期刊
COMMUNICATIONS BIOLOGY
卷 1, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s42003-018-0091-x
关键词
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资金
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)
- National Natural Sciences Foundation of China [31571366, 31771477]
- National Key Research and Development Program of China [2016YFA0501704]
- 111 Project
- Fundamental Research Funds for the Central Universities
- Alexander-von-Humboldt foundation
- Federal Ministry of Education and Research
The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.
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