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. 2008 May 21:9:236.
doi: 10.1186/1471-2164-9-236.

MicroRNA-encoding long non-coding RNAs

Shunmin He  1 , Hua Su, Changning Liu, Geir Skogerbø, Housheng He, Dandan He, Xiaopeng Zhu, Tao Liu, Yi Zhao, Runsheng Chen
Affiliations

MicroRNA-encoding long non-coding RNAs

Shunmin He et al. BMC Genomics. .

Abstract

Background: Recent analysis of the mouse transcriptional data has revealed the existence of approximately 34,000 messenger-like non-coding RNAs (ml-ncRNAs). Whereas the functional properties of these ml-ncRNAs are beginning to be unravelled, no functional information is available for the large majority of these transcripts.

Results: A few ml-ncRNA have been shown to have genomic loci that overlap with microRNA loci, leading us to suspect that a fraction of ml-ncRNA may encode microRNAs. We therefore developed an algorithm (PriMir) for specifically detecting potential microRNA-encoding transcripts in the entire set of 34,030 mouse full-length ml-ncRNAs. In combination with mouse-rat sequence conservation, this algorithm detected 97 (80 of them were novel) strong miRNA-encoding candidates, and for 52 of these we obtained experimental evidence for the existence of their corresponding mature microRNA by microarray and stem-loop RT-PCR. Sequence analysis of the microRNA-encoding RNAs revealed an internal motif, whose presence correlates strongly (R2 = 0.9, P-value = 2.2 x 10(-16)) with the occurrence of stem-loops with characteristics of known pre-miRNAs, indicating the presence of a larger number microRNA-encoding RNAs (from 300 up to 800) in the ml-ncRNAs population.

Conclusion: Our work highlights a unique group of ml-ncRNAs and offers clues to their functions.

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Figures

Figure 1
Figure 1
The PriMir pipeline. Prediction and verification of Nmir_018. A. The secondary structure of ml-ncRNA A530020N14 as predicted by RNAfold [44, 45]. B. Among the six hairpins extracted from the ml-ncRNA, four are conserved between mouse and rat (blue frame). The green dashed line indicates the pre-miRNA 5' and 3' end positions predicted by PriMir. Of the four conserved hairpins, one had a PriMir score above 7, and was regarded as a pre-miRNA candidate (pre-Nmir_018; red frame). C. The upper part of the panel shows the predicted pre-Nmir_018, red color letters indicating the position of the mature miRNA Nmir_018. The lower part shows the microarray slide (left) with a positive signal for Nmir_018 (white frame), and an agarose gel electrophoresis (right) of the loop-stem RT-PCR fragment for Nmir_018 (white frame).
Figure 2
Figure 2
The internal motif (IM) of the me-ncRNAs. A: Logo of the IM (WebLogo [52]). B: The relationship between IM frequency and PriMir Score. The ml-ncRNAs were binned according to their PriMir score, and the fraction of transcripts with the IM were plotted. Blue circles: All ml-ncRNAs. Red triangles: ml-ncRNAs with conserved stem-loops. C: Frequency of IM in experimentally supported me-ncRNAs, unsupported ME-ncRNA candidates, other ml-ncRNAs, and in flanking sequence of intronic miRNAs.
Figure 3
Figure 3
The distribution of PriMir scores of the training set and the potential pre-miRNA candidates. Red columns: the 220 known pre-miRNAs in the training set; blue columns: the 23 known pre-miRNAs in ml-ncRNAs; green columns: the predicted 4463 conserved hairpins. The red arrow indicates the cutoff value "7" PirMir used to predict candidate pre-miRNAs. Hundred and sixty-one (73%) of the 220 known pre-miRNAs fall above this cutoff. Of the 23 known pre-miRNAs located in ml-ncRNAs, 18 (78%) have scores higher than 7.
See this image and copyright information in PMC

References

    1. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, Kodzius R, Shimokawa K, Bajic VB, Brenner SE, Batalov S, Forrest AR, Zavolan M, Davis MJ, Wilming LG, Aidinis V, Allen JE, Ambesi-Impiombato A, Apweiler R, Aturaliya RN, Bailey TL, Bansal M, Baxter L, Beisel KW, Bersano T, Bono H, Chalk AM, Chiu KP, Choudhary V, Christoffels A, Clutterbuck DR, Crowe ML, Dalla E, Dalrymple BP, de Bono B, Della Gatta G, di Bernardo D, Down T, Engstrom P, Fagiolini M, Faulkner G, Fletcher CF, Fukushima T, Furuno M, Futaki S, Gariboldi M, Georgii-Hemming P, Gingeras TR, Gojobori T, Green RE, Gustincich S, Harbers M, Hayashi Y, Hensch TK, Hirokawa N, Hill D, Huminiecki L, Iacono M, Ikeo K, Iwama A, Ishikawa T, Jakt M, Kanapin A, Katoh M, Kawasawa Y, Kelso J, Kitamura H, Kitano H, Kollias G, Krishnan SP, Kruger A, Kummerfeld SK, Kurochkin IV, Lareau LF, Lazarevic D, Lipovich L, Liu J, Liuni S, McWilliam S, Madan Babu M, Madera M, Marchionni L, Matsuda H, Matsuzawa S, Miki H, Mignone F, Miyake S, Morris K, Mottagui-Tabar S, Mulder N, Nakano N, Nakauchi H, Ng P, Nilsson R, Nishiguchi S, Nishikawa S, Nori F, Ohara O, Okazaki Y, Orlando V, Pang KC, Pavan WJ, Pavesi G, Pesole G, Petrovsky N, Piazza S, Reed J, Reid JF, Ring BZ, Ringwald M, Rost B, Ruan Y, Salzberg SL, Sandelin A, Schneider C, Schonbach C, Sekiguchi K, Semple CA, Seno S, Sessa L, Sheng Y, Shibata Y, Shimada H, Shimada K, Silva D, Sinclair B, Sperling S, Stupka E, Sugiura K, Sultana R, Takenaka Y, Taki K, Tammoja K, Tan SL, Tang S, Taylor MS, Tegner J, Teichmann SA, Ueda HR, van Nimwegen E, Verardo R, Wei CL, Yagi K, Yamanishi H, Zabarovsky E, Zhu S, Zimmer A, Hide W, Bult C, Grimmond SM, Teasdale RD, Liu ET, Brusic V, Quackenbush J, Wahlestedt C, Mattick JS, Hume DA, Kai C, Sasaki D, Tomaru Y, Fukuda S, Kanamori-Katayama M, Suzuki M, Aoki J, Arakawa T, Iida J, Imamura K, Itoh M, Kato T, Kawaji H, Kawagashira N, Kawashima T, Kojima M, Kondo S, Konno H, Nakano K, Ninomiya N, Nishio T, Okada M, Plessy C, Shibata K, Shiraki T, Suzuki S, Tagami M, Waki K, Watahiki A, Okamura-Oho Y, Suzuki H, Kawai J, Hayashizaki Y. The transcriptional landscape of the mammalian genome. Science. 2005;309:1559–1563. doi: 10.1126/science.1112014. - DOI - PubMed
    1. Ota T, Suzuki Y, Nishikawa T, Otsuki T, Sugiyama T, Irie R, Wakamatsu A, Hayashi K, Sato H, Nagai K, Kimura K, Makita H, Sekine M, Obayashi M, Nishi T, Shibahara T, Tanaka T, Ishii S, Yamamoto J, Saito K, Kawai Y, Isono Y, Nakamura Y, Nagahari K, Murakami K, Yasuda T, Iwayanagi T, Wagatsuma M, Shiratori A, Sudo H, Hosoiri T, Kaku Y, Kodaira H, Kondo H, Sugawara M, Takahashi M, Kanda K, Yokoi T, Furuya T, Kikkawa E, Omura Y, Abe K, Kamihara K, Katsuta N, Sato K, Tanikawa M, Yamazaki M, Ninomiya K, Ishibashi T, Yamashita H, Murakawa K, Fujimori K, Tanai H, Kimata M, Watanabe M, Hiraoka S, Chiba Y, Ishida S, Ono Y, Takiguchi S, Watanabe S, Yosida M, Hotuta T, Kusano J, Kanehori K, Takahashi-Fujii A, Hara H, Tanase TO, Nomura Y, Togiya S, Komai F, Hara R, Takeuchi K, Arita M, Imose N, Musashino K, Yuuki H, Oshima A, Sasaki N, Aotsuka S, Yoshikawa Y, Matsunawa H, Ichihara T, Shiohata N, Sano S, Moriya S, Momiyama H, Satoh N, Takami S, Terashima Y, Suzuki O, Nakagawa S, Senoh A, Mizoguchi H, Goto Y, Shimizu F, Wakebe H, Hishigaki H, Watanabe T, Sugiyama A, Takemoto M, Kawakami B, Yamazaki M, Watanabe K, Kumagai A, Itakura S, Fukuzumi Y, Fujimori Y, Komiyama M, Tashiro H, Tanigami A, Fujiwara T, Ono T, Yamada K, Fujii Y, Ozaki K, Hirao M, Ohmori Y, Kawabata A, Hikiji T, Kobatake N, Inagaki H, Ikema Y, Okamoto S, Okitani R, Kawakami T, Noguchi S, Itoh T, Shigeta K, Senba T, Matsumura K, Nakajima Y, Mizuno T, Morinaga M, Sasaki M, Togashi T, Oyama M, Hata H, Watanabe M, Komatsu T, Mizushima-Sugano J, Satoh T, Shirai Y, Takahashi Y, Nakagawa K, Okumura K, Nagase T, Nomura N, Kikuchi H, Masuho Y, Yamashita R, Nakai K, Yada T, Nakamura Y, Ohara O, Isogai T, Sugano S. Complete sequencing and characterization of 21,243 full-length human cDNAs. Nat Genet. 2004;36:40–45. doi: 10.1038/ng1285. - DOI - PubMed
    1. Erdmann VA, Szymanski M, Hochberg A, de Groot N, Barciszewski J. Collection of mRNA-like non-coding RNAs. Nucleic Acids Res. 1999;27:192–195. doi: 10.1093/nar/27.1.192. - DOI - PMC - PubMed
    1. Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, Rinn JL, Tongprasit W, Samanta M, Weissman S, Gerstein M, Snyder M. Global identification of human transcribed sequences with genome tiling arrays. Science. 2004;306:2242–2246. doi: 10.1126/science.1103388. - DOI - PubMed
    1. Marahrens Y, Loring J, Jaenisch R. Role of the Xist gene in X chromosome choosing. Cell. 1998;92:657–664. doi: 10.1016/S0092-8674(00)81133-2. - DOI - PubMed

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