miRCarta: a central repository for collecting miRNA candidates

Abstract

The continuous increase of available biological data as consequence of modern high-throughput technologies poses new challenges for analysis techniques and database applications. Especially for miRNAs, one class of small non-coding RNAs, many algorithms have been developed to predict new candidates from next-generation sequencing data. While the amount of publications describing novel miRNA candidates keeps steadily increasing, the current gold standard database for miRNAs - miRBase - has not been updated since June 2014. As a result, publications describing new miRNA candidates in the last three to five years might have a substantial overlap of candidates without noticing. With miRCarta we implemented a database to collect novel miRNA candidates and augment the information provided by miRBase. In the first stage, miRCarta is thought to be a highly sensitive collection of potential miRNA candidates with a high degree of analysis functionality, annotations and details on each miRNA. We added-besides the full content of the miRBase-12,857 human miRNA precursors to miRCarta. Users can match their own predictions to the entries of miRCarta to reduce potential redundancies in their studies. miRCarta provides the most comprehensive collection of human miRNAs and miRNA candidates to form a basis for further refinement and validation studies. The database is freely accessible at https://mircarta.cs.uni-saarland.de/.

Figure 1.

Overview of the integrated or linked data sources and the functionality of miRCarta.

Figure 2.

Figurative example for the new naming scheme in miRCarta. MiRNAs are named with m-[number] and are organism unspecific. Precursors are named [organism_abbreviation]-[5p miRNA]-[3p miRNA].[location ID]. In this example, we have a human precursor hsa-1-3.1, consisting of miRNAs m-1 and m-3. If this precursor has another location in the genome it gets another location ID as exemplified for ppy-2-3.1 and ppy-2-3.2.

Figure 3.

Example of a precursor view for a predicted candidate in miRCarta. First, we list several basic facts about the precursor like its sequence, location, links to miRNAs, etc. In addition, we visualize the stem loop structure with the FornaContainer plugin (36) and color the miRNAs in the same way as in the sequence of the precursor. Below the structure, we show the pileup plots for the normalized or raw read counts with plotly.js. The user can easily switch here between log and linear scale or even visualize only counts with zero or one mismatches. The button ‘Show details’ opens a new HTML page, where more information can be found on how many samples had reads for this precursor and graphics showing if we found these reads rather continuously in several experiments or only a few. The last part shows the genomic context of the current precursor in a window of ±10 kb. This way it can be easily assessed if there are more precursors in this range or if the precursor lies in a gene or close to a gene for example. The genomic context is also interactive and allows for zooming in and out, and shows more information when clicking on a gene or miRNA etc.

Figure 4.

Excerpt of the results of uploading a GFF3 file for the predictions of Friedländer et al. (40). We find overlaps for 1461 of 4934 uploaded precursors/miRNAs in miRCarta. The first four rows in the table show examples for entries we did not find in miRCarta and the genomic context view shows that there are also no other miRNAs in a window of ±10 kb around the annotated location. The fifth row shows an entry where we have overlaps in miRCarta and the genomic context view illustrates that there are many other miRNA annotations in range.