Semi-supervised segmentation and genome annotation

Chan, R. C. W.; McNeil, M.; Roberts, E. G.; Mendez, M.; Libbrecht, M. W.; Hoffman, M. M.

doi:10.1101/2020.01.30.926923

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bioRxiv · 10.1101/2020.01.30.926923

Semi-supervised segmentation and genome annotation

Chan, R. C. W.·McNeil, M.·Roberts, E. G.·Mendez, M.·Libbrecht, M. W.·Hoffman, M. M.

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Abstract

Segmentation and genome annotation methods automatically discover joint signal patterns in whole genome datasets. Previously, researchers trained these algorithms in a fully unsupervised way, with no prior knowledge of the functions of particular regions. Adding information provided by expert-created annotations to supervise training could improve the annotations created by these methods. We implemented semi-supervised learning using virtual evidence in the annotation method Segway. Additionally, we defined a positionally tolerant precision and recall metric for scoring genome annotations based on the proximity of each annotation feature to the truth set. We demonstrate semi-supervised Segways ability to learn patterns corresponding to provided transcription start sites on a specified supervision label, and subsequently recover other transcription start sites in unseen data on the same supervision label.

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Chan, R. C. W., McNeil, M., Roberts, E. G., Mendez, M., Libbrecht, M. W., Hoffman, M. M.. 2020-02-01. Semi-supervised segmentation and genome annotation. https://doi.org/10.1101/2020.01.30.926923

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