ArrayCGH: Analyzing comparative genomic hybridization data

Detecting Chromosomal aberrations from ArrayCGH and gene expression ArrayCGH experimental data Chromosomal aberrations such as deletions or duplications of chromosomal regions are a crucial contributing factor to cancer. The aberrations can be detected by observing the relative hybridization intensities of healthy vs. diseased patients for BAC-clones covering complete genomes. A Hidden Markov Model with a inhomogeneous Markov Chain allows to reflect dependencies between overlapping clones.

For further information contact Alexander Schliep ( This project is connected to the following projects: GHMM.


Members: Alexander Schliep, Michael Seifert. Collaborators: Wei Chen (Max-Planck-Institut for Molecular Genetics, Human Molecular Genetics).


Wiedenhoeft et al.. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. biorXiv 2015.

Seifert et al.. Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models. Bioinformatics 2011, 27:12, 1645–1652.

Seifert. Analyzing Microarray Data Using Homogenous and Inhomogenous Hidden Markov Models. Master's Thesis, Martin-Luther-Universität Halle, 2006.