The new development of MNDR.

1. Expand data sources and coverage of species and ncRNA types.

2. Support new function of fuzzy and batch search.

3. Provide three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools.

4. Add drug information related to ncRNA and disease.

5. Add RNA localization and interaction information.

Fig 1-1:

1. Main functions of the database are provided in menu bar form (boxed in light blue).

2. Other databases contributed by our group.

3. Citation.

Fig 1-1 Homepage

The Exact search page is displayed in Fig 2-1:

1. Carefully select a dataset: Four choices are provided.

2. Enter a keyword corresponding to select dataset.

3. Five categories provided to filter results: Category, Species, Data Source, Experimental Method and Score.

4. Use NCBI Gene / miRBase / circBase / piRBase / Disease Ontology / MeSH to normalize your input information.

Fig 2-1 Exact Search page

The Fuzzy search page is displayed in Fig 2-2:

1. Select the category of your keyword.

2. Enter a keyword corresponding to select.

3. Choose the match entries what you want.

Fig 2-2 Fuzzy Search page

The Batch search page is displayed in Fig 2-3:

1. Select a dataset for your keywords.

2. Enter the keywords or upload a file.

Fig 2-3 Batch Search page

This tutorial is as follows.

1. First we have to choose the type of keyword. There are four keyword types in our search as the picture shows. In this example, we choose ‘ncRNA Symbol’ as the keyword type.

Fig 3-1

2. Next, we enter the keyword according to the keyword type selected in the previous step. In this example, we choose 'has-miR-34a-5p' as the keyword.

Fig 3-2

3. Then select the category for the keyword you entered. In this example, we choose 'miRNA' as the category of the keyword ‘has-miR-34a-5p’.

Fig 3-3

4. Then select the species for the keyword you entered. In this example, we choose 'Homo sapiens' as the species of the keyword ‘has-miR-34a-5p’.

Fig 3-4

5. You can choose the type of data source as the filter. In this example, we want query the associations detected by experimental evidence, so we choose 'Experiment Validation'.

Fig 3-5

6. Then you can also choose the type of experimental method as the filter. In this example we choose ‘Strong Experimental Evidence’.

Fig 3-6

7. We provide a score for each association. The greater the value, the higher the credibility. To filter low-confidence associations, in this example, we choose the 0.5 as the minimum score and 1.0 as the maximum score.

Fig 3-7

8. With all the filters above, we can click 'Search' to query the result.

Fig 3-8

9. After several seconds, the result will occur. All the associations are represented in the table format, and your filters and the total numbers of associations are in the head of the web page.

Fig 3-9

In the result page, all entries are listed with basic information including ncRNA symbols,ncRNA categories, diseases, species and score.

1. Your current input conditions.

2. Total sum of results.

3. Download your search results.

4. Click to turn the page.

5. Click to link to detail page.

Fig 4-1 Search result page

In the detail page, you can get information including MNDR ID, confidence score, ncRNA information, RNA interaction information, RNA localization information, disease information, related drug information, evidence support and references.

Fig 5-1:

For ncRNA-disease associations, users can choose any union of ncRNA and disease to see results.

We redesigned the MNDR ID. Take MNDR-E-MI-32103 as an example, where 'MNDR' stands for MNDR database, 'E' stands for data type of experimental verification, while 'P' stands for the prediciton data. 'MI' stands for ncRNA type is miRNA, and similarly, 'LNC' stands for lncRNA, 'CIRC' Stands for circRNA, 'PI' stands for piRNA, 'SNO' stands for snoRNA, and '32103' stand for ID numbers.

Fig 5-1 Detial page of basic information

Fig 5-2:

For ncRNA-disease associations, users can click EntreZ ID/miRBase Accession/circBase ID to see its basic description in NCBI gene database/miRBase/circBase.

Fig 5-2 Detail page of ncRNA information

Fig 5-3:

1. Click any disease as a keyword to search in database.

2. Click Disease Ontology/MeSH ID to see its description in detail.

Fig 5-3 Detail page of disease information

Fig 5-4:

1. Related drug information from four source is provided.

2. Four functions of RNA-Drug are demonstrated.

3. Click Link can see more information.

Fig 5-4 Detail page of drug information

Fig 5-5:

RNA interactions of each entry also in our database are provided. Click Link can see more information.

Fig 5-5 Detail page of RNA interaction information

Fig 5-6:

RNA locations of each entry also in our database are provided. Click Link can see more information.

Fig 5-6 Detail page of RNA localization information

Fig 5-7:

1. Evidence Support including three parts: strong evidence, weak evidence and support database.

2. Tissue or cell line, target genes/RNAs and expression of each ncRNA.

3. Click pubmed ID to see description in detail.

Fig 5-7 Detail page of evidence support and reference

In the browse page, you can click each node to see results.

1. 'Diseases' display all entries as long as the current selected disease is involved.

2. 'ncRNA Category' indicates all kinds of ncRNA in MNDR.

3. 'Species' display all entries as long as the organism matches the condition.

Fig 6-1 Browse page

Three ncRNA- disease prediction tools are provided in our database, including miRNA, lncRNA and circRNA.

The miRNA-disease predictor is displayed in Fig 7-1:

1. Directly input one or more miRNA sequences or upload a .fasta file with fasta format.

2. The current input is limited to a maximum of 5 sequences.

3. The prediction is currently only valid for humans.

4. The prediction results are for reference only, and some miRNAs may not be calculated and predicted due to tool limitations.

Fig 7-1 miRNA-disease Predictor page

The predicted result is displayed in Fig 7-2:

1. The top 5 predicted results are listed.

2. The results include disease names and scores.

3. The results can be downloaded.

Fig 7-2 Result page of miRNA-disease Predictor

The lncRNA-disease predictor is displayed in Fig 7-3:

1. Directly input one lncRNA sequences or upload a .fasta file with fasta format.

2. Currently the tool only supports one sequence prediction.

3. The prediction is currently only valid for humans.

4. The prediction results are for reference only, and some miRNAs may not be calculated and predicted due to tool limitations.

Fig 6-3 lncRNA-disease Predictor page

The predicted result is displayed in Fig 7-4:

1. The top 5 predicted results are listed.

2. The results include disease names and scores.

3. The results can be downloaded.

Fig 7-4 Result page of lncRNA-disease Predictor

The circRNA-disease predictor is displayed in Fig 7-5:

1. Directly input one or more circRNA sequences or upload a .fasta file with fasta format.

2. Currently the tool only supports one sequence prediction.

3. The prediction is currently only valid for humans.

4. The prediction results are for reference only.

5. Currently the tool only supports the prediction for 18 neoplasms.

Fig 7-5 circRNA-disease Predictor page

The predicted result is displayed in Fig 7-6:

1. The top 5 predicted results are listed.

2. The results include disease names and scores.

3. The results can be downloaded.

Fig 7-6 Result page of circRNA-disease Predictor

In MNDR v3.0, the ncRNA-disease associations are collected from different types of resources under one common framework, including experimental and prediction evidence. In principle, we assume that:

1. Experimental evidence should contribute more important to the confidence score than prediction evidence;

2. Strong experimental evidence should provide more reliable evidence than weak experimental evidence;

3. ncRNA-disease associations supported by more evidence should be given significantly higher confidence scores than those supported by fewer evidence.

Similar to MNDR v2.0, according to the evidence types and number of evidence resources, we calculate the confidence score (S) for each ncRNA-disease association as follows:

where i is the evidence type(s: strong experimental evidence, w: weak experimental evidence, p: computational prediction method), x is the number of evidence resources, we set weight factor Ws, Ww and Wp to 0.95, 0.45, and 0.1, respectively (if x=0, we set weight factor Wi to 0).

Integration of source databases which use different ncRNA, disease and drug naming conventions is challenging. To ensure maximal connectivity of data, we transform each ncRNA, disease and drug name found in the input sources to the appropriate naming convention.

1. For miRNA, we use miRBase ID and miRBase Accession.

2. For circRNA, we use circBase ID.

3. For piRNA, we ues piRBase Name and piRBase Accession.

4. For others, we use official Gene Symbol and Entrez ID.

5. For diseases, we normalized each disease name and ID according to Disease Ontology and MeSH.

6. For drugs, we use NCBI PubChem Compound symbol and CID.

7. For species, we normalized organism names according to NCBI Taxonomy Database.

Contact wangdong79@smu.edu.cn or wangdong@ems.hrbmu.edu.cn
© Department of Bioinformatics, Southern Medical University