No. Tool Species Method in brief Availability 1 ERPIN Lambert et al. [33,34] Animal, Plants Sequence alignment, dynamic programming, clustering http://rna.igmors.u-psud.fr/Software/erpin.php (Stand-alone application) 2 MiRScan Lim et al. [35,36] C.elegans Evolutionary conservation and Sequence Similarity http://genes.mit.edu/mirscan (Web application) 3 Srnaloop Grad et al. [37] C.elegans Conservation, sequence alignment by dynamic programming, folding energy, scoring based on base  pairing, filter based on structural parameters http://arep.med.harvard.edu/miRNA (Stand-alone application) 4 MiRCheck Bartel et al. [38] Arabidopsis Conservation, base pairing features of secondary structures http://www.mybiosoftware.com/rna-analysis/11199 (Stand-alone application) 5 findMiRNA Adai et al. [39] (2005) Arabidopsis Predicts possible microRNAs corresponds to target sites by the use of sequence complementarity, free energy, conservation http://sundarlab.ucdavis.edu/mirna (Stand-alone application) 6 miRAlign Wang et al. [40] Animals and plants Sequence alignment, Free energy http://bioinfo.au.tsinghua.edu.cn/miralign (Web application) 7 TripletSVM Chenghai et al. [41] Human Support vector machines, Sequence characteristics(triplet) http://bioinfo.au.tsinghua.edu.cn/software/mirnasvm (Stand-aloneapplication) 8 miR-abela Sheng et al. [42] Mammals Support vector machines for valid stem loop filtering. Search limited to extended genomic regions of known microRNA clusters of human / mouse / rat. http://www.mirz.unibas.ch/cgi/pred_miRNA_genes. cgi (Web application) 9 MiRFinder Huang et al. [43] Human Support vector machines with features extracted based on mutations of pre-microRNA secondary structure with that of pseudo microRNAs, free energy, base pairing of mature microRNAs http://www.bioinformatics.org/mirfinder/ (Stand-alone application) 10 miRDeep Friedlander et al. [44] All species Align sequenced reads with genome,  probabilistic scoring of secondary structure and signature www.mdc-berlin.de (Stand-alone application) 11 ANN Classifier Chandra [45] (2009) Human Arti_cial neural networks, Structural and thermodynamic features http://www.mca.cet.ac.in/research.htm (Stand-alone application) 12 MirExpress Wang et al. [46] Human, Plants Sequenced genome is not required, pre-processing on Deep sequencing data, alignment with known microRNAs and prediction using microRNA expression profile http://mirexpress.mbc.nctu.edu.tw (Stand-alone application) 13 Mpred Chandra et al. [47] Human Hidden markov model, Artificial neural networks, structural and thermodynamic features http://www.mca.cet.ac.in/research.htm (Stand-alone application) 14 MiRPara Wu et al. [48] All species Support vector machines, Properties of microRNAs, pre-microRNAs and pri-microRNAs https://code.google.com/p/mirpara/ (Stand-alone application) 15 microPred Rukshan et al. [49] (2011) Human Support vector machines, Sequence and structure features http://www.cs.ox.ac.uk/people/manohara.rukshan.batuwita/microPred.htm (Stand-alone application) 16 miRDeep* Jiyuan An et al. [50] Human Analysis on deep sequenced data- reads aggregation, sequence alignment, aggregate for potential microRNAs http://www.australianprostatecentre.org/research/software/mirdeep-star (Stand-alone application) 17 mirTools Zhu et al. [51] Human Unique reads from deep sequencing data are identified, aligned with reference genome, classified to different categories, microRNAs are identified from 'unclassified' sequences using miRdeep tool http://122.228.158.106/mr2_dev/download.php (Stand-alone application) 18 miRanalyzer (2011) Michael Hackenberg et al. [52] Human Unique reads from deep sequencing data are identified, aligned with known microRNAs, then non coding RNAs. Machine learning using Random forest algorithm ( feature vectors :structure, sequence (triplet), energy parameters ) http://web.bioinformatics.cicbiogune.es/microRNA (both stand-alone and web application) 19 miR-BAG Ashwani Jha et al. [53] All species Structural, thermodynamic, positional Features of 21 nt wide window, global features of full length sequence and machine learning approach ( BAGing(Bootstrap Aggregating) with SVM, Naive Bayes and Best First Decision Tree (BFS)) http://scbb.ihbt.res.in/presents/mirbag (Both standalone and web application)

Table 1 lists microRNA prediction software tools in the order of development and publication year. The table shows a brief description of method used, the type of availability (whether through web interface or to download and install locally), and species related

Table 1: Available pre-microRNA classification and prediction Tools.