Browsing by Subject "BioVLAB-MMIA-NGS"

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    BioVLAB-MMIA-NGS: MicroRNA-mRNA Integrated Analysis using High Throughput Sequencing Data
    (Oxford, 2015-09) Chae, Heejoon; Rhee, Sungmin; Nephew, Kenneth P.; Kim, Sun; Department of Cellular & Integrative Physiology, School of Medicine
    Motivation: It is now well established that microRNAs (miRNAs) play a critical role in regulating gene expression in a sequence-specific manner, and genome-wide efforts are underway to predict known and novel miRNA targets. However, the integrated miRNA–mRNA analysis remains a major computational challenge, requiring powerful informatics systems and bioinformatics expertise. Results: The objective of this study was to modify our widely recognized Web server for the integrated mRNA–miRNA analysis (MMIA) and its subsequent deployment on the Amazon cloud (BioVLAB-MMIA) to be compatible with high-throughput platforms, including next-generation sequencing (NGS) data (e.g. RNA-seq). We developed a new version called the BioVLAB-MMIA-NGS, deployed on both Amazon cloud and on a high-performance publicly available server called MAHA. By using NGS data and integrating various bioinformatics tools and databases, BioVLAB-MMIA-NGS offers several advantages. First, sequencing data is more accurate than array-based methods for determining miRNA expression levels. Second, potential novel miRNAs can be detected by using various computational methods for characterizing miRNAs. Third, because miRNA-mediated gene regulation is due to hybridization of an miRNA to its target mRNA, sequencing data can be used to identify many-to-many relationship between miRNAs and target genes with high accuracy.