Segway 2.0: Gaussian mixture models and minibatch training.
basic_science · Level V
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- Record sourced from PubMed, PMID 29028889.
- Also identified by DOI 10.1093/bioinformatics/btx603 and PMC identifier 5860603.
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Abstract
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. michael.hoffman@utoronto.ca. Supplementary data are available at Bioinformatics online.
Medical subject headings
- Genomics
- Molecular Sequence Annotation
- Sequence Analysis, DNA
- Software