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Published on: June 24, 2019
CnnPOGTP: a novel CNN-based predictor for identifying the optimal growth temperatures of prokaryotes using only
Shaojing Wang1, Guoqiang Li1, Zitong Liao1
1Key Laboratory of Molecular Microbiology and Technology, College of Life Sciences, Nankai University, Ministry of Education, Tianjin 300071, China.
Summary:
Temperature is very important for the growth of microorganisms. Appropriate temperature conditions can improve the possibility for isolation of currently uncultured microorganisms. The development of metagenomic binning technology had dramatically increased the availability of genomic information of prokaryotes, providing convenience to infer the optimal growth temperature (OGT). Here, we proposed CnnPOGTP, a predictor for OGTs of prokaryotes based on deep learning method using only k-mers distribution derived from genomic sequence. This method was annotation free, and the predicted OGT could be obtained by simply providing the genome sequence to the CnnPOGTP website.
Availability And Implementation:
http://www.orgene.net/CnnPOGTP.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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