RNAhybrid: microRNA target prediction easy, fast and flexible

By Jan Krüger, Marc Rehmsmeier. Nucleic Acids Res., 2006

1,810 citations recorded by the source corpus.

In the elucidation of the microRNA regulatory network, knowledge of potential targets is of highest importance. Among existing target prediction methods, RNAhybrid [M. Rehmsmeier, P. Steffen, M. Höchsmann and R. Giegerich (2004) RNA, 10, 1507–1517] is unique in offering a flexible online prediction. Recently, some useful features have been added, among these the possibility to disallow G:U base pairs in the seed region, and a seed-match speed-up, which accelerates the program by a factor of 8. In addition, the program can now be used as a webservice for remote calls from user-implemented programs. We demonstrate RNAhybrid's flexibility with the prediction of a non-canonical target site for Caenorhabditis elegans miR-241 in the 3′-untranslated region of lin-39. RNAhybrid is available at .

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/68d4c362fd444bacb6186eda2519fd0591e0beb4

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Citations in the corpus

MicroRNAs in Common Human Diseases (2012, neutral): “Keywords: MicroRNA; Human diseases; Diagnostics; Therapeutics; Biomarker”
miR2Disease: a manually curated database for microRNA deregulation in human disease (2008, background): “target genes/sites of microRNAs, such as TargetScan (15), PicTar (16), RNAhybrid (17) and PITA (18).”
MicroRNAs and complex diseases: from experimental results to computational models (2019, positive): “0) not only collects experimentally verified interactions between miRNAs and miRNA target genes but also provides three established computational tools (i.e. miRanda, RNAhybrid and TargetScan) for identifying miRNA targets in 30- UTR of genes.”
IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions (2008, methodology): “The most popular tools incorporating this idea are RNAHYBRID (Rehmsmeieret al., 2004; Kruger and Rehmsmeier, 2006), RNADUPLEX and RNAPLEX (Tafer and Hofacker, 2008), and DINAMELT (Dimitrov and Zuker, 2004; Markham and Zuker, 2005).”
miRTarBase: a database curates experimentally validated microRNA–target interactions (2010, neutral): “Additionally, among the several computational methods and web-based programs developed to identify target genes of miRNAs include miRanda (6), TargetScan (13), RNAhybrid (14), Pictar (15) and PITA (16).”
SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features (2016, background): “Finally, it has been indicated that the (predicted) RNA secondary structures can serve as informative features for predicting RNA functional sites like those targeted by microRNA (38,39) or vulnerable to deleterious mutations”
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.”
miRGen: a database for the study of animal microRNA genomic organization and function (2006, methodology): “Two additional databases, ARGONAUTE (31) and miRNAMap (32), offer enhanced interfaces to the data contained in miRBase for human, mouse, rat and dog. miRNAMap also reports computationally predicted miRNAs and their predicted targets according to programs miRanda and RNAhybrid (33).”