Research article

Deep Sequencing of Chicken microRNAs

Joan Burnside¹ , Ming Ouyang³ , Amy Anderson¹ , Erin Bernberg¹ , Cheng Lu² , Blake C Meyers² , Pamela J Green² , Milos Markis¹ , Grace Isaacs¹ , Emily Huang¹ and Robin W Morgan¹

¹Department of Animal and Food Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, 19711, USA

²Department of Plant and Soil Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, 19711, USA

³University of Louisville, Department of Computer Engineering & Computer Science Louisville, Kentucky, 40292, USA

author email corresponding author email

BMC Genomics 2008, 9:185doi:10.1186/1471-2164-9-185

Published:	22 April 2008

Abstract

Background

The use of new, deep sequencing technologies has greatly accelerated microRNA discovery. We have applied this approach to the identification of chicken microRNAs and to the comparison of microRNAs in chicken embryo fibroblasts (CEF) infected with Marek's disease virus (MDV) to those present in uninfected CEF.

Results

We obtained 125,463 high quality reads that showed an exact match to the chicken genome. The majority of the reads corresponded to previously annotated chicken microRNAs; however, the sequences of many potential novel microsRNAs were obtained. A comparison of the reads obtained in MDV-infected and uninfected CEF indicates that infection does not significantly perturb the expression profile of microRNAs. Frequently sequenced microRNAs include miR-221/222, which are thought to play a role in growth and proliferation. A number of microRNAs (e.g., let-7, miR-199a-1, 26a) are expressed at lower levels in MDV-induced tumors, highlighting the potential importance of this class of molecules in tumorigenesis.

Conclusion

Deep sequencing technology is highly suited for small RNA discovery. This approach is independent of comparative sequence analysis, which has been the primary method used to identify chicken microRNAs. Our results have confirmed the expression of many microRNAs identified by sequence similarity and identified a pool of candidate novel microRNAs.