MicroRNAs (miRNAs) are small RNA molecules that play key regulatory roles in general biological processes and disease pathogenesis. These small RNA molecules interact with their target mRNAs to induce mRNA degradation and/or inhibit the translation of mRNAs into proteins. Therefore, identifying miRNA targets is an essential step to fully understand the regulatory effects of miRNAs. Here, we describe a regularized regression approach that integrates the sequence information with the miRNA and mRNA expression profiles for detecting miRNA targets. This method takes into account the full spectrum of gene sequence features of miRNA targets, including the thermodynamic stability, the accessibility energy, and the context features of the target sites,. Given these sequence features for each putative miRNA-mRNA interaction and their expression values, this model is able to quantify the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature for predicting functional miRNA-mRNA interactions.
MiRNA target identification miRNA and mRNA expression profiles Regularized regression Sequence features Context score Thermodynamic stability
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This work is supported in part by NIH grant R01 LM010022 and the seed grant from the University of Texas Health Science Center at Houston.
Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20CrossRefPubMedGoogle Scholar
Fabian MR, Sonenberg N, Filipowicz W (2010) Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem 79:351–379CrossRefPubMedGoogle Scholar
Huntzinger E, Izaurralde E (2011) Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat Rev Genet 12:99–110CrossRefPubMedGoogle Scholar
Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A et al (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129:1401–1414CrossRefPubMedPubMedCentralGoogle Scholar
Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39:D152–D157CrossRefPubMedGoogle Scholar
Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500CrossRefPubMedGoogle Scholar
Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G et al (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics 10:295CrossRefPubMedPubMedCentralGoogle Scholar
Betel D, Wilson M, Gabow A, Marks DS, Sander C (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res 36:D149–D153CrossRefPubMedGoogle Scholar
Xu W, San Lucas A, Wang Z, Liu Y (2014) identifying microRNA targets in different gene regions. BMC Bioinformatics 15:11CrossRefGoogle Scholar
Xu W, Wang Z, Liu Y (2014) The characterization of microRNA-mediated gene regulation as impacted by both target site location and seed match type. PLoS One 9:e108260CrossRefPubMedPubMedCentralGoogle Scholar
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284CrossRefPubMedGoogle Scholar
Huang JC, Morris QD, Frey BJ (2007) Bayesian inference of MicroRNA targets from sequence and expression data. J Comput Biol 14:550–563CrossRefPubMedGoogle Scholar
Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, Chou CH et al (2014) miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 42:D78–D85CrossRefPubMedGoogle Scholar
Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP (2011) Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol 18:1139–1146CrossRefPubMedPubMedCentralGoogle Scholar
Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21:3940–3941CrossRefPubMedGoogle Scholar
Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M et al (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci U S A 98:15149–15154CrossRefPubMedPubMedCentralGoogle Scholar
Zhuang X, Li Z, Lin H, Gu L, Lin Q, Lu Z et al (2015) Integrated miRNA and mRNA expression profiling to identify mRNA targets of dysregulated miRNAs in non-obstructive azoospermia. Sci Rep 5:7922CrossRefPubMedPubMedCentralGoogle Scholar