Desperately seeking microRNA targets

By Marshall P. Thomas, J. Lieberman, A. Lal. Nature Structural &Molecular Biology, 2010

528 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4060f73f86880a3dddd4606b560afa2af0494c4a

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Citations in the corpus

Long non-coding RNAs: a new frontier in the study of human diseases. (2013, background): “microRNAs regulate a large portion of the transcriptome, including the coding and noncoding transcriptome [98].”
Transcriptional and Post-transcriptional Gene Regulation by Long Non-coding RNA (2017, background): “Computational predictions suggest that, potentially, a single miRNA may target hundreds of transcripts [41].”
The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations (2014, background): “Predictions are primarily based on base pairings in the miRNA seed region (the first eight nucleotides of an miRNA), conservation of target sites across species, accessibility of target sites, free energy of the miRNA–mRNA duplex, or a combination of two or more of these features (3,14).”
In vivo identification of tumor suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma (2011, unknown): “miRNAs are endogenous, noncoding 22 nucleotide RNAmolecules that bind to microRNA response elements (MREs) contained in their target mRNAs (Bartel, 2009; Thomas et al., 2010).”
A ceRNA hypothesis: the Rosetta stone of a hidden RNA language? (2011, background): “Although several target prediction algorithms are successful in identifying some microRNA targets, they commonly fail to predict some important microRNA targets, mainly because the rules of targeting are still not understood (Bartel, 2009; Thomas et al., 2010).”
miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments (2012, background): “These different properties has resulted that the miRNA analysis pipelines, such as novel miRNA prediction and target prediction, are quite different between plants and animals [9-11].”
CRISPRTarget (2013, background): “Therefore, predictive tools to discover functional binding sites have been developed that use the properties of known sites to predict new ones.(45,46) The critical factor is distinguishing true sites from false positives, and there are a large number of algorithms implemented for miRNA target disco”
An overview of microRNAs (2015, background): “In general, bioinformatic approaches are a good starting point for miRNA analysis and many research labs make use of them[102, 103].”