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INTRODUCTION
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IPEV applied CNN to distinguish prokaryotic and eukaryotic Virus from virome data. It is built on Python3.8.6 , Tensorflow 2.3.1. IPEV calculates a set of scores that reflect the probability that the input sequence fragments are prokaryotic and eukaryotic viral sequences. By using parallelism and algorithmic optimization, IPEV gets the results of the calculations very quickly.
Please direct your questions or comments to yinhengchuang@pku.edu.cn or hqzhu@pku.edu.cn
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DOWNLOAD
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DATA
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All data used to train and test IPEV, related results and scripts are stored zenodo
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CITATION
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REFERENCES
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Fang, Z., Tan, J., Wu, S., Li, M., Xu, C., Xie, Z., and Zhu, H. (2019). PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. GigaScience, 8(6), giz066. McNair, K., Bailey, B.A. and Edwards, R.A. (2012) PHACTS, a computational approach to classifying the lifestyle of phages. Bioinformatics, 28(5), 614-618. Breitbart Mya RF. Here a virus, there a virus, everywhere the same virus?, Trends Microbiol 2005;13:278-284. Richter, D.C., Ott, F., Auch, A.F., Schmid, R. and Huson, D.H. (2008) MetaSim-A Sequencing Simulator for Genomics and Metagenomics. PloS One, 3(10), e3373. Pfeiffer F, Grober C, Blank M et al. Systematic evaluation of error rates and causes in short samples in next-generation sequencing, Sci Rep 2018;8:10950. |
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