ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities.

MOTIVATION: High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools. RESULTS: Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22 427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1, that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses. AVAILABILITY AND IMPLEMENTATION: ExpOmics is available at http://www.biomedical-web.com/expomics.

MicrobeTCM: A comprehensive platform for the interactions of microbiota and traditional Chinese medicine.

Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupoint-formulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com.

circMine: a comprehensive database to integrate, analyze and visualize human disease-related circRNA transcriptome.

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.

COVID19db: a comprehensive database platform to discover potential drugs and targets of COVID-19 at whole transcriptomic scale.

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.

ExomiRHub: A comprehensive database for hosting and analyzing human disease-related extracellular microRNA transcriptomics data.

Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.com/exomirhub/), a user-friendly database designed for biologists. This database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. ExomiRHub also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. All the data in ExomiRHub were further standardized and curated with annotations. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. Moreover, ExomiRHub provides a web service that allows biologists to analyze their uploaded miRNA expression data. Four additional tools were developed to evaluate the functions and targets of miRNAs and miRNA variations. Through ExomiRHub, we identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating its potential to significantly accelerate the discovery of extracellular miRNA biomarkers.