Registration Open for Bioc2024 July 24-26


This is the development version of MADSEQ; for the stable release version, see MADSEQ.

Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data

Bioconductor version: Development (3.20)

The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample.

Author: Yu Kong, Adam Auton, John Murray Greally

Maintainer: Yu Kong <yu.kong at>

Citation (from within R, enter citation("MADSEQ")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

R Package MADSEQ HTML R Script
Reference Manual PDF


biocViews Bayesian, CopyNumberVariation, Coverage, GenomicVariation, Sequencing, Software, SomaticMutation, VariantDetection
Version 1.31.0
In Bioconductor since BioC 3.4 (R-3.3) (8 years)
License GPL(>=2)
Depends R (>= 3.5.0), rjags (>= 4.6)
Imports VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR
System Requirements
Bug Reports
See More
Suggests knitr
Linking To
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package MADSEQ_1.31.0.tar.gz
Windows Binary
macOS Binary (x86_64) MADSEQ_1.31.0.tgz
macOS Binary (arm64) MADSEQ_1.31.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
Bioc Package Browser
Package Short Url
Package Downloads Report Download Stats