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- MicroRNAs function as part of a ribonucleoprotein complex;
Alexander Deiters 1. Colleen M.
Full text PDF Related articles. Citations 2 Recent citations: Daniel A. Busto et al. Related articles Based on techniques. Sanchez et al. J Biomol Screen 17 6 — Zhang J, Chung T, Oldenburg K A simple statistical parameter for use in evaluation and validation of high throughput screening assays.
Cellular MicroRNA Sensors Based on Luciferase Reporters
Comparing to HMDD v2. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories genetics, epigenetics, target, circulation, tissue and other covering 20 types of detailed evidence code.
Furthermore, we added new functionalities like network visualization on the web interface. Given the important functionality of miRNAs, dysregulation of miRNAs is associated with a large number of diseases, such as cancer, cardiovascular diseases, and neurodegenerative diseases 5. Therefore, a database for miRNA—disease association is important for biomedical scientists investigating the roles of miRNAs in diseases, and for bioinformatics scientists discovering patterns of miRNAs in diseases and developing novel miRNA—disease association prediction algorithms.
However, the roles of miRNAs in diseases are prominently diverged. For example, miRNAs can both promote and suppress cancers occurrence and progression, and they can serve as diagnosis and prognosis biomarkers 8—11 and novel therapeutic targets for the treatment of cancers 12 , Therefore, it is of urgent demand to update the database for more comprehensive data coverage and more accurate classifications of the miRNA—disease association evidence. To this end, we adopted an improved pipeline for manual data curation Figure 1A and launched v3.
Currently, HMDD v3. As the result, there is about two-fold increment of data, comparing to HMDD v2. A more detailed description of the updated database is available at the following sections. Overview of the HMDD v3. B Pie chart depicting the fractions of entries from six evidence categories.
C Circular chart showing the distribution of all diseases on the basis of disease classification from Disease Ontology. D Cumulative counts of miRNA—disease entries per publication time. The main purpose of HMDD v3. To compile the dataset, similar to HMDD v2. Then, we performed classification of miRNA—disease associations according to the evidence. According to the disease hierarchy from Disease Ontology 15 , we further grouped diseases into eight types Figure 1C.
Since the publication of HMDD v2. Interestingly, the studies that focus on the expression of miRNA in circulation system or lesion tissues contribute the most to the growth of data, which indicates the wide application of transcriptome profiling for screening potential miRNA biomarkers and therapeutic targets. However, such kind of experiential evidence is often weak or preliminary. In HMDD v3. In all, the detailed evidence code classification and disease name standardization, together with the significant data accumulation, constitute the major improvement of HMDD v3. The HMDD v3.
The entries are firstly grouped based on their evidence categories, which constitute the top node in the hierarchical tree. Under one category, user can choose a miRNA or a disease to obtain the related entries. For each miRNA, links to miRBase 3 and the functional enrichment information of its target genes 14 are provided. Since the target data of miRWalk 21 include predicted results and experimental evidence, the target gene set for each miRNA was defined as the consensus of both two kind of target data.
Then we performed gene set enrichment analysis for each target gene set using hypergeometric test. Finally, the P values for all signature gene sets are adjusted by Benjamini-Hochberg correction. The batch searching is also allowed if multiple at maximum 20 semicolon-delimited keywords are entered. The searching results can be downloaded by clicking the button above the result table. Finally, we added a function for the network visualization of disease-context miRNA—target interaction, based on the experimentally supported miRNA—target data from miRTarBase Users can click one disease to view the interaction network between the miRNAs and genes associated with this disease the disease genes are from DisGeNET Alternatively, if users click one miRNA name, all disease genes targeted by this miRNA will be illustrated in the similar network fashion.
In the HMDD v2. If one disease is associated with many miRNAs, its underlying mechanism would involve a complicated miRNA regulatory network. Therefore, MSW could be used to preliminarily evaluate the complexity of a disease 7. Clearly, cancers, as the well-acknowledged complex diseases, dominate the top list of diseases, though there are marginal differences in the detailed rank of diseases between HMDD v3.
Therefore, it is necessary to build and update miRNA—disease association databases to include the recent studies. HMDD v3.
Overview of MicroRNA Biology
Moreover, HMDD datasets could also be used for various analyses including but not limited to tracing the research hotspot, predicting miRNA-associated disease and performing functional enrichment analysis. Finally, we believe HMDD v3. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.
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