miRBase: annotating high confidence microRNAs using deep sequencing data

By Ana Kozomara, Sam Griffiths-Jones. Nucleic Acids Res., 2013

5,006 citations recorded by the source corpus.

We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/167836c4dc8502d026517111541966bfa19a918b

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Citations in the corpus

Predicting miRNA‐disease association based on inductive matrix completion (2018, background): “The latest version of miRBase collects 28 645 entries among 223 species (1881 human miRNAs) (Kozomara and Griffiths-Jones, 2014).”
mirDIP 4.1—integrative database of human microRNA target predictions (2017, background): “mimiRNA 2009 yes yes (41) MirAncesTar 2017 yes yes yes (42) mirbase 2006 2014 yes yes (43) miRcode 2012 yes yes (44)”
Metazoan MicroRNAs (2018, background): “For example, miRBase v21 lists 1,193 miRNA gene annotations in mouse and 1,881 in human (Kozomara and Griffiths-Jones, 2014), raising the question of how many of these additional annotations represent authentic miRNAs missed by the stringent annotation criteria (which might happen, for instance, if ”
miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database (2019, methodology): “Gene and miRNA-specific databases miRBase (7), NCBI Entrez gene (29), NCBI RefSeq (30) SNP or mutation related databases SomamiR (22,31) miRNA–disease association Database HMDD (9,10) The regulation of miRNAs TransMir (19), miRSponge (24)”
Ensembl comparative genomics resources (2016, background): “In brief, ncRNA genes are classified according to their Rfam (46) or miRBase (47) annotation.”
RNA Sequencing and Analysis (2015, positive): “To identify known miRNAs, the sequencing reads can be mapped to a specific database, such as miRBase, a repository containing over 24,500 miRNA loci from 206 species in its latest release (v21) in June 2014 (Kozomara and Griffiths-Jones 2014).”
LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases (2018, methodology): “MicroRNA was derived from miRBase (25), and microRNA targets were predicted using PITA (26), miRanda (27) and RNAhybrid (28) algorithms, and a high-quality miRNA target dataset was generated by intersecting data generated by at least two different miRNA target prediction methods.”