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Bioinformatics. 2018 Feb 15;34(4):669-671. doi: 10.1093/bioinformatics/btx603.

Segway 2.0: Gaussian mixture models and minibatch training.

Author information

1
Princess Margaret Cancer Centre, Toronto, ON M5G 1L7, Canada.
2
Engineering Physics Program, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
3
Department of Computer Science and Engineering.
4
Department of Electrical Engineering.
5
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
6
Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada.
7
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada.

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.

PMID:
29028889
PMCID:
PMC5860603
DOI:
10.1093/bioinformatics/btx603
[Indexed for MEDLINE]
Free PMC Article

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