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Briefings in Bioinformatics Advance Access originally published online on March 30, 2009
Briefings in Bioinformatics 2009 10(5):490-497; doi:10.1093/bib/bbp019
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© The Author 2009. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Expression profiling of microRNAs by deep sequencing

Chad J. Creighton, Jeffrey G. Reid and Preethi H. Gunaratne

Corresponding author. Chad J. Creighton, PhD, Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, One Baylor Plaza MS 305, Houston, TX 77030, USA. Tel: +1 713 798 2264; Fax: +1 713 798 2716; E-mail: creighto{at}bcm.edu

MicroRNAs are short non-coding RNAs that regulate the stability and translation of mRNAs. Profiling experiments, using microarray or deep sequencing technology, have identified microRNAs that are preferentially expressed in certain tissues, specific stages of development, or disease states such as cancer. Deep sequencing utilizes massively parallel sequencing, generating millions of small RNA sequence reads from a given sample. Profiling of microRNAs by deep sequencing measures absolute abundance and allows for the discovery of novel microRNAs that have eluded previous cloning and standard sequencing efforts. Public databases provide in silico predictions of microRNA gene targets by various algorithms. To better determine which of these predictions represent true positives, microRNA expression data can be integrated with gene expression data to identify putative microRNA:mRNA functional pairs. Here we discuss tools and methodologies for the analysis of microRNA expression data from deep sequencing.

Keywords: deep sequencing, expression profiling, microRNA

Submitted: January 14, 2009. Received (in revised form): March 3, 2009.


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[Abstract] [Full Text] [PDF]


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