PRISM
PRISM infers the composition of epigenetically distinct tumor subclones from reduced representation bisulfite sequencing (RRBS) DNA methylation patterns to characterize tumor heterogeneity and global DNA methylation reprogramming.
Key Features:
- Methylation pattern-based inference: Utilizes subclonally reprogrammed methylation patterns and dichotomous methylation states (fully methylated vs. unmethylated) within short genomic regions to reveal constituent tumor populations.
- Reference-free analysis: Performs inference without reliance on a reference genome, using methylation pattern signals alone.
- RRBS and Bismark compatibility: Designed for RRBS data and requires mapping results from Bismark (BAM files) as input.
- HMM-based error correction: Incorporates an in silico proofreading step using a DNA methyltransferase 1-like hidden Markov model to correct erroneous methylation patterns.
- Beta-binomial mixture modeling: Models frequencies of dichotomous methylation patterns using a beta-binomial mixture model to obtain sufficient statistics for subclonal abundance.
- Expectation-maximization fitting: Fits the statistical model with an expectation-maximization algorithm to infer subclonal composition and evolutionary relationships.
Scientific Applications:
- Tumor subclone deconvolution: Infers relative abundances of epigenetically distinct subclones from RRBS-derived methylation patterns.
- Study of epigenetic reprogramming: Characterizes global DNA methylation reprogramming in cancers, including myeloid malignancies.
- Epigenetic heterogeneity and evolution: Reconstructs aspects of tumor heterogeneity and evolutionary history from methylation pattern data.
Methodology:
Inputs are BAM files of RRBS reads aligned by Bismark; methylation patterns are identified and corrected using a DNA methyltransferase 1-like hidden Markov model; frequencies of dichotomous methylation patterns in short regions are modeled with a beta-binomial mixture model; the model is fitted using an expectation-maximization algorithm to infer subclonal composition.
Topics
Details
- License:
- MIT
- Programming Languages:
- Python
- Added:
- 11/14/2019
- Last Updated:
- 12/6/2020
Operations
Publications
Lee D, Lee S, Kim S. PRISM: methylation pattern-based, reference-free inference of subclonal makeup. Bioinformatics. 2019;35(14):i520-i529. doi:10.1093/bioinformatics/btz327. PMID:31510697. PMCID:PMC6612819.