Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

By Chenghai Xue, Fei Li, Tao He, Guo-Ping Liu, Yanda Li, Xuegong Zhang. BMC Bioinformatics, 2005

492 citations recorded by the source corpus.

BackgroundMicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.ResultsA set of novel features of local contiguous structure-sequence information is proposed for distinguishing the hairpins of real pre-miRNAs and pseudo pre-miRNAs. Support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data. Remarkably, the SVM classifier built on human data can correctly identify up to 90% of the pre-miRNAs from other species, including plants and virus, without utilizing any comparative genomics information.ConclusionThe local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new miRNAs.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c4a3e22f23b078b1a27638940d91befa2908b152

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Citations in the corpus

MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features (2007, methodology): “The human pseudo pre-miRNAs were obtained from http://bioinfo.au.tsinghua.edu.cn/mirnasvm/, which contained 8494 premiRNA-like hairpins ( 21 ).”
miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments (2009, methodology): “(9) Bindings The number of bindings in the stem divided by the hairpin length W74 Nucleic Acids Research, 2009, Vol.”
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction (2016, methodology): “MiPred (Jiang et al., 2007), microPred (Batuwita & Palade, 2009), triplet-SVM (Xue et al., 2005), and miRBoost (Tempel et al., 2015) uses the support vector machine (SVM); CSHMM (Agarwal et al., 2010) has adopted the hidden Markov model (HMM) and additionally utilized context-sensitive…”
Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. (2008, methodology): “…of Dulbecco’s Modified Eagle Medium (DMEM)/F12 containing 20% Knockout Serum Replacer (Invitrogen), 0.1 mM -mercaptoethanol, 0.1 mM nonessential amino acids, 1 mM glutamine, and 4 ng/mL FGF2 (R&D Systems) and conditioned by mitotically inactivated mouse embryonic fibroblasts (Xue et al. 2005).”