Segway 2.0: Gaussian mixture models and minibatch training

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


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 We have made available scripts ( and datasets ( for this paper's analysis.


Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Eukaryota / genetics
  • Genomics / methods*
  • Molecular Sequence Annotation / methods*
  • Sequence Analysis, DNA / methods*
  • Software*