Princess Margaret Cancer Centre, Toronto, ON M5G 1L7, Canada.
Engineering Physics Program, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.
Bioinformatics. 2018 Feb 15;34(4):669-671. doi: 10.1093/bioinformatics/btx603.
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.
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.
Supplementary data are available at Bioinformatics online.
Segway 执行半自动基因组注释，发现多个基因组信号数据集之间的联合模式。我们讨论了 Segway 的一个主要新版本，并强调了它能够更准确地对数据进行建模的能力。Segway 2.0 的主要增强功能包括能够使用混合高斯模型对数据进行建模，从而能够捕获任意复杂的信号分布，以及使用小批量训练，从而获得更好的学习参数。
Segway 及其源代码可在 http://segway.hoffmanlab.org 免费下载。我们提供了用于本文分析的脚本（https://doi.org/10.5281/zenodo.802939）和数据集（https://doi.org/10.5281/zenodo.802906）。
补充数据可在“Bioinformatics”在线获取。
