Using high-throughput sequencing data to identify the transcriptional start sites of mouse microRNAs

• Main Authors: Hao-Yu Fang, 方浩宇
• Other Authors: 謝立青
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/32453885381096374336

碩士 === 國立中興大學 === 基因體暨生物資訊學研究所 === 101 === MicroRNAs (miRNAs) are non-coding small RNAs that inhibit protein coding gene expression by hybridizing with messenger RNAs (mRNAs). MiRNAs are involved in a lot of diverse biological processes and various diseases. To identify miRNA transcription start sites (TSSs) is important for studying the upstream regulatory networks of miRNAs. Up to now the studies regarding miRNA TSS identification are all focus on human miRNAs. We are interested in other species and our aim in this study is to identify mouse miRNA TSSs and the result would contribute to understanding the evolution of upstream regulatory networks of miRNAs. In this study, we integrated two types of high-throughput sequencing data, i.e. transcription start sites sequencing (TSSseq) and Cap Analysis of Gene Expression (CAGE), as the evidence of miRNA TSSs. A machine-learning-based Support Vector Machine (SVM) was developed to identify mouse miRNA TSSs. In addition, we also incorporated the ESTs (expression sequence tag) and sequence conservation information to provide evidence for mouse miRNA TSSs.