期刊
COMMUNICATIONS BIOLOGY
卷 3, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s42003-020-01233-4
关键词
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资金
- National Natural Science Foundation of China [31902156, 31702087, 31730089]
- National Key Research and Development Program [2016YFD0101900]
- National Swine Industry Technology System [CARS-35]
- Natural Science Foundation of Hubei Province of China [2019CFC855]
- Fundamental Research Funds for the Central Universities of China [2020IVB025, 2662018JC033]
The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model toSus scrofa, which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies inArabidopsis thalianaandOryza sativa. Additionally, to facilitate the use of the model, we present ISwine (http://iswine.iomics.pro/), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future.
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