miRandola is a manually curated database containing information on extracellular and circulating non-coding RNAs. The first version of the database was published in Plos One in 2012 and it consisted of microRNA data extracted from public available papers in PubMed. The aim of miRandola is to collect all the information about non-coding RNAs that circulate in the blood stream and other body fluids. Non-coding RNAs play an important role in the regulation of various biological processes. They are frequently dysregulated in cancer, cardiovascular diseases and many other diseases and have shown promise as tissue-based markers for classification and prognostication. Extracellular non-coding RNAs in serum, plasma, saliva, urine and other body fluids have recently been shown to be associated with various pathological conditions. Non-coding RNAs circulate in the bloodstream in a highly stable, extracellular form, thus they may be used as blood-based biomarkers. The long term aim of miRandola is to become a comprehensive database for non-invasive biomarkers. We are working for new updates that will include other biomarkers such as extracellular DNA and circulating tumor cells. Users are welcome to download the database. The following table shows the comparison between the first published version of miRandola (2012) and the new version (2017):
|miRandola 2012||miRandola 2017|
|Long non-coding RNAs||0||12|
|Extracellular RNA forms||4||7|
|Organisms and animal models||1||14|
|External data||ExoCarta||ExoCarta and Vesiclepedia|
|Modern graphic design||No||Yes, using Bootstrap|
|*Assisted curation using text mining||No||Yes|
The first team of miRandola consisted of only one human biocurator. For this new version we increased the number of
biocurators involved in the project and moreover we introduced the Assisted Curation using a text mining approach.
It is important to underline that the new version of miRandola is still a manually curated database but we used
the text mining in order to search and prioritize the papers to curate through human curators. It is in fact clear that
the manual curation is a time consuming process and the approach we used will help our internal curators
to boost the update of the database at least two times per year (expected every 6 months).
The text mining dictionary included human non-coding RNAs , diseases , and keywords to indicate the RNA is present extracellularly and circulates. Text mining was run on more than 26 million abstracts in PubMed using the tagger software developed in . Pairs of these entities were scored by summing scores for all co-occurrences of the entities in the same sentence, paragraph and document with decreasing weights. These scores were then normalized, and the geometric mean of the RNA-disease and RNA-circulating scores was taken as the score for a triple. Papers that contain all three types of entity were given the same score as the triple. Papers were reviewed by human curators in order of decreasing score. References  Junge A, Refsgaard JC, Garde C, Pan X, Santos A, Alkan F, Anthon C, von Mering C, Workman CT, Jensen LJ, Gorodkin J. (2017). RAIN: RNA-protein Association and Interaction Networks. Database, baw167, 1–9. http://doi.org/10.1093/cercor/bhw393  Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. (2015). DISEASES: Text mining and data integration of disease-gene associations. Methods, 74, 83–89. http://doi.org/10.1016/j.ymeth.2014.11.020  Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, Vasileiadou A, Arvanitidis C, Jensen LJ. (2013). The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. PLoS ONE, 8(6), 2–7. http://doi.org/10.1371/journal.pone.0065390