Abstract
Recent developments in Next-Generation Sequencing (NGS) technologies have opened doors for ultra high throughput sequencing mRNA (mRNA-seq) of the whole transcriptome. mRNA-seq has enabled researchers to comprehensively search for underlying biological determinants of diseases and ultimately discover novel preventive and therapeutic solutions. Unfortunately, given the complexity of mRNA-seq data, data generation has outgrown current analytical capacity, hindering the pace of research in this area. Thus, there is an urgent need to develop novel statistical methodology that addresses problems related to mRNA-seq data. This review addresses the common challenge of the presence of overdispersion in mRNA count data. We review current methods for modeling overdispersion, such as negative binomial, quasi-likelihood Poisson method, and the two-stage adaptive method; introduce related statistical theories; and discuss their applications to mRNA-seq count data.
Original language | English (US) |
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Pages (from-to) | 34-40 |
Number of pages | 7 |
Journal | Open Bioinformatics Journal |
Volume | 7 |
Issue number | SUPPL.1 |
DOIs | |
State | Published - 2013 |
Keywords
- Count response
- Negative binomial theory
- Over-dispersion
- Poisson
- Quasi-likelihood
- mRNA-seq
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Biomedical Engineering
- Health Informatics