With the upgrade and development of the high-throughput sequencing technology, multi-omics data can be obtained at a low cost. However, mapping tools that existed for microbial multi-omics data analysis cannot satisfy the needs of data description and result in high learning costs, complex dependencies, and high fees for researchers in experimental biology fields. Therefore, developing a toolkit for multi-omics data is essential for microbiologists to save effort. In this work, we developed MicrobioSee, a real-time interactive visualization tool based on web technologies, which could visualize microbial multi-omics data. It includes 17 modules surrounding the major omics data of microorganisms such as the transcriptome, metagenome, and proteome. With MicrobioSee, methods for plotting are simplified in multi-omics studies, such as visualization of diversity, ROC, and enrichment pathways for DEGs. Subsequently, three case studies were chosen to represent the functional application of MicrobioSee. Overall, we provided a concise toolkit along with user-friendly, time-saving, cross-platform, and source-opening for researchers, especially microbiologists without coding experience. MicrobioSee is freely available at https://microbiosee.gxu.edu.cn.
- MicrobioSee is a complementary toolkit for multi-omics data, and is available free of charge from https://microbiosee.gxu.edu.cn;
- Detailed user documentation can be found at https://microbiosee.github.io,
- The main code is available at https://github.com/MicrobioSee/MicrobioSee.
- In this work, we developed MicrobioSee, a web-based real-time interactive visualization tool based on web technologies, which could visualize microbial multi-omics data and include seventeen modules surrounding the major omics of microorganisms such as transcriptome, metagenome, and proteome. Subsequently, three case studies were chosen to represent the functional application of MicrobioSee. Overall, we provide a concise tool along with user-friendly, time-saving, cross-platform, and source-opening for researchers especially microbiologists without coding experience.
Cite:
Jinhui Li, Yimeng Sang, Sen Zeng, Shuming Mo, Zufan Zhang, Sheng He, Xinying Li, Guijiao Su, Jianping Liao, and Chengjian Jiang. MicrobioSee: a web-based visualization toolkit for multi-omics of microbiology. Frontiers in Genetics. 2022. 13: 853612. DOI: 10.3389/fgene.2022.853612.
- Orcid: Jinhui Li