bioRxivGenomicsPreprint
MicroWorldOmics:A "One step for All" desktop application for comprehensive analysis in microbiome and virome and exploring "Dark Matter" from MicroWorld
Runze LiiD, Wei DongiD, Mingjie WangiD, Xiaoqing HuiD, Jiao Wan, Zhuang YangiD
Abstract
A large amount of high-throughput sequencing data generated in ecology, medicine, and pharmacy has increasingly raised the complexity of data analysis and interpretation. However, the microbiome and virome field still lacks a convenient and programming-free desktop application for the comprehensive analysis in microbiome and virome data, particularly in virome analysis and Dark Matter exploration. Therefore, a plugin development mode desktop service, MicroWorldOmics, is proposed with a convenient and one-stop pipeline for life sciences and biomedical fields. Plugin development allows users to analyze data in parallel and interactively, while these tasks usually require advanced bioinformatician to achieve. MicroWorldOmics is a software widely used in microbiome and virome, containing 90 sub-applications, including four main functions: epidemiological analysis, metagenomic/amplicons and virome comprehensive analysis, and exploring Dark matter. More than 80 Python modules and 600 R packages are invoked in all aspects of bioinformatics analysis, statistical analysis, deep learning, and visualization. At the same time, the software supports multiple input and output results, such as GFF3, FASTA, CSV, PNG, JPG, JSON, and TXT, etc. Besides, to improve the efficiency for users, MicroWorldOmics supports being deployed in three mainstream systems: Windows, Linux, and Mac and also sets the demo/example data for users to test the benchmark easily. In summary, the development of MicroWorldOmics greatly facilitates the process of analyzing microbiome and virome data for researchers who have no programming foundation and are devoted to life science and biomedicine data analysis.
- assay: metagenomics
- assay: proteomics
- research focus: software / tool
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- Citations
- 0
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