Computer Science and Engineering
 Gothenburg University | Chalmers

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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. The development is hosted at GitHub (http://wiedenhoeft.github.io/HaMMLET/).

Publications

Wiedenhoeft, John and Brugel, Eric and Schliep, Alexander. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression (2016) [details]

Wiedenhoeft, John and Brugel, Eric and Schliep, Alexander. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression (2016) [details]

Wiedenhoeft, John and Schliep, Alexander. Using HaMMLET for Bayesian segmentation of WGS read-depth data (2018) [details]

Bravo, Gustavo A. and Antonelli , Alexandre and Bacon, Christine D. and Bartoszek, Krzysztof and Blom, Mozes P. K. and Huynh , Stella and Jones, Graham and Knowles, L. Lacey and Lamichhaney, Sangeet and Marcussen, Thomas and Morlon, Hélène and Nakhleh, Luay K. and Oxelman, Bengt and Pfeil, Bernard and Schliep, Alexander and Wahlberg, Niklas and Werneck, Fernanda P. and Wiedenhoeft, John and Willows-Munro, Sandi and Edwards, Scott V.. Embracing heterogeneity: building the Tree of Life and the future of phylogenomics (2018) [details]