PT  - JOURNAL ARTICLE
AU  - Rachel C.W. Chan
AU  - Maxwell W. Libbrecht
AU  - Eric G. Roberts
AU  - William Stafford Noble
AU  - Michael M. Hoffman
TI  - Segway 2.0: Gaussian mixture models and minibatch training
AID  - 10.1101/147470
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 147470
4099  - http://biorxiv.org/content/early/2017/06/08/147470.short
4100  - http://biorxiv.org/content/early/2017/06/08/147470.full
AB  - Summary Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.Availability and Implementation Segway and its source code are freely available for download at https://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802940) and datasets (https://doi.org/10.5281/zenodo.802907) for this paper’s analysis.Contact michael.hoffman{at}utoronto.ca