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      Accurate detection for a wide range of mutation and editing sites of microRNAs from small RNA high-throughput sequencing profiles

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          Abstract

          Various types of mutation and editing (M/E) events in microRNAs (miRNAs) can change the stabilities of pre-miRNAs and/or complementarities between miRNAs and their targets. Small RNA (sRNA) high-throughput sequencing (HTS) profiles can contain many mutated and edited miRNAs. Systematic detection of miRNA mutation and editing sites from the huge volume of sRNA HTS profiles is computationally difficult, as high sensitivity and low false positive rate (FPR) are both required. We propose a novel method (named MiRME) for an accurate and fast detection of miRNA M/E sites using a progressive sequence alignment approach which refines sensitivity and improves FPR step-by-step. From 70 sRNA HTS profiles with over 1.3 billion reads, MiRME has detected thousands of statistically significant M/E sites, including 3′-editing sites, 57 A-to-I editing sites (of which 32 are novel), as well as some putative non-canonical editing sites. We demonstrated that a few non-canonical editing sites were not resulted from mutations in genome by integrating the analysis of genome HTS profiles of two human cell lines, suggesting the existence of new editing types to further diversify the functions of miRNAs. Compared with six existing studies or methods, MiRME has shown much superior performance for the identification and visualization of the M/E sites of miRNAs from the ever-increasing sRNA HTS profiles.

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          Most cited references36

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Genetic variation in microRNA networks: the implications for cancer research.

            Many studies have highlighted the role that microRNAs have in physiological processes and how their deregulation can lead to cancer. More recently, it has been proposed that the presence of single nucleotide polymorphisms in microRNA genes, their processing machinery and target binding sites affects cancer risk, treatment efficacy and patient prognosis. In reviewing this new field of cancer biology, we describe the methodological approaches of these studies and make recommendations for which strategies will be most informative in the future.
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              Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells.

              MicroRNAs (miRNAs) are emerging as important, albeit poorly characterized, regulators of biological processes. Key to further elucidation of their roles is the generation of more complete lists of their numbers and expression changes in different cell states. Here, we report a new method for surveying the expression of small RNAs, including microRNAs, using Illumina sequencing technology. We also present a set of methods for annotating sequences deriving from known miRNAs, identifying variability in mature miRNA sequences, and identifying sequences belonging to previously unidentified miRNA genes. Application of this approach to RNA from human embryonic stem cells obtained before and after their differentiation into embryoid bodies revealed the sequences and expression levels of 334 known plus 104 novel miRNA genes. One hundred seventy-one known and 23 novel microRNA sequences exhibited significant expression differences between these two developmental states. Owing to the increased number of sequence reads, these libraries represent the deepest miRNA sampling to date, spanning nearly six orders of magnitude of expression. The predicted targets of those miRNAs enriched in either sample shared common features. Included among the high-ranked predicted gene targets are those implicated in differentiation, cell cycle control, programmed cell death, and transcriptional regulation.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                19 August 2016
                26 May 2016
                26 May 2016
                : 44
                : 14
                : e123
                Affiliations
                [1 ]Faculty of Life Science and Technology, Kunming University of Science and Technology Kunming, Yunnan 650500, China
                [2 ]Faculty of Information Engineering and Automation, Kunming University of Science and Technology Kunming, Yunnan 650500, China
                [3 ]Advanced Analytics Institute & Centre for Health Technologies, Faculty of Engineering & IT University of Technology Sydney, Australia
                [4 ]Yunnan Key Lab of Primate Biomedicine Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +86 871 65918047; Fax: +86 871 65920570; Email: zhengyun5488@ 123456gmail.com
                Correspondence may also be addressed Jinyan Li. Tel: +61 2 95149264; Email: jinyan.li@ 123456uts.edu.au
                Article
                10.1093/nar/gkw471
                5001599
                27229138
                65733d83-d026-42ee-b144-d3a98f5a32c6
                © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 13 May 2016
                : 09 May 2016
                : 30 September 2015
                Page count
                Pages: 16
                Categories
                15
                Methods Online
                Custom metadata
                19 August 2016

                Genetics
                Genetics

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