BioVLAB-MMIA-NGS: microRNA-mRNA integrated analysis using high-throughput sequencing data
- Authors
- Chae, H (Chae, Heejoon); Rhee, S (Rhee, Sungmin); Nephew, KP (Nephew, Kenneth P.; Kim, S (Kim, Sun)
- Issue Date
- Jan-2015
- Publisher
- OXFORD UNIV PRESS
- Citation
- BIOINFORMATICS, v.31, no.2, pp 265 - 267
- Pages
- 3
- Journal Title
- BIOINFORMATICS
- Volume
- 31
- Number
- 2
- Start Page
- 265
- End Page
- 267
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/147204
- DOI
- 10.1093/bioinformatics/btu614
- ISSN
- 1367-4803
1367-4811
- Abstract
- 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
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