Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences

By Eric Bonnet, Jan Wuyts, Pierre Rouzé, Y. Van de Peer. Bioinform., 2004

559 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e59fb79091b8272747adab84547c8a5fc7126ce8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine (2005, background): “Recent reports have shown that local sequence features are important in pre-miRNAs [29].”
Discovering microRNAs from deep sequencing data using miRDeep (2008, background): “As miRNA precursors are more stable than nonprecursor hairpin”
Principles of MicroRNA–Target Recognition (2005, unknown): “nearly perfectly complementary [23,24,25].”
MicroRNAs in Common Human Diseases (2012, background): “Taking a variety of factors into consideration, such as sequence conservation and thermodynamic stability of secondary structure, researchers were now able to identify new miRNA species that failed to be discovered by cloning approaches [17].”
LNCipedia: a database for annotated human lncRNA transcript sequences and structures (2012, background): “Structural RNAs, such as miRNAs, have a significantly lower minimum free energy of folding compared with randomly shuffled sequences (18).”
Transposable Elements Are Major Contributors to the Origin, Diversification, and Regulation of Vertebrate Long Noncoding RNAs (2013, methodology): “49%) in the Gencode v13 set (see Table S7) were used as input for randfold [85].”
miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades (2011, methodology): “Since it is known that miRNA precursors are energetically stable given the nucleotide compositions (36), the stability of potential pre-cursors can optionally be predicted using the randfold tool.”
MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features (2007, methodology): “In order to determine if the MFE value is signiﬁcantly diﬀerent from that of random sequences, a Monte Carlo randomization test was used (22).”
Fast and reliable prediction of noncoding RNAs (2005, unknown): “This effect is particularly dramatic in the case of microRNA precursors [33].”