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Segway 2.0: Gaussian mixture models and minibatch training.

ABSTRACT: 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 http://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802939) and datasets (https://doi.org/10.5281/zenodo.802906) for this paper's analysis. Contact:michael.hoffman@utoronto.ca. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Chan RCW

PROVIDER: S-EPMC5860603 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Segway 2.0: Gaussian mixture models and minibatch training.

Chan Rachel C W RCW Libbrecht Maxwell W MW Roberts Eric G EG Bilmes Jeffrey A JA Noble William Stafford WS Hoffman Michael M MM

Bioinformatics (Oxford, England) 20180201 4

<h4>Summary</h4>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.<h4>Availability and implemen ...[more]

PMID: 29028889

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