Construction of microRNA prediction model and regulatory networks in plants

  • 江 謝逸帆

Student thesis: Master's Thesis


MicroRNAs are endogenous non-coding small RNAs (about 22 nucleotides) which play important roles in post-transcriptional regulation of gene expression via mRNA cleavage or translation inhibition The major topics of miRNA research can be classified into three parts: novel miRNA identification miRNA target recognition and transcriptional regulation of miRNA expression However no relevant research comprehensively integrates all these three parts so far For the discovery of novel miRNAs although several machine learning-based approaches were developed to identify novel miRNAs from high-throughput next generation sequencing (NGS) most of them essentially require precursor/genomic sequences for reference especially focusing on pre-miRNA identification Hence non-availability of genomic sequences becomes a limitation in miRNA discovery in non-model organisms It is necessary to develop a systematic approach to identify novel miRNAs without reference sequences In order to identify miRNAs from non-model organisms support vector machine (SVM) algorithm was used to build a prediction model for novel miRNA identification from NGS datasets with significant features of mature miRNA including 5’-uracil the difference of read count between guide and passenger strand of miRNA precursor and the paired structure of dicer cleavage site On the other hand a web-based resource AtmiRNET was established to systematically reconstruct regulatory networks of miRNAs in Arabidopsis Followed up the series of analytical functions (i e promoters regulators targets and networks) in AtmiRNET reliable miRNA promoters TF-miRNA relations and direct and indirect targets are recommended to execute functional enrichment analysis and network reconstruction of Arabidopsis miRNAs The valuable information that is visually oriented in AtmiRNET recruits the scant understanding of plant miRNAs
Date of Award2015 Sept 11
Original languageEnglish
SupervisorWen-Chi Chang (Supervisor)

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