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Open Access Highly Accessed Research article

Analysis of microRNA transcriptome by deep sequencing of small RNA libraries of peripheral blood

Candida Vaz1, Hafiz M Ahmad2, Pratibha Sharma3, Rashi Gupta1, Lalit Kumar3, Ritu Kulshreshtha2 and Alok Bhattacharya12*

Author Affiliations

1 School of Information Technology, Jawaharlal Nehru University, New Delhi, India

2 School of Life Sciences, Jawaharlal Nehru University, New Delhi, India

3 Department of Medical Oncology, Institute Rotary Cancer Hospital, All India Institute of Medical Science, New Delhi, India

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BMC Genomics 2010, 11:288  doi:10.1186/1471-2164-11-288

Published: 7 May 2010

Abstract

Background

MicroRNAs are a class of small non-coding RNAs that regulate mRNA expression at the post - transcriptional level and thereby many fundamental biological processes. A number of methods, such as multiplex polymerase chain reaction, microarrays have been developed for profiling levels of known miRNAs. These methods lack the ability to identify novel miRNAs and accurately determine expression at a range of concentrations. Deep or massively parallel sequencing methods are providing suitable platforms for genome wide transcriptome analysis and have the ability to identify novel transcripts.

Results

The results of analysis of small RNA sequences obtained by Solexa technology of normal peripheral blood mononuclear cells, tumor cell lines K562 and HL60 are presented. In general K562 cells displayed overall low level of miRNA population and also low levels of DICER. Some of the highly expressed miRNAs in the leukocytes include several members of the let-7 family, miR-21, 103, 185, 191 and 320a. Comparison of the miRNA profiles of normal versus K562 or HL60 cells revealed a specific set of differentially expressed molecules. Correlation of the miRNA with that of mRNA expression profiles, obtained by microarray, revealed a set of target genes showing inverse correlation with miRNA levels. Relative expression levels of individual miRNAs belonging to a cluster were found to be highly variable. Our computational pipeline also predicted a number of novel miRNAs. Some of the predictions were validated by Real-time RT-PCR and or RNase protection assay. Organization of some of the novel miRNAs in human genome suggests that these may also be part of existing clusters or form new clusters.

Conclusions

We conclude that about 904 miRNAs are expressed in human leukocytes. Out of these 370 are novel miRNAs. We have identified miRNAs that are differentially regulated in normal PBMC with respect to cancer cells, K562 and HL60. Our results suggest that post - transcriptional processes may play a significant role in regulating levels of miRNAs in tumor cells. The study also provides a customized automated computation pipeline for miRNA profiling and identification of novel miRNAs; even those that are missed out by other existing pipelines. The Computational Pipeline is available at the website: http://mirna.jnu.ac.in/deep_sequencing/deep_sequencing.html webcite