RNAenrich is a web server for functional enrichment and interaction network analysis to almost all types of RNAs, covering mRNA or gene, microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA), small nucleolar RNA (snoRNA), and PIWI-interacting RNA (piRNA) in human and mouse. In particular, RNAenrich adopts a new enrichment strategy, employing RNA-targeted functional genes from experimentally-supported data, which not only makes the most coverage of different types of RNAs but also ensures confidence level of the enriched results. The enrichment results will give a description about which pathway, function and disease these RNAs mainly participate in, presenting as downloadable tables and visual figures.
RNAenrich supports five categories of enrichment analysis:
(1) Signaling Pathway -- protein-directed signaling conduction pathway that RNAs participate in(2) Metabolic Pathway -- metabolite-based pathway or reaction that RNAs regulate
(3) Gene Ontology -- biological process, cellular location and molecular function that RNAs participate in
(4) Disease -- RNA-involved occurrence or development of disease or complication
(5) Therapeutic Target -- drug target from therapeutic target-related databases that RNAs regulate
Statistics of RNA items that RNAenrich can analyze:
| Species | mRNA | miRNA | lncRNA | circRNA | snoRNA | piRNA |
|---|---|---|---|---|---|---|
| Human | 19,070 | 2,664 | 3,994 | 56,590 | 741 | N/A |
| Mouse | 22,707 | 1,979 | 354 | 1,154 | 254 | 2,090 |
Citing the RNAenrich:
Zhang S, Amahong K, Zhang Y, Hu X, Huang S, Lu M, Zeng Z, Li Z, Zhang B, Qiu Y, Dai H, Gao J, Zhu F*. RNAenrich: a web server for non-coding RNA enrichment. Bioinformatics. 2023 Jul 1;39(7):btad421. doi: 10.1093/bioinformatics/btad421; PMID: 37399102The enrichment module will give an answer about which pathways, diseases and cellular processes are statistically significantly associated with the input RNA list. The input RNA list can be Gene ID, Gene Symbol, RNA name or RNA ID. Five ways of enrichment are provided in the module.
Select species
RNA type
ID type
Input RNA List
Select desired databases
Options of over-representation analysis (ORA) enrichment analysis
P-value adjustment method
Significance level
Minimum required hits per sub-category
Maximum required hits per sub-category
The RNA-RNA interaction module will give a profile about how a single regulatory ncRNA joins in gene regulation network. In this module, each of RNA-RNA interaction pair is provided special regulatory information.
Select species
RNA type
ID type
Query RNA List
The ID conversion module provides a conversion function for RNA IDs from different sources, such as RNAcentral ID, Official Symbol, Gene ID, Ensembl ID and so on.
Select species
RNA type
Input ID type
Output ID type
Enter RNA IDs
1. Introduction of RNAenrich
2. Enrichment
2.1 Data Input
2.2 Results
2.2.1 Enrichment Table
2.2.2 Enrichment Graph
2.2.3 Protein-protein interactions plot
3. RNA-RNA interaction
3.1 Data Input
3.2 Results
3.2.1 RNA-RNA interaction Table
3.2.2 RNA-RNA interaction Graph
4. ID conversion
4.1 Data Input
4.2 Results
RNAenrich is a web server for RNA enrichment and RNA-RNA interaction network analysis of all types of regulatory RNAs, especially non-coding RNAs in human and mouse species. For a list of mRNA/Gene or non-coding RNAs (microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA), small nucleolar RNA (snoRNA), and PIWI-interacting RNA (piRNA)), the tool can determine which Gene Ontology (GO) terms, signaling pathways, metabolic pathways, diseases or therapeutic targets show a statistical significance.
The function of RNAenrich is composed of two modules: (1) Enrichment analysis and (2) RNA-RNA interaction analysis.
Enrichment module will give an answer about which Go terms, pathways, diseases, and therapeutic targets are significantly associated with the input mRNAs/Genes or noncoding RNAs such as miRNA, lncRNA, circRNA, snoRNA, and piRNA.
Step 1: Select species: Homo sapiens (Human) and Mus musculus (mouse).
Step 2: Choose the RNA type: miRNA, lncRNA, circRNA, snoRNA, piRNA, or mRNA/Gene. Then choose the ID type: Gene ID, Gene Symbol, RNA ID, or RNA name.
Step 3: Choose wanted databases to proceed an enrichment analysis. RNAenrich provides five ways including nine databases to carry out an enrichment.
Step 4: Choose options for statistics. The tool provides different "P-value adjustment methods" to select. In a default setting, "Significance level" is 0.05; "Minimum required hits per sub-category' is 10; "Maximum required hits per sub-category" is 500.
Step 5: Click "Run" button to submit an enrichment analysis.
The task is generally completed within ten to tens of seconds.
Enrichment analysis results included "Enrichment table", "Enrichment Graph" and "Protein-protein interactions plot".
In enrichment table, users can choose one or more databases to present enrichment results. Table information includes Database, LinkID, Count, Rate, pvalue, p.adjust and qvalue. Users can search-related information by the "Search" box. Enrichment results can download by clicking the "Enrichment result download" button. Restart a new task by clicking on the bottom button "Back to RNA input page".
2.2.2 The enrichment Graph (bar plot and bubble plot)
In the enrichment graph, users can choose one or more databases to present enrichment results by clicking on the top left corner to select. When users choose all databases, the number of enrichment results for each database to display is top 5. In bubble plot, the bubble distance to the Y-axis is determined by enrichment score (-lg(pvalue)), and the bubble size is proportional to number of RNA target genes.
When users choose a single enrichment database to display, the bar plot or bubble plot will be drawn with the top 20 terms according to the pvalue. In addition, the tool will generate a correlation plot to describe the correlation between the different terms. Plots can download by clicking "download" button.
2.2.3 Protein-protein interaction (PPI) network
RNAenrich will generate PPI plot to describe which PPI the input RNAs are involved in. Protein-protein interactions plot: users can choose the number of RNA target genes to analyze, the confidence score can be set to 0.95 or 0.75. The plot can be downloaded by clicking "download" button.
Clicking a node displays detailed protein information; clicking an edge displays a confidence score.
The RNA-RNA interaction module will give an overview of how a single regulatory ncRNA joins in the gene regulatory network.
Step 1: Select species: Homo sapiens (Human) and Mus musculus (mouse).
Step 2: Choose the RNA type: miRNA, lncRNA, circRNA, snoRNA, piRNA, or mRNA/Gene. Then choose the ID type: Gene ID, Gene Symbol, RNA ID, or RNA name.
Step 3: Input the RNA list.
Step 4: Click the "Query" button to submit an analysis.
3.2 View and download the results
Results include "RNA-RNA interaction Table" and "RNA-RNA interaction Graph".
3.2.1 RNA-RNA interaction Table
In the "RNA-RNA interaction Table", users can search relevant information from the table by the "search" box. The result can download by clicking the "RNA query result download" button.
3.2.2 RNA-RNA interaction Graph
In the "RNA-interaction Graph", users can choose an input RNA to make an RNA-interaction graph. Users can choose a maximum number of RNA-RNA interactions. The default number is 10. In the graph of RNA-RNA interaction, the circle represents selected RNA that make an interaction graph; blue rectangle represents interacted miRNA with selected RNA; gray rectangle represents interacted mRNA with miRNA. The graph of RNA-RNA interaction can download by clicking "download" button.
The ID conversion module provides a conversion function for RNA IDs from different sources, such as RNAcentral ID, Official Symbol, Gene ID, Ensembl ID and so on.
Step 1: Select species: Homo sapiens (Human) and Mus musculus (mouse).
Step 2: Choose the RNA type: miRNA, lncRNA, circRNA, snoRNA, piRNA, or mRNA/Gene.
Step 3: Choose Input and Output ID type: RNAcentral ID, miRBaseID (Mature), miRBase ID (Stem-loop), Official Symbol, GeneID, miRNA name.
Step 4: Click the "Submit" button to run.
4.2 View and download the results
In the result page, users can search input and output IDs. The result can download by clicking the "ID conversion result download" button.
Please feel free to visit the website of Prof. Feng Zhu (the corresponding author):
Work website: https://idrblab.org/Peoples.php
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Get in touch directly at zhangsong_@zju.edu.cn and zhangyintao@zju.edu.cn