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Methods for microRNA profiling and discovery using massively parallel sequencing

University of British Columbia

MicroRNAs (miRNAs) are emerging as important, albeit poorly characterized, regulators of gene expression. Here, I review the current knowledge of miRNAs in humans, including their biogenesis, modes of action and various methods for studying them. To fully elucidate the various functions of miRNAs in humans, we require a more complete understanding of their numbers and expression changes amongst different cell types. This document includes a description of a new method for surveying the expression of miRNAs that employs the new Illumina sequencing technology. A set of methods is presented that enables identification of sequences belonging to known miRNAs as well as variability in their mature sequences. As well, a novel system for miRNA gene discovery using these data is described. Application of this approach to RNA from human embryonic stem cells (hESCs) obtained before and after differentiation into embryoid bodies (EBs) revealed the sequences and expression levels of 362 known plus 170 novel miRNA genes. Of these, 190 known and 31 novel microRNA sequences exhibited significant expression differences between these two developmental states. Owing to the increased number of sequence reads, these libraries currently represent the deepest miRNA sampling in any human cell type spanning nearly six orders of magnitude of expression. Predicted targets of the differentially expressed miRNAs were ranked to identify those that are likely under cooperative miRNA regulation in either hESCs or EBs. The predicted targets of those miRNAs enriched in either sample shared common features. Included amongst the high-ranked predicted gene targets are those implicated in differentiation, cell cycle control, programmed cell death and transcriptional regulation. Direct validation of these predicted targets or global discovery of miRNA targets should reveal the functions of these sequences in the differentiation of hESCs.

Text

2011-02-23

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http://hdl.handle.net/2429/31683

Bioinformatics

University of British Columbia

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