, 3015 GD, Rotterdam, the Netherlands.
Gigascience
|October 17, 2023
Summary
Katdetectr is a new R package for detecting genomic hypermutation regions (kataegis) in cancer. It offers robust, fast, and accurate identification, characterization, and visualization of these significant cancer mutation patterns.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Kataegis describes regional genomic hypermutation in various cancers, characterized by high local mutation rates.
- Identifying kataegis loci is crucial due to their biological significance and potential clinical relevance in malignancies.
Purpose of the Study:
- To introduce Katdetectr, an open-source R/Bioconductor package for detecting kataegis loci in genomic data.
- To provide functionalities for characterizing and visualizing kataegis, facilitating subsequent analyses.
Main Methods:
- Katdetectr processes standard genomic formats (MAF, VCF, VRanges).
- It employs unsupervised changepoint analysis using the Pruned Exact Linear Time (PELT) algorithm to identify mutation hotspots.
- Kataegis calling is performed based on user-defined parameters.
Main Results:
- Katdetectr demonstrated robustness against varying tumor mutational burden.
- It achieved the fastest mean computation time compared to other evaluated tools.
- Katdetectr exhibited superior accuracy (0.99) and Matthews correlation coefficient (0.98) on both synthetic and real cancer datasets.
Conclusions:
- Katdetectr provides a robust, flexible, and fast workflow for kataegis detection, characterization, and visualization.
- The package is available on Bioconductor, offering a valuable tool for cancer genomics research.
