HaMMLET: Dynamically compressed Bayesian Hidden Markov Model using Haar Wavelet Shrinkage

HaMMLET is a powerful open-source implementation of a Bayesian Hidden Markov Model. It uses the Haar wavelet transform to dynamically compress the data, which leads to improved speed and convergence of Forward-Backward Gibbs Sampling. It can be used in applications such as CNV detection from aCGH data.

For further information see the main website at http://wiedenhoeft.github.io/HaMMLET/ or contact John Wiedenhoeft (john.wiedenhoeft@chalmers.se). This software is a result of or used in the following projects: GHMM, BayesianHMM.

Team

Members: John Wiedenhoeft, Alexander Schliep, John Wiedenhoeft, Eric J Brugel.

Publications

Wiedenhoeft et al.. Bayesian localization of CNV candidates in WGS data within minutes. Algorithms for Molecular Biology 2019, 14:20.

Wiedenhoeft. Dynamically Compressed Bayesian Hidden Markov Models using Haar Wavelets. Ph.D. Thesis, Rutgers, The State University of New Jersey, Oct 2018.

Wiedenhoeft et al.. Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data.. Methods Mol Biol 2018, 1833, 83–93.

Wiedenhoeft et al.. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. In Research in Computational Molecular Biology: 20th Annual Conference, RECOMB 2016, Santa Monica, CA, USA, April 17-21, 2016, Proceedings, Springer, 9649, 263, 2016.

Wiedenhoeft et al.. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. PLoS Computational Biology 2016, 12:5, e1004871.

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