Many open access transcriptomic data of coronavirus disease 2019 (COVID-19) were generated, they have great heterogeneity and are difficult to analyze. To utilize these invaluable data for better understanding of COVID-19, additional software should be developed. Especially for researchers without bioinformatic skills, a user-friendly platform is mandatory. We developed the COVID19db platform (http://hpcc.siat.ac.cn/covid19db & http://www.biomedical-web.com/covid19db) that provides 39 930 drug–target–pathway interactions and 95 COVID-19 related datasets, which include transcriptomes of 4127 human samples across 13 body sites associated with the exposure of 33 microbes and 33 drugs/agents. To facilitate data application, each dataset was standardized and annotated with rich clinical information. The platform further provides 14 different analytical applications to analyze various mechanisms underlying COVID-19. Moreover, the 14 applications enable researchers to customize grouping and setting for different analyses and allow them to perform analyses using their own data. Furthermore, a Drug Discovery tool is designed to identify potential drugs and targets at whole transcriptomic scale. For proof of concept, we used COVID19db and identified multiple potential drugs and targets for COVID-19. In summary, COVID19db provides user-friendly web interfaces to freely analyze, download data, and submit new data for further integration, it can accelerate the identification of effective strategies against COVID-19.

Many circRNA transcriptome data were deposited in public resources, but these data show great heterogeneity. Researchers without bioinformatics skills have difficulty in investigating these invaluable data or their own data. Here, we specifically designed circMine (http://hpcc.siat.ac.cn/circmine and http://www.biomedical-web.com/circmine/) that provides 1 821 448 entries formed by 136 871 circRNAs, 87 diseases and 120 circRNA transcriptome datasets of 1107 samples across 31 human body sites. circMine further provides 13 online analytical functions to comprehensively investigate these datasets to evaluate the clinical and biological significance of circRNA. To improve the data applicability, each dataset was standardized and annotated with relevant clinical information. All of the 13 analytic functions allow users to group samples based on their clinical data and assign different parameters for different analyses, and enable them to perform these analyses using their own circRNA transcriptomes. Moreover, three additional tools were developed in circMine to systematically discover the circRNA–miRNA interaction and circRNA translatability. For example, we systematically discovered five potential translatable circRNAs associated with prostate cancer progression using circMine. In summary, circMine provides user-friendly web interfaces to browse, search, analyze and download data freely, and submit new data for further integration, and it can be an important resource to discover significant circRNA in different diseases.

Background Although studies reported that extracellular miRNAs have significant functions in regulating the development of human diseases, our understanding of their role in human diseases remains to be further addressed. Many extracellular miRNA expression data were deposited in public resources, which are heterogeneous and difficult to investigate due to the data generated from different high throughput platforms. To use these invaluable data for accelerating the discovery of non-invasive miRNA biomarkers, a comprehensive and user-friendly database platform is essential, especially for bench researchers who lack bioinformatics skills. Methods We integrated, standardized, and annotated human extracellular miRNA expression data and cancer-related miRNA transcriptome data from NCBI GEO and The Cancer Genome Atlas (TCGA), respectively. Moreover, we developed the ExomiRHub database platform that designed with comprehensive online analysis functions and tools to analyze these data or User's own data. These analysis functions and tools were designed to enable users to select samples, define groups and parameters for their own analysis. Results ExomiRHub includes 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, including 29,198 samples and 23 sample types. ExomiRHub further includes 16,012 miRNA transcriptome data of 156 cancer sub-types to enhance the usability of it in cancer research. To accelerate the identification of non-invasive miRNA biomarkers, ExomiRHub provides 25 online analytical and visualization functions to individually analyze these data. Moreover, ExomiRHub provides Web Service to enable users in conducting the analyses on their uploaded data. Furthermore, ExomiRHub provides four additional tools to evaluate the functions and targets of miRNAs and their variations. Finally, we used ExomiRHub and discovered non-invasive miRNA biomarkers associated with angiogenesis-related pathways for monitoring glioma progression. Conclusion The comprehensive data and functions of ExomiRHub can greatly accelerate the discovery of non-invasive miRNA biomarkers. It is freely accessible at the websites of http://hpcc.siat.ac.cn/exomirhub/ & http://www.biomedical-web.com/exomirhub/.