iMir: An integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq
- Giorgio Giurato†1,
- Maria Rosaria De Filippo†2,
- Antonio Rinaldi1,
- Adnan Hashim1,
- Giovanni Nassa1,
- Maria Ravo1,
- Francesca Rizzo1,
- Roberta Tarallo1 and
- Alessandro Weisz1, 3Email author
© Giurato et al.; licensee BioMed Central Ltd. 2013
Received: 17 September 2013
Accepted: 10 December 2013
Published: 13 December 2013
Qualitative and quantitative analysis of small non-coding RNAs by next generation sequencing (smallRNA-Seq) represents a novel technology increasingly used to investigate with high sensitivity and specificity RNA population comprising microRNAs and other regulatory small transcripts. Analysis of smallRNA-Seq data to gather biologically relevant information, i.e. detection and differential expression analysis of known and novel non-coding RNAs, target prediction, etc., requires implementation of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. As a consequence, the analytical workflow is slowed down by the need for continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once.
We designed a novel modular pipeline (iMir) for comprehensive analysis of smallRNA-Seq data, comprising specific tools for adapter trimming, quality filtering, differential expression analysis, biological target prediction and other useful options by integrating multiple open source modules and resources in an automated workflow. As statistics is crucial in deep-sequencing data analysis, we devised and integrated in iMir tools based on different statistical approaches to allow the operator to analyze data rigorously. The pipeline created here proved to be efficient and time-saving than currently available methods and, in addition, flexible enough to allow the user to select the preferred combination of analytical steps. We present here the results obtained by applying this pipeline to analyze simultaneously 6 smallRNA-Seq datasets from either exponentially growing or growth-arrested human breast cancer MCF-7 cells, that led to the rapid and accurate identification, quantitation and differential expression analysis of ~450 miRNAs, including several novel miRNAs and isomiRs, as well as identification of the putative mRNA targets of differentially expressed miRNAs. In addition, iMir allowed also the identification of ~70 piRNAs (piwi-interacting RNAs), some of which differentially expressed in proliferating vs growth arrested cells.
The integrated data analysis pipeline described here is based on a reliable, flexible and fully automated workflow, useful to rapidly and efficiently analyze high-throughput smallRNA-Seq data, such as those produced by the most recent high-performance next generation sequencers. iMir is available at http://www.labmedmolge.unisa.it/inglese/research/imir.
KeywordsNext generation sequencing SmallRNA-Seq Data analysis pipeline Breast cancer Small non-coding RNA microRNA Piwi-interacting RNA
Small RNA analysis by massively parallel sequencing (smallRNA-Seq) represents an increasingly popular method to address different questions concerning the biological role of miRNAs and other regulatory small transcripts, such as piwi-interacting (piRNAs), small inhibitory (siRNAs), transcription initiation (tiRNAs), transfer (tRNAs) and other small non-coding (sncRNAs) RNAs, including also extra-cellular small RNAs (exRNAs). Among sncRNAs, miRNAs and piRNAs are emerging as key regulators in multiple cellular functions and for this reason are widely studied by direct sequencing. miRNAs, the best know and studied class of sncRNAs, are interesting to investigate due to their ability to control gene expression in eukaryotes by fine tuning mRNA translation [1-3]. They represent a class of short (~ 22 nucleotides) RNA molecules that play pivotal roles in a variety of molecular processes, such as immune response , differentiation , development [6-8], infection [9, 10] and carcinogenesis [11-13]. miRNA genes are synthesized as long precursor RNA molecules (pri-miRNAs), usually by RNA polymerase II , that are rapidly processed in the nucleus by Drosha RNase III to release approximately 70 nucleotides long miRNA precursor stem loop (pre-miRNA)  that in turn are exported to the cytoplasm by Exportin 5 . In the cytoplasm, mature miRNAs are produced through the action of Dicer RNase . These small RNAs regulate gene expression by binding to targets sites generally in the 3′ untraslated region (3′ UTR) of target mRNAs, resulting in mRNA degradation or translation inhibition [1, 18]. miRNAs recognition of the 3′ UTR of their target mRNA is mediated by hybridization between nucleotides 2-8 at 5′ end of the small RNA (seed sequence) and the complementary sequences present in the 3′ UTR of the mRNA [1, 19, 20]. On the other hand, small non-coding RNAs that interact with Piwi proteins, called piRNAs, are emerging as regulatory transcripts able to control a broad range of biological processes. The main roles of these molecules has been investigated mainly in germline stem cells, where they are involved in: (i) regulation of transposone activity; (ii) modulation of genome epigenetic state, (iii) development and (iv) spermatogenesis . However piRNAs have been also identified in somatic cells, including human cancer cells , suggesting their possible involvement in tumors. This aspect highlights the need for sensitive and efficient bioinformatics tools to study these novel class of sncRNAs in smallRNA-Seq datasets. SmallRNA-Seq allows detection of RNAs with a high dynamic range and reliably measures small differences in RNA concentration between samples, enabling also to discover novel RNA molecules not annotated in databases. Generally, data analysis is performed by combining multiple statistical and bioinformatics tools available from different sources. Many useful programs for processing these data exist nowadays, such as RandA , Shortran , UEA sRNA Workbench , DSAP , miRTools 2.0  and miRExpress . Two main issues hamper diffusion and implementation of such programs: (i) web-based tools have some restriction on data upload; (ii) stand-alone programs often lack one or more analysis steps, such as for example prediction of novel sncRNAs. As main consequence, the analytical workflow is slowed down by the need for the continuous interventions by the operator, a critical factor when a large number of samples need to be analyzed at once. A main challenge in bioinformatics is thus to create comprehensive computational tools for handling and analyzing, in an automated manner, the huge amount of data generated by these experiments.
We describe here a modular analysis pipeline, iMir, for comprehensive analyses of smallRNA-Seq data integrating multiple open source modules and resources linked together in automated way. The pipeline allows identification of miRNAs and other sncRNAs, such as piRNAs, to perform differential expression analysis and, for miRNAs, to predict the corresponding mRNA targets. In addition, iMir provides the possibility to perform hierarchical clustering and to apply different statistical approaches to the analysis, improving discrimination of expressed sncRNAs and allows to identify those more likely to be biologically relevant. The pipeline output includes graphics and text files that are useful for a better interpretation of the results. iMir is well suited for the analysis of smallRNA-seq data obtained from animal samples. Moreover, it can be used to investigate the role of sncRNAs in plants adding the appropriate reference tracks in iMir database.
Identification and analysis of differentially expressed sncRNAs using digital data is implemented in iMir with two different methods (Module 5, Figures 1C and 2E). The first one, based on the DESeq bioconductor package , is particularly suited when biological or technical replicates are available . The second, based on quantile normalization and Fisher’s exact test to assess the statistical relevance, is specially designed for use when no replicates are available . The last iMir module (Module 6, Figure 2F) is designed to perform mRNA targets prediction of expressed, or differentially expressed, miRNAs. mRNA targets are predicted by using miRanda [38, 39], that includes current knowledge on target rules and uses a compendium of mammalian miRNAs, and TargetScan [40, 41], that computes mRNA targets by searching for the presence of 8mer and 7mer sites matching the seed region of each miRNA. iMir includes in its databases different sncRNAs, such as miRNAs, piRNAs, tRNAs, mRNAs and data from RFam for human, rat and mouse (Additional file 2: Table S1). Performance of iMir was compared with that of the individual bioinformatics tools considered by Williamson et al. , selected on the basis of their popularity highlighted by number of citations in the literature. Furthermore, the number of known (available in miRBase) and of novel (absent from the latest release of miRBase) miRNAs detected and the time required to carry to completion the whole analytical flow were evaluated on multiple datasets generated in our laboratory and available from public data repositories and then taken as indicators of iMir performance. The results obtained are in line with what previously reported , suggesting reliability of this new tool.
Results and discussion
Number of reads before and after adapter cleavage and reads mapped in each sncRNA library included in iMir
Reads after adapter Cleavage
Remaining reads mapping on the genome
Reads not assigned
Number of known RNAs and of predicted novel miRNAs identified with iMir in replicate smallRNA-Seq datasets from MCF-7 cells
Exponentially growing cells
miRNA (miRBase v.20)
tRNA (UCSC Genome Browser)
rRNA (NCBI Nucleotide)
piRNA (NCBI Nucleotide)
Novel miRNA predicted
Recently, an increasing number of studies highlighted the role of piRNAs in breast cancer [45, 46]. Since the average length of these RNAs is ~30nt (see: Figure in Additional file 3: Figure S1), smallRNA-Seq represents an efficient analytical approach to assess also absolute and relative expression of these molecules. Based on this assumption, we searched for and analyzed piRNAs in the datasets selected to test iMir performance. To reduce cross-mapping artifacts, reads corresponding to other RNAs, in particular miRNAs, tRNAs, rRNAs and mRNAs, were first filtered out with iMir mapping them against the selected transcribed RNA libraries. This allowed at once to start from a set of more reliable data and to gather information concerning other small RNAs detected by sequencing (Table 1 and Table 2). This analysis led to the identification of 70 and 85 piRNAs expressed in growth-arrested and exponentially growing MCF-7 cells, respectively. Differential piRNA expression analysis and statistical significance testing performed with iMir revealed 12 downregulated and 25 upregulated piRNAs in growing cells, when compared to quiescent ones (p-value = 0.05, threshold = 1.5; Figure 3). We do not have a ready explanation for these relatively low numbers of piRNAs identified in breast cancer cells, except for the fact that piRNAs know to date have been identified in germ cells  and it is thus possible that the majority of them is expressed only in these cell types. Furthermore, most piRNAs identified so far associate with the piRNA biogenesis factor Piwil1 (Hiwi) [21, 47], that is not detectable in MCF-7 cells, where only Piwil 2 (Hili) and Piwil4 (Hiwi2) are detected [Hashim et al., manuscript in preparation]. As new validated piRNA datasets will become available, for example those identified by association to Piwil 2 and 4, the possibility built into iMir to customize its database will allow to include these in the analysis. The decision to focus here on individual piRNAs instead of considering their genomic organization in clusters is based on the observation that in somatic cells piRNAs deriving from a given cluster show different levels of steady-state expression, possibly due to a specific mechanism of precursor RNA maturation active in these cells or to differences in their half-life. In addition, recent results suggest that individual piRNAs could play important roles in tumor cells [48-50].
We then tested another function of iMir by performing differential miRNAs expression analysis in two different ways: (i) starting directly from the number of raw read-counts obtained with miRanalyzer  or (ii) by adding to each of these counts a correction factor (31), computed as the median of the whole read dataset (see above). Once compared, the results obtained with the two approaches showed slight but substantial differences, since ~10% of the miRNAs identified with the first method (pvalue ≤ 0.05) were excluded by the second one (Additional file 4: Table S2). This is explained by the fact that the RNAs expressed at a very low level under both experimental conditions, and thus of uncertain biological significance, were filtered out when using this correction. iMir offers the possibility to choose this method, when needed, also to other classes of sncRNAs.
With respect to the possibility to perform target prediction for selected miRNAs using miRanda and TargetScan databases, another useful function of the tool, it is worth mentioning the possibility for the user to update when required these and the other databases associated to the pipeline, such as those of miRNAs from miRBase, [51-53], of other sncRNAs from different sources and of mRNA targets from TargetScan [40, 41, 54] and miRanda [38, 39].
We designed, built and describe here iMir, a pipeline that integrates multiple open source modules/resources and implements statistical approaches, combined in an automated flow for high-throughput smallRNA-Seq data analysis. iMir is rapid, accurate and efficient, allowing to examine multiple samples at once and thereby addressing a critical factor for high-throughtput analysis of sncRNA sequencing data, represented by the need for continuous interventions by an operator skilled in informatics and programming. The graphical user interface of iMir, allows a simplified use of the many tools integrated in the pipeline and to customize data analysis according to different needs. In addition, the implementation of different statistical approaches provides the possibility to analyze data according to standard, widely used, as well as to specific needs. Finally, iMir works on Linux and Mac operative systems, user-friendly for biologists with limited skills in informatics. In the future, following the evolution of NGS technologies and recommendations by the scientific community, we plan to keep improving iMir features, including for example tools for sequence variants detection, evolutionary sncRNAs analysis across multiple species and adding specific functions for analysis of emerging classes of small RNAs (pi-, si-, sn-, sno-, ti-RNA, etc.).
Availability and requirements
Project name: iMir.
Project home page:http://www.labmedmolge.unisa.it/inglese/research/imir.
Operating System(s): Unix/Linux based.
Other requirements: Python, Java, Perl, R, DESeq, Bowtie, Vienna RNA Secondary Structure package.
License: GNU GPL v3.
Any restrictions to use by non-academics: specified by GNU GPL v3.
Small non-coding RNA
Next generation sequencing.
Work supported by: Italian Ministry for Education, University and Research (Grant PRIN 2010LC747T_002 to AW and FIRB RBFR12W5V5_003 to RT), Italian Association for Cancer Research (Grant IG-13176), Fondazione Umberto Veronesi’ (Grant 2012-13), EU SeqAhead COST Action (BM1006), CNR Epigen Flagship Project (Grant 2013), University of Salerno (Fondi FARB 2011-2012). GN is supported by a ‘Mario e Valeria Rindi’ fellowship of the Italian Foundation for Cancer Research, MR is supported by a ‘Vladimir Ashkenazy’ fellowship of Italian Association for Cancer Research, FR is supported by a “Young Investigator Programme” fellowship of the Fondazione ‘Umberto Veronesi’, University of Napoli ‘Federico II’ and AH is a PhD student in “Experimental Physiopathology and Neurosciences”, Second University of Napoli.
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- He L, Hannon GJ: MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004, 5 (7): 522-531. 10.1038/nrg1379.View ArticlePubMedGoogle Scholar
- Flynt AS, Lai EC: Biological principles of microRNA-mediated regulation: shared themes amid diversity. Nat Rev Genet. 2008, 9 (11): 831-842. 10.1038/nrg2455.PubMed CentralView ArticlePubMedGoogle Scholar
- Tili E, Michaille JJ, Cimino A, Costinean S, Dumitru CD, Adair B, Fabbri M, Alder H, Liu CG, Calin GA, et al: Modulation of miR-155 and miR-125b levels following lipopolysaccharide/TNF-alpha stimulation and their possible roles in regulating the response to endotoxin shock. J Immunol. 2007, 179 (8): 5082-5089.View ArticlePubMedGoogle Scholar
- Tay YM, Tam WL, Ang YS, Gaughwin PM, Yang H, Wang W, Liu R, George J, Ng HH, Perera RJ, et al: MicroRNA-134 modulates the differentiation of mouse embryonic stem cells, where it causes post-transcriptional attenuation of Nanog and LRH1. Stem Cells. 2008, 26 (1): 17-29. 10.1634/stemcells.2007-0295.View ArticlePubMedGoogle Scholar
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294 (5543): 853-858. 10.1126/science.1064921.View ArticlePubMedGoogle Scholar
- Lau NC, Lim LP, Weinstein EG, Bartel DP: An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science. 2001, 294 (5543): 858-862. 10.1126/science.1065062.View ArticlePubMedGoogle Scholar
- Lee RC, Ambros V: An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001, 294 (5543): 862-864. 10.1126/science.1065329.View ArticlePubMedGoogle Scholar
- Gupta A, Gartner JJ, Sethupathy P, Hatzigeorgiou AG, Fraser NW: Anti-apoptotic function of a microRNA encoded by the HSV-1 latency-associated transcript. Nature. 2006, 442 (7098): 82-85.PubMedGoogle Scholar
- Jopling CL, Yi M, Lancaster AM, Lemon SM, Sarnow P: Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science. 2005, 309 (5740): 1577-1581. 10.1126/science.1113329.View ArticlePubMedGoogle Scholar
- Huang Q, Gumireddy K, Schrier M, le Sage C, Nagel R, Nair S, Egan DA, Li A, Huang G, Klein-Szanto AJ, et al: The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat Cell Biol. 2008, 10 (2): 202-210. 10.1038/ncb1681.View ArticlePubMedGoogle Scholar
- Silber J, Lim DA, Petritsch C, Persson AI, Maunakea AK, Yu M, Vandenberg SR, Ginzinger DG, James CD, Costello JF, et al: miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells. BMC Med. 2008, 6: 14-10.1186/1741-7015-6-14.PubMed CentralView ArticlePubMedGoogle Scholar
- Paris O, Ferraro L, Grober OMV, Ravo M, De Filippo MR, Giurato G, Nassa G, Tarallo R, Cantarella C, Rizzo F, et al: Direct regulation of microRNA biogenesis and expression by estrogen receptor beta in hormone-responsive breast cancer. Oncogene. 2012, 31 (38): 4196-4206. 10.1038/onc.2011.583.View ArticlePubMedGoogle Scholar
- Lee Y, Kim M, Han J, Yeom KH, Lee S, Baek SH, Kim VN: MicroRNA genes are transcribed by RNA polymerase II. EMBO J. 2004, 23 (20): 4051-4060. 10.1038/sj.emboj.7600385.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Radmark O, Kim S, et al: The nuclear Rnase III Drosha initiates microRNA processing. Nature. 2003, 425 (6956): 415-419. 10.1038/nature01957.View ArticlePubMedGoogle Scholar
- Lund E, Guttinger S, Calado A, Dahlberg JE, Kutay U: Nuclear export of microRNA precursors. Science. 2004, 303 (5654): 95-98. 10.1126/science.1090599.View ArticlePubMedGoogle Scholar
- Hutvagner G, McLachlan J, Pasquinelli AE, Balint E, Tuschl T, Zamore PD: A cellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA. Science. 2001, 293 (5531): 834-838. 10.1126/science.1062961.View ArticlePubMedGoogle Scholar
- Nilsen TW: Mechanisms of microRNA-mediated gene regulation in animal cells. Trends Genet. 2007, 23 (5): 243-249. 10.1016/j.tig.2007.02.011.View ArticlePubMedGoogle Scholar
- Ambros V: The functions of animal microRNAs. Nature. 2004, 431 (7006): 350-355. 10.1038/nature02871.View ArticlePubMedGoogle Scholar
- Zamore PD, Haley B: Ribo-gnome: the big world of small RNAs. Science. 2005, 309 (5740): 1519-1524. 10.1126/science.1111444.View ArticlePubMedGoogle Scholar
- Luteijn MJ, Ketting RF: PIWI-interacting RNAs: from generation to transgenerational epigenetics. Nat Rev Genet. 2013, 14 (8): 523-534. 10.1038/nrg3495.View ArticlePubMedGoogle Scholar
- Cheng J, Guo JM, Xiao BX, Miao Y, Jiang Z, Zhou H, Li QN: piRNA, the new non-coding RNA, is aberrantly expressed in human cancer cells. Clin Chim Acta. 2011, 412 (17-18): 1621-1625.View ArticlePubMedGoogle Scholar
- Isakov O, Ronen R, Kovarsky J, Gabay A, Gan I, Modai S, Shomron N: Novel nsight into the non-coding reperto ire through deep sequencing analysis. Nucleic Acids Res. 2012, 10: 1093-Google Scholar
- Gupta V, Markmann K, Pedersen CN, Stougaard J, Andersen SU: shortran: a pipeline for small RNA-seq data analysis. Bioinformatics. 2012, 28 (20): 2698-2700. 10.1093/bioinformatics/bts496.PubMed CentralView ArticlePubMedGoogle Scholar
- Stocks MB, Moxon S, Mapleson D, Woolfenden HC, Mohorianu I, Folkes L, Schwach F, Dalmay T, Moulton V: The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics. 2012, 28 (15): 2059-2061. 10.1093/bioinformatics/bts311.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang PJ, Liu YC, Lee CC, Lin WC, Gan RR, Lyu PC, Tang P: DSAP: deep-sequencing small RNA analysis pipeline. Nucleic Acids Res. 2010, 38: W385-W391. 10.1093/nar/gkq392. Web Server issuePubMed CentralView ArticlePubMedGoogle Scholar
- Wu J, Liu Q, Wang X, Zheng J, Wang T, You M, Sheng Sun Z, Shi Q: mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol. 2013, 10 (7): 1087-1092. 10.4161/rna.25193.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang WC, Lin FM, Chang WC, Lin KY, Huang HD, Lin NS: miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression. BMC bioinformatics. 2009, 10: 328-10.1186/1471-2105-10-328.PubMed CentralView ArticlePubMedGoogle Scholar
- Cordero F, Beccuti M, Arigoni M, Donatelli S, Calogero RA: Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis. PLoS One. 2012, 7 (2): e31630-10.1371/journal.pone.0031630.PubMed CentralView ArticlePubMedGoogle Scholar
- Williamson V, Kim A, Xie B, McMichael GO, Gao Y, Vladimirov V: Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation. Brief Bioinform. 2013, 14 (1): 36-45. 10.1093/bib/bbs010.PubMed CentralView ArticlePubMedGoogle Scholar
- Martin M: Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011, 17: 1-View ArticleGoogle Scholar
- Hackenberg M, Rodriguez-Ezpeleta N, Aransay AM: miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res. 2011, 39: W132-W138. 10.1093/nar/gkr247. Web Server issuePubMed CentralView ArticlePubMedGoogle Scholar
- Morin RD, O’Connor MD, Griffith M, Kuchenbauer F, Delaney A, Prabhu AL, Zhao Y, McDonald H, Zeng T, Hirst M, et al: Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 2008, 18 (4): 610-621. 10.1101/gr.7179508.PubMed CentralView ArticlePubMedGoogle Scholar
- Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012, 40 (1): 37-52. 10.1093/nar/gkr688.PubMed CentralView ArticlePubMedGoogle Scholar
- Wee LM, Flores-Jasso CF, Salomon WE, Zamore PD: Argonaute divides its RNA guide into domains with distinct functions and RNA-binding properties. Cell. 2012, 151 (5): 1055-1067. 10.1016/j.cell.2012.10.036.PubMed CentralView ArticlePubMedGoogle Scholar
- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11 (10): R106-10.1186/gb-2010-11-10-r106.PubMed CentralView ArticlePubMedGoogle Scholar
- Garmire LX, Subramaniam S: Evaluation of normalization methods in mammalian microRNA-Seq data. RNA. 2012, 18 (6): 1279-1288. 10.1261/rna.030916.111.PubMed CentralView ArticlePubMedGoogle Scholar
- Betel D, Koppal A, Agius P, Sander C, Leslie C: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 2010, 11 (8): R90-10.1186/gb-2010-11-8-r90.PubMed CentralView ArticlePubMedGoogle Scholar
- Betel D, Wilson M, Gabow A, Marks DS, Sander C: The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008, 36: D149-D153. Database issuePubMed CentralView ArticlePubMedGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120 (1): 15-20. 10.1016/j.cell.2004.12.035.View ArticlePubMedGoogle Scholar
- Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007, 27 (1): 91-105. 10.1016/j.molcel.2007.06.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Cicatiello L, Mutarelli M, Grober OM, Paris O, Ferraro L, Ravo M, Tarallo R, Luo S, Schroth GP, et al: Estrogen receptor alpha controls a gene network in luminal-like breast cancer cells comprising multiple transcription factors and microRNAs. Am J Pathol. 2010, 176 (5): 2113-2130. 10.2353/ajpath.2010.090837.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferraro L, Ravo M, Nassa G, Tarallo R, De Filippo MR, Giurato G, Cirillo F, Stellato C, Silvestro S, Cantarella C, et al: Effects of estrogen on microRNA expression in hormone-responsive breast cancer cells. Horm Cancer. 2011, 2 (5): 610-621.Google Scholar
- Burge SW, Daub J, Eberhardt R, Tate J, Barquist L, Nawrocki EP, Eddy SR, Gardner PP, Bateman A: Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 2013, 41: D226-D232. 10.1093/nar/gks1005. Database issuePubMed CentralView ArticlePubMedGoogle Scholar
- Esposito T, Magliocca S, Formicola D, Gianfrancesco F: piR_015520 belongs to Piwi-associated RNAs regulates expression of the human melatonin receptor 1A gene. PLoS One. 2011, 6 (7): e22727-10.1371/journal.pone.0022727.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang G, Hu H, Xue X, Shen S, Gao E, Guo G, Shen X, Zhang X: Altered expression of piRNAs and their relation with clinicopathologic features of breast cancer. Clin Transl Oncol. 2012, [Epub ahead of print]Google Scholar
- Aravin A, Gaidatzis D, Pfeffer S, Lagos-Quintana M, Landgraf P, Iovino N, Morris P, Brownstein MJ, Kuramochi-Miyagawa S, Nakano T, Chien M, Russo JJ, Ju J, Sheridan R, Sander C, Zavolan M, Tuschl T: A novel class of small RNAs bind to MILI protein in mouse testes. Nature. 2006, 442 (7099): 203-207.PubMedGoogle Scholar
- Cheng J, Deng H, Xiao B, Zhou H, Zhou F, Shen Z, Guo J: piR-823, a novel non-coding small RNA, demonstrates in vitro and in vivo tumor suppressive activity in human gastric cancer cells. Cancer Lett. 2012, 315 (1): 12-17. 10.1016/j.canlet.2011.10.004.View ArticlePubMedGoogle Scholar
- Huang G, Hu H, Xue X, Shen S, Gao E, Guo G, Shen X, Zhang X: Altered expression of piRNAs and their relation with clinicopathologic features of breast cancer. Clin Transl Oncol. 2013, 15 (7): 563-568. 10.1007/s12094-012-0966-0.View ArticlePubMedGoogle Scholar
- Law PT, Qin H, Ching AK, Lai KP, Co NN, He M, Lung RW, Chan AW, Chan TF, Wong N: Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma. J Hepatol. 2013, 58 (6): 1165-1173. 10.1016/j.jhep.2013.01.032.View ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39: D152-D157. 10.1093/nar/gkq1027. Database issuePubMed CentralView ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006, 34: D140-D144. 10.1093/nar/gkj112. Database issue)PubMed CentralView ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36: D154-D158. 10.1093/nar/gkn221. Database issuePubMed CentralView ArticlePubMedGoogle Scholar
- Friedman RC, Farh KK, Burge CB, Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009, 19 (1): 92-105.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.