Genome-wide survey of miRNAs and their evolutionary history in the ascidian, Halocynthia roretzi
© The Author(s). 2017
Received: 5 December 2016
Accepted: 12 April 2017
Published: 20 April 2017
miRNAs play essential roles in the modulation of cellular functions via degradation and/or translation attenuation of target mRNAs. They have been surveyed in a single ascidian genus, Ciona. Recently, an annotated draft genome sequence for a distantly related ascidian, Halocynthia roretzi, has become available, but miRNAs in H. roretzi have not been previously studied.
We report the prediction of 319 candidate H. roretzi miRNAs, obtained through three complementary methods. Experimental validation suggests that more than half of these candidate miRNAs are expressed during embryogenesis. The majority of predicted H. roretzi miRNAs appear specific to ascidians or tunicates, and only 32 candidates, belonging to 25 families, are widely conserved across metazoans.
Our study presents a comprehensive identification of candidate H. roretzi miRNAs. This resource will facilitate the study of the mechanisms for miRNA-controlled gene regulatory networks during ascidian development. Further, our analysis suggests that the majority of Halocynthia miRNAs are specific to ascidian or tunicates, with only a small number of widely conserved miRNAs. This result is consistent with the general notion that animal miRNAs are less conserved between taxa than plant ones.
KeywordsAscidian Halocynthia roretzi miRNA Genome-wide survey miRNA targets
miRNAs are a class of short endogenous non-coding regulatory RNAs whose length is approximately 22 nt. They modulate various biological processes, such as cellular differentiation, proliferation, apoptosis, development and homeostasis [1–4]. They act by repressing translation or destabilizing target mRNA, thereby providing an additional layer of control in gene regulatory networks . In animals, a seed sequence is present at nucleotides 2–7 of the mature sequence and is a major determinant of miRNA targeting specificity. miRNAs sharing the same seed are considered to belong to the same family . miRNA genes generally locate in non-coding intergenic or intronic regions , with some rare cases found in protein-coding regions . The activity of miRNA genes is often restricted to specific developmental stages or tissues, and their expression is sometimes only stimulated by environmental cues such as temperature [9, 10], oxidative , salt or drought  stresses. While high-throughput small RNA sequencing (miRNA-seq) [13–16] provides a powerful approach for miRNA identification, their restricted expression makes it difficult to use this method to exhaustively survey miRNAs in a given species. Potential miRNA genes can also be computationally predicted in whole genome sequences, and this could usefully complement miRNA-seq approaches.
Ascidians (Phylum: Chordata, Subphylum: Tunicata, Class: Ascidiacea) have been used as model species in development biology for over a century [17, 18]. These species offer attractive experimental features, including a compact genome (e.g. Halocynthia roretzi genome is around 170 Mb with about 16,000 protein-coding genes ), invariant embryonic cell lineages, small embryonic cell number, and translucent embryos, which allow the description of developmental processes with a cellular level of resolution. 15 years ago, the complete genome sequences of two ascidian species, Ciona robusta  (formerly Ciona intestinalis Type A ) and Ciona savignyi  were assembled, annotated and made publicly accessible through genome browsers . Since then, the genomes of additional tunicate species have been sequenced, partially annotated and publicly released [19, 23–26], opening the way to a study of the evolution of ascidian coding and non-coding genetic elements. It is generally considered that ascidians are subject to rapid molecular evolution, in both coding and non-coding sequences [27, 28].
Recently, many miRNAs have been described in C. robusta and C. savignyi (Order: Phlebobranchia) [29–33]. Over 400 miRNA candidates were predicted  and the expression of 380 of them was experimentally detected in C. robusta by miRNA-seq and microarray data [31, 32]. Some C. robusta miRNAs control development processes . For example, miR-124 promotes neuronal development via the inhibition of Notch signaling [34, 35], while miR-1 and miR-133 have muscle-specific functions, as in vertebrates.
In this study, we performed a comprehensive search for miRNA in H. roretzi, an ascidian of a different order, the stolidobranchia, using a recently sequenced and annotated genome draft . Three major approaches were used to predict miRNAs: conservation to miRNA described in miRBase, de novo miRNAs prediction, and similarity to Ciona small RNA-seq reads. A total of 319 miRNA genes were discovered, whose evolutionary conservation was studied. This study thus advances our understanding of the complex gene regulatory network of ascidian embryos and will facilitate future developmental biology studies.
61 miRBase metazoan miRNAs are conserved in Halocynthia and approximately half of them may be ascidian or tunicate-specific
Conserved miRNAs in H. roretzi
Location in Scaffolds (S) and the orientation (+,-)
Mature sequences (5′-3′)
Precursor Length (nt)
The phylogenetic distribution of these H. roretzi 49 miRNA families in miRBase was next examined (Fig. 1 and Additional file 2). 25 families were highly conserved across metazoa, including let-7 and miR-7 to -367 (Fig. 1). Of these, 18 families were found in both deuterostomes and protostomes and may thus represent ancestral metazoan miRNAs. Seven families were exclusively found in deuterostomes, in either only chordates (6) or in both chordates and ambulacraria. We attribute the absence of miR-218 from Ciona and Oikopleura to the possible restricted expression of this miRNA, which may have precluded its identification by miRNA-seq. Interestingly, an ancestral metazoan miRNA, miR-281, appears to have been specifically lost from the vertebrate lineage, as it is present in all surveyed tunicates, amphioxus, and protostomes. The loss in echinoderms is not clear, since the number of species is only three.
Twenty one families were found in the distantly related C. robusta and H. roretzi ascidians but not in other animals (labeled in green on Fig. 1), and may thus correspond to ascidian or tunicate-specific miRNAs. These miRNAs are all represented by at least 10 reads in the Ciona small RNA sequencing dataset  (BLASTN, word size of 15, and E-value ≤1000), suggesting that they are expressed during C. robusta embryogenesis and are therefore likely to be genuine miRNAs. Finally, three miRNA families, miR-3182, miR-3876 and miR-7238 were only found in H. roretzi and a single non-tunicate species (Fig. 1, right most three columns). Our confidence in the predictions of these miRNAs is more limited.
To test whether the small number of candidate Halocynthia miRNAs conserved across metazoa reflected a low sensitivity in our identification method, or the overall weak conservation of ascidian miRNAs, we checked the situation in C. robusta. A total of 348 C. robusta miRNAs, belonging to 285 families, were previously identified and deposited in miRBase. These miRNAs were predicted from miRNA-seq data collected from Ciona embryos at the gastrula and larval stages, using the miRTRAP computational program , a method that makes no hypothesis on the evolutionary conservation of these candidates. Of these 348 miRNAs, only 47 miRNAs, belonging to 36 families, were widely conserved in many non-tunicate metazoan species (Additional file 3). An additional six miRNAs (Cirobu-mir-1473, Cirobu-mir-1497, Cirobu-mir-1502a, Cirobu-mir-1502b, Cirobu-mir-1502c, Cirobu-mir-1502d) belonging to three families were found in at least one tunicate species other than C. robusta (miRNA data had so far been described in three tunicate species, C. robusta [29–32], Ciona savignyi, 43 miRNAs in miRBase [32, 36, 37], and Oikopleura dioica, 69 miRNAs reported, [36, 37]). miRNA candidate Cirobu-mir-3575 was also found in Rattus norvegicus. The remaining 294 C. robusta candidate miRNAs (belonging to 239 families) appeared to be specific for C. robusta.
The evolutionary analysis of these Ciona miRNAs, and the small number of Halocynthia candidate miRNA detected by conservation to miRBase entries suggest that a majority of ascidian miRNAs may be either ascidian or tunicate-specific. Discriminating between these two possibilities is currently difficult as the number of miRNA reported so far in the non-ascidian tunicate Oikopleura dioica (n = 69) are small, suggesting that the list could be far from complete. Similarly, the current repertoire of Ciona savignyi miRNA (n = 43) is incomplete, explaining the small number of the miRNAs for this species listed in Fig. 1.
De novo miRNAs prediction
Analysis of C. robusta small RNA-Seq data confirms de novo miRNA predictions
As the first test of our de novo predictions of Halocynthia miRNAs, we next made use of a previously generated set of C. robusta small RNA-Seq reads . These reads, which are enriched in miRNAs, but could also include other classes of small non-coding RNAs , were mapped onto the H. roretzi reference genome using BLASTN (see Methods), and hits flanked by sequences whose minimum folding free energy (MFE) and stem-loop structure passed our filtration criteria were selected for further analysis (see Methods section for details). This identified 268 novel candidate miRNAs. Remarkably this set included 236 of the 268 candidate miRNA predicted by our de novo approach, of which 38 were also detected using miRBase data (Fig. 3; Additional file 1; ID numbers of the novel H. roretzi miRNAs that were detected in these ways are from 5000 to 5229). Figure 4a shows the stem-loop structures of a selection of these novel miRNA genes, with high small RNA-Seq reads (≥50 reads) support. Figure 4b showed stem-loop structures of miRNA genes with weak small RNA-Seq reads (≤10 reads) support, which represents low confidence predictions. The mature sequences of these H. roretzi miRNAs were inferred via the matched position of C. robusta small RNA-Seq reads (Fig. 4, red letters).
Interestingly mature miRNA sequences appear to have diverged significantly between Ciona and Halocynthia. Of the 230 novel predicted mature H. roretzi miRNAs (268 hits minus 38 identified in Fig. 3), Hrore-miR-5008 was the only one with less than two mismatches to the 458 C. robusta mature miRNAs predicted by Keshavan et al. . When four mismatches were allowed, only five hits were detected, Harore-miR-5008 (Cirobu-miR-13a), Harore-miR-5046 (Cirobu-miR-200b), Harore-miR-5154_5p (Cirobu-miR-244), Harore-miR-5165 (Cirobu-miR-246), and Harore-miR-5214 (Cirobu-miR-246).
In addition to 458 C. robusta mature miRNAs, we reexamined whether the 230 novel Halocynthia miRNAs have homologues in the entire C. robusta genome (http://ghost.zool.kyoto-u.ac.jp/download_kh.html). When two mismatches were allowed in the mature sequences, 168 H. roretzi miRNAs were found to have a homolog in the C. robusta genome (BLASTN top 500 hits, word size of 7, and an alignment length of ≥20). When four mismatches were accepted, however, 225 out of 230 (97.8%) H. roretzi miRNAs were found to have a homolog in the C. robusta genome and 66 (29.3%) of these 225 precursors could form canonical stem-loops when the temperature parameters of RNAfold were adjusted to 18 °C  (Additional file 1, right most column). We have analyzed positions of the mismatches. 24.5% occurred in seed region (base 2–7) and 75.5% occurred outside of the seed region, suggesting that mismatches distribute evenly over entire miRNAs as base number of seed region is only six out of 20–24 nucleotide. These results suggest that, although the sequences of mature ascidian miRNAs may diverge rapidly, C. robusta and H. roretzi may share more homologous miRNAs than expected from the results of the previous section.
The union of the miRNAs predicted by all approaches consists of 319 candidate miRNAs in the Halocynthia genome: 61 well-conserved miRNAs, 226 de novo predicted miRNAs and 32 additionally predicted from Ciona small RNA-Seq data. These predictions largely overlap (Fig. 3).
Validation of potential miRNAs prediction
Potential target prediction of the miRNAs
To get insight into the functions of the miRNAs, 3′ UTR sequences were extracted from each gene model in Aniseed  (http://www.aniseed.cnrs.fr/). Then, targets were tentatively predicted. A total of 3451 possible target sites in putative 3′ UTR sequences, which correspond to 17% of coding genes (2734 genes out of approximately 16,000 gene models), were detected for 275 miRNAs. Among those, 285 target genes of 140 miRNAs have gene ontology (GO) terms associated with various development processes. Although the functional validation of these targets goes beyond the scope of this article and these targets have not been functionally validated, information on their identity, provided as a list in Additional file 5, may be useful for future studies of miRNA functions.
Compared to high-throughput small RNA sequencing, computational miRNA discovery approaches offer several advantages when reference genome sequence is available. First, they do not need the availability of small RNA-Seq. Second, it could in theory discover all possible miRNAs, while small RNA sequencing can only identify miRNAs expressed in the cells, tissues, organs, from which the RNA was collected. This is particularly useful as some miRNAs are only expressed in response to stresses, such as hyper-salinity, hyper osmotic pressure and disease. On the other hand, the disadvantage of computational predictions is that no direct experimental support of the predicted miRNAs is provided until their expression is validated via RT-PCR or small RNA-Seq.
In this study, the repertoire of miRNAs in the H. roretzi genome was investigated by bioinformatics methods (Fig. 3 and Additional file 1) using three methods: homology search using mature miRNAs sequences deposited in miRBase (method 1 in Fig. 3), de novo miRNAs prediction using srnaloop (method 2), and prediction based on sequence similarity with C. robusta small RNA-Seq data (method 3). We found 61 conserved miRNAs, 226 additional miRNAs predicted by srnaloop, and another non-overlapping set of 32 miRNAs using C. robusta small RNA-Seq data. 38 conserved miRNAs were predicted by all three methods, a significant overlap supporting the reliability of the methods used in the present study.
Thirty two (25 families) of 319 miRNAs (10%) are well-conserved miRNAs over several phyla, and 24 (21 families) are shared only with C. robusta. In addition, 230 novel miRNAs of H. roretzi were predicted. 41 of these may correspond to genuine miRNAs in C. robusta. Therefore, 20% (65) miRNAs may correspond to ascidian/tunicate-specific miRNAs shared between H. roretzi (order Stolidobranchia) and C. robusta (order Stolidobranchia), although this estimation is still rough. The situation is similar in C. robusta. We checked the 348 previously identified miRNAs in C. robusta that are deposited in miRBase  and found that only 47 of them are well-conserved miRNAs over phyla. Therefore, it seems that ascidian species have small number of widely conserved miRNAs, and a larger number of ascidian/tunicate-specific miRNAs. These results are consistent with the general conjecture that animal miRNAs are not well conserved between distant taxa .
miRNAs play crucial roles in the modulation of developmental processes as well as response to environment stresses, but little is known about their functions in the tunicate species. We reported potential H. roretzi miRNAs inferred from the genome sequence, and showed that only a small number of miRNAs were conserved across phyla. Most miRNAs were newly discovered in this study. Our study suggested the possibility that many miRNAs could be conserved among ascidian species. This finding would hopefully facilitate future studies of gene regulation by miRNAs.
Data Preparation & Pre-processing
The H. roretzi genome sequences were assembled by us and are publicly available through the Aniseed database (http://www.aniseed.cnrs.fr/) . Prior to miRNA predication, we masked genomic regions with less miRNA-cording possibility. This includes protein coding and non-coding RNA generating regions that are rRNAs, tRNAs, snoRNAs and lncRNAs. To be specific, coding sequences and repeat regions were masked firstly. Coding regions were obtained from the predicted gene models in the Aniseed, and repeat regions were generated by RepeatMasker and RepeatModeler (http://repeatmasker.org). Tandem repeats were masked by Tandem Repeats Finder . Other RNAs, including rRNAs, tRNAs, snRNAs and lncRNAs were filtered using Rfam database (Release 11.0)  by cmsearch in INFERNAL  (E-value threshold: 1e-3). Potential tRNAs were also screened by tRNAscan-SE .
Identification of widely conserved miRNAs and precursors
To reduce the search space, we firstly queried all metazoan mature miRNA seeds deposited in miRBase database (Release 21)  against the masked H. roretzi genome sequences using blastn with a word size of 7 and E-value of 10. The 110 bp sequences of matched genomic regions were retrieved separately and extended for additional 20 bp from both 5′ and 3′ ends. Potential miRNAs with no more than two mismatches with known metazoan miRNAs were identified by patscan [13, 41, 42], and the candidates were folded by RNAfold [50, 51] subsequently. Unmatched sequences were trimmed. If more than one miRNA were mapped to the overlapping position, only the best one with the minimum folding free energy (MFE) was kept. In addition, Rfam (Release 11.0)  covariance model (CM) was also used to search for conserved miRNA structures. Low complexity sequences were removed. We retained sequences whose minimum folding free energy (MFE) and stem-loop structure passed our filtration criteria that are mentioned below.
De novo miRNAs prediction
To detect potential miRNA precursors, srnaloop  was employed to identify hairpin structures from masked H. roretzi genome sequence. We used the parameter “-st d -Gs -1.5 -gu 1 -sml 100000000 -am 990000 -t 15 -lml 5”. “-st d” means that we used the DNA sequences, “-Gs -1.5” means that gap start score is -1.5, “-gu 1” means that GU base pairs score is 1, “-t 15” means that alignment score threshold is 15, “-lml 5” means that minimum size of hairpin loop is 5. The sequence length parameter “-sml” and the maximum hairpin alignments “-am” parameters were set at their maximal possible value (100000000 and 900000). In srnaloop software, the predicated hairpin lengths were less than and tend to be close to the length parameter that is set, while the miRNA precursors length generally ranges from 60 to120 (Fig. 6a). Therefore, the maximum length of hairpin sequence parameter “-l” was set from 60 to 130 with an interval of 10, and chose the shortest hairpin that passed the folding free energy evaluation. Potential precursors were further blasted against nr database to remove protein coding genes (E-value threshold of 10).
miRNAs prediction using C. robusta small RNA-Seq data
The C. robusta small RNA-Seq reads (SRR038843, SRR038844, 26 nt) were mapped onto the repeat-sequence-masked H. roretzi reference genome using BLASTN (E-value of ≤1000, word size of 10). Only the best hits with a high-scoring segment pair (HSP) identity of ≥90% and an alignment length of ≥20 and ≤2 mismatches were kept. Then, we examined possible stem-loop structures in the sequences and the flanking 100 bp sequences using srnaloop . Sequences whose minimum folding free energy (MFE) and stem-loop structure passed our filtration criteria were reserved. Among the candidate stem-loops, those with more than ten H. roretzi mRNA reads in mRNA-seq results (C. Dantec, H. Nishida and P. Lemaire, unpublished results) were excluded.
All the potential miRNAs reported in this study passed the following filtration criteria. The miRNA precursors were generally considered that they have a smaller minimum folding free energy (MFE, ΔG kcal/mol) than ordinary genomic sequences of the same length. In this study, the MFE of potential precursors were obtained by folding their sequences using RNAfold, and whose minimum folding free energy not satisfying the following threshold were excluded from further analysis:
MFE ≤ -0.31 * L + 6.00 (see Fig. 6c)
Where, L indicates the predicated precursor length. The relationship between MFE and the length of miRNA precursor were tested on all metazoan species and simulated by a linear regression analysis [53, 54]. Then, the stem-loop structures were manually checked to exclude sequences with big bulges, or in which a part of mature miRNA is presented in the loop. To reduce false positive predictions, precursors that contain more than one loop (folded by RNAfold) were discarded (see Fig. 6b).
The GC contents are relatively low in the C. robusta (34.7%) and H. roretzi (35.7%) genome sequences. We have calculated GC content of the miRNAs in C. robusta (see Fig. 6d, blue bars). The GC content is not as biased as that of the genome. Therefore, we used 30–70% GC contents as filtration criterion since most miRNAs of C. robusta also fit this criterion. There could be a constraint on the GC contents of miRNAs as they have to firmly bind to target mRNAs.
Inference of Mature miRNA for novel miRNA precursors
Patscan were used to find 5p and 3p mature miRNAs. The pattern file is: p1 = 22…27 5…50 ~ p1[6,0,0]. “p1 = 22…27” means that match the 5p mature miRNA sequence whose length is between 22 nt and 27 nt. “5…50” means that the distance between 5p and 3p mature miRNAs could range from 5 nt to 50 nt. “ ~ p1[6,0,0]” means that match the 3p mature miRNA sequence by allowing up to six mismatches to the reverse and complement 5p mature miRNA sequence. If more than one pairs were detected, the one closest to loop structure was kept. If no paired sequence found, the 24 bp pair sequences closest to the loop structure would be used. Putative miRNAs and miRNA stars were simply named using the suffix “5p” and “3p” subsequently.
Inference of strand of miRNA precursors
The strands of the miRNAs were determined according to the miRBase database (Release 21)  (best BLASTN hit), Halocynthia ESTs (http://magest.hgc.jp) (E-value ≤ 1e-5) and Ciona small RNA-Seq data (best BLASTN hit). In miRNAs without a significant hit, the sequences of them are tentatively represented by those in plus strand of the reference genome.
Prediction of miRNA targets
To get insight into the functions of the miRNAs, 3′ UTR sequences (defined as 400 bp sequence that immediately follows the translation stop codon in the genome sequence in this study) were extracted from each gene model in Aniseed  (http://www.aniseed.cnrs.fr/). We adopted RNAhybrid  to identify miRNA targets with p-value ≤ 0.01. Considering that position of the putative mature miRNAs could not be precisely inferred, we adjusted the seed sequence by using a series of helix constraint parameter “-f 1,6”, “-f 2,7”, “-f 3,8”,“-f 4,9”, “-f 5,10”,“-f 6,11”.
Validation of miRNAs via RT-PCR
To validate the expression of predicted miRNAs, embryonic samples of six stages, unfertilized egg, blastula, gastrula, neurula, tailbud and hatched larvae were collected. Eggs of two adults were fertilized with sperms of five adults. Follicle cells that reside outside of the vitelline membrane and test sells that reside inside of the vitelline membrane were removed by digesting the vitelline membrane with protease, as previously described . Total RNAs were extracted using TRIzol Reagent (Life Technologies). The quantity of total RNA was examined using Ultrospec 2100 pro (Life Sciences) and RNA integrity was assessed via agarose gel electrophoresis. RT-PCR primers (Additional file 4) were synthesized and purchased from Eurofins Genomics (Tokyo, Japan). Simple miRNA Detection Kit (BioDynamics Laboratory Inc. Japan) was used for RT-PCR according to the manufacture’s protocol. The PCR products were exposed to UV light after the polyacrylamide gel electrophoresis, and the images were taken with NIPPON genetics FAS-IV illuminator. The expected size of the amplified fragment is ~40 bp because the 19 bp universal primer is amplified together with ~20 bp miRNA-specific primers. To validate sequences of the PCR products, they were dissected from the gel, subcloned into the pGEM-T Easy vector (Promega), and sequencing reaction was performed at Eurofins Genomics (Tokyo, Japan).
High-scoring segment pair
Minimum folding free energy
Computational resources were provided by the Genome Information Research Center, Osaka University.
This work was supported by Grants-in-Aid for Scientific Research from JSPS to H.N. (22370078, 25113518, 15H04377) and T.A.O. (22870019). K.W. is supported by MEXT Scholarship (125058), Mitsubishi Corporation International Scholarship (MITSU1451), Osaka University Scholarship and Research Assistant Fellowship, National Natural Science Foundation of China (31601163) and China Postdoctoral Science Foundation (2016M601661).
Availability of data and materials
The data supporting this article can be found in the Additional files.
KW, CD, PL, TO and HN designed the study, wrote and revised the manuscript. KW analyzed and interpreted the data. KW and TO performed experimental validation. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
- Yu Z, Li Y, Fan H, Liu Z, Pestell RG. miRNAs regulate stem cell self-renewal and differentiation. Front Genet. 2012;3:191.View ArticlePubMedPubMed CentralGoogle Scholar
- Nakano H, Yamada Y, Miyazawa T, Yoshida T. Gain-of-function microRNA screens identify miR-193a regulating proliferation and apoptosis in epithelial ovarian cancer cells. Int J Oncol. 2013;42(6):1875–82.PubMedPubMed CentralGoogle Scholar
- Tzur G, Israel A, Levy A, Benjamin H, Meiri E, Shufaro Y, Meir K, Khvalevsky E, Spector Y, Rojansky N, et al. Comprehensive gene and microRNA expression profiling reveals a role for microRNAs in human liver development. PLoS One. 2009;4(10), e7511.View ArticlePubMedPubMed CentralGoogle Scholar
- Shiiba M, Shinozuka K, Saito K, Fushimi K, Kasamatsu A, Ogawara K, Uzawa K, Ito H, Takiguchi Y, Tanzawa H. MicroRNA-125b regulates proliferation and radioresistance of oral squamous cell carcinoma. Br J Cancer. 2013;108(9):1817–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Martinez NJ, Walhout AJ. The interplay between transcription factors and microRNAs in genome-scale regulatory networks. Bioessays. 2009;31(4):435–45.View ArticlePubMedPubMed CentralGoogle 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.View ArticlePubMedGoogle Scholar
- Li SC, Pan CY, Lin WC. Bioinformatic discovery of microRNA precursors from human ESTs and introns. BMC Genomics. 2006;7:164.View ArticlePubMedPubMed CentralGoogle Scholar
- Rodriguez A, Griffiths-Jones S, Ashurst JL, Bradley A. Identification of mammalian microRNA host genes and transcription units. Genome Res. 2004;14(10A):1902–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Hao Y, Liu JR, Zhang Y, Yang PG, Feng YJ, Cui YJ, Yang CH, Gu XH. The microRNA expression profile in porcine skeletal muscle is changed by constant heat stress. Anim Genet. 2016;47(3):365–9.View ArticlePubMedGoogle Scholar
- Chen CC, Fu SF, Norikazu M, Yang YW, Liu YJ, Ikeo K, Gojobori T, Huang HJ. Comparative miRNAs analysis of Two contrasting broccoli inbred lines with divergent head-forming capacity under temperature stress. BMC Genomics. 2015;16:1026.View ArticlePubMedPubMed CentralGoogle Scholar
- Bedreag OH, Sandesc D, Chiriac SD, Rogobete AF, Cradigati AC, Sarandan M, Dumache R, Nartita R, Papurica M. The Use of Circulating miRNAs as Biomarkers for Oxidative Stress in Critically Ill Polytrauma Patients. Clin Lab. 2016;62(3):263–74.PubMedGoogle Scholar
- Jian H, Wang J, Wang T, Wei L, Li J, Liu L. Identification of Rapeseed MicroRNAs Involved in Early Stage Seed Germination under Salt and Drought Stresses. Front Plant Sci. 2016;7:658.PubMedPubMed CentralGoogle Scholar
- Cai P, Hou N, Piao X, Liu S, Liu H, Yang F, Wang J, Jin Q, Wang H, Chen Q. Profiles of small non-coding RNAs in Schistosoma japonicum during development. PLoS Negl Trop Dis. 2011;5(8):e1256.View ArticlePubMedPubMed CentralGoogle Scholar
- Xie L, Yang Z, Li G, Shen L, Xiang X, Liu X, Xu D, Xu L, Chen Y, Tian Z, et al. Genome-wide identification of bone metastasis-related microRNAs in lung adenocarcinoma by high-throughput sequencing. PLoS One. 2013;8(4):e61212.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu N, Yang J, Guo S, Xu Y, Zhang M. Genome-wide identification and comparative analysis of conserved and novel microRNAs in grafted watermelon by high-throughput sequencing. PLoS One. 2013;8(2):e57359.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang T, Chen L, Zhao M, Tian Q, Zhang WH. Identification of drought-responsive microRNAs in Medicago truncatula by genome-wide high-throughput sequencing. BMC Genomics. 2011;12:367.View ArticlePubMedPubMed CentralGoogle Scholar
- Lemaire P. Evolutionary crossroads in developmental biology: the tunicates. Development. 2011;138(11):2143–52.View ArticlePubMedGoogle Scholar
- Satoh N. Ascidian embryos as a model system to analyze expression and function of developmental genes. Differentiation. 2001;68(1):1–12.View ArticlePubMedGoogle Scholar
- Brozovic M, Martin C, Dantec C, Dauga D, Mendez M, Simion P, Percher M, Laporte B, Scornavacca C, Di Gregorio A, et al. ANISEED 2015: a digital framework for the comparative developmental biology of ascidians. Nucleic Acids Res. 2016;44(D1):D808–18.View ArticlePubMedGoogle Scholar
- Dehal P, Satou Y, Campbell RK, Chapman J, Degnan B, De Tomaso A, Davidson B, Di Gregorio A, Gelpke M, Goodstein DM, et al. The draft genome of Ciona intestinalis: insights into chordate and vertebrate origins. Science. 2002;298(5601):2157–67.View ArticlePubMedGoogle Scholar
- Brunetti R, Gissi C, Pennati R, Caicci F, Gasparini F, Manni L. Morphological evidence that the molecularly determined Ciona intestinalis type A and type B are different species: Ciona robusta and Ciona intestinalis. J Zool Syst Evol Res. 2015;53(3):186–93.View ArticleGoogle Scholar
- Vinson JP, Jaffe DB, O’Neill K, Karlsson EK, Stange-Thomann N, Anderson S, Mesirov JP, Satoh N, Satou Y, Nusbaum C, et al. Assembly of polymorphic genomes: algorithms and application to Ciona savignyi. Genome Res. 2005;15(8):1127–35.View ArticlePubMedPubMed CentralGoogle Scholar
- Voskoboynik A, Neff NF, Sahoo D, Newman AM, Pushkarev D, Koh W, Passarelli B, Fan HC, Mantalas GL, Palmeri KJ, et al. The genome sequence of the colonial chordate, Botryllus schlosseri. Elife. 2013;2:e00569.View ArticlePubMedPubMed CentralGoogle Scholar
- Gyoja F, Satou Y, Shin-i T, Kohara Y, Swalla BJ, Satoh N. Analysis of large scale expression sequenced tags (ESTs) from the anural ascidian, Molgula tectiformis. Dev Biol. 2007;307(2):460–82.View ArticlePubMedGoogle Scholar
- Seo HC, Kube M, Edvardsen RB, Jensen MF, Beck A, Spriet E, Gorsky G, Thompson EM, Lehrach H, Reinhardt R, et al. Miniature genome in the marine chordate Oikopleura dioica. Science. 2001;294(5551):2506.View ArticlePubMedGoogle Scholar
- Danks G, Campsteijn C, Parida M, Butcher S, Doddapaneni H, Fu B, Petrin R, Metpally R, Lenhard B, Wincker P, et al. OikoBase: a genomics and developmental transcriptomics resource for the urochordate Oikopleura dioica. Nucleic Acids Res. 2013;41(Database issue):D845–53.View ArticlePubMedGoogle Scholar
- Stolfi A, Lowe EK, Racioppi C, Ristoratore F, Brown CT, Swalla BJ, Christiaen L. Divergent mechanisms regulate conserved cardiopharyngeal development and gene expression in distantly related ascidians. Elife. 2014;3:e03728.View ArticlePubMedPubMed CentralGoogle Scholar
- Tsagkogeorga G, Turon X, Galtier N, Douzery EJ, Delsuc F. Accelerated evolutionary rate of housekeeping genes in tunicates. J Mol Evol. 2010;71(2):153–67.View ArticlePubMedGoogle Scholar
- Norden-Krichmar TM, Holtz J, Pasquinelli AE, Gaasterland T. Computational prediction and experimental validation of Ciona intestinalis microRNA genes. BMC Genomics. 2007;8:445.View ArticlePubMedPubMed CentralGoogle Scholar
- Shi W, Hendrix D, Levine M, Haley B. A distinct class of small RNAs arises from pre-miRNA-proximal regions in a simple chordate. Nat Struct Mol Biol. 2009;16(2):183–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Keshavan R, Virata M, Keshavan A, Zeller RW. Computational identification of Ciona intestinalis microRNAs. Zoolog Sci. 2010;27(2):162–70.View ArticlePubMedGoogle Scholar
- Hendrix D, Levine M, Shi W. miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data. Genome Biol. 2010;11(4):R39.View ArticlePubMedPubMed CentralGoogle Scholar
- Kusakabe R, Tani S, Nishitsuji K, Shindo M, Okamura K, Miyamoto Y, Nakai K, Suzuki Y, Kusakabe TG, Inoue K. Characterization of the compact bicistronic microRNA precursor, miR-1/miR-133, expressed specifically in Ciona muscle tissues. Gene Expr Patterns. 2013;13(1-2):43–50.View ArticlePubMedGoogle Scholar
- Chen JS, Pedro MS, Zeller RW. miR-124 function during Ciona intestinalis neuronal development includes extensive interaction with the Notch signaling pathway. Development. 2011;138(22):4943–53.View ArticlePubMedGoogle Scholar
- Joyce Tang W, Chen JS, Zeller RW. Transcriptional regulation of the peripheral nervous system in Ciona intestinalis. Dev Biol. 2013;378(2):183–93.View ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42(Database issue):D68–73.View ArticlePubMedGoogle Scholar
- Fu X, Adamski M, Thompson EM. Altered miRNA repertoire in the simplified chordate, Oikopleura dioica. Mol Biol Evol. 2008;25(6):1067–80.View ArticlePubMedGoogle Scholar
- Grad Y, Aach J, Hayes GD, Reinhart BJ, Church GM, Ruvkun G, Kim J. Computational and experimental identification of C. elegans microRNAs. Mol Cell. 2003;11(5):1253–63.View ArticlePubMedGoogle Scholar
- Wang C, Han J, Liu C, Kibet KN, Kayesh E, Shangguan L, Li X, Fang J. Identification of microRNAs from Amur grape (Vitis amurensis Rupr.) by deep sequencing and analysis of microRNA variations with bioinformatics. BMC Genomics. 2012;13:122.View ArticlePubMedPubMed CentralGoogle Scholar
- Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Dsouza M, Larsen N, Overbeek R. Searching for patterns in genomic data. Trends Genet. 1997;13(12):497–8.View ArticlePubMedGoogle Scholar
- Hatakeyama Y, Shibuya N, Nishiyama T, Nakashima N. Structural variant of the intergenic internal ribosome entry site elements in dicistroviruses and computational search for their counterparts. RNA. 2004;10(5):779–86.View ArticlePubMedPubMed CentralGoogle Scholar
- Ryvkin P, Leung YY, Ungar LH, Gregory BD, Wang LS. Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs. Methods. 2014;67(1):28–35.View ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011;39(Database issue):D152–7.View ArticlePubMedGoogle Scholar
- Li A, Mao L. Evolution of plant microRNA gene families. Cell Res. 2007;17(3):212–8.PubMedGoogle Scholar
- Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27(2):573–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Gardner PP, Daub J, Tate J, Moore BL, Osuch IH, Griffiths-Jones S, Finn RD, Nawrocki EP, Kolbe DL, Eddy SR, et al. Rfam: Wikipedia, clans and the “decimal” release. Nucleic Acids Res. 2011;39(Database issue):D141–5.View ArticlePubMedGoogle Scholar
- Nawrocki EP, Kolbe DL, Eddy SR. Infernal 1.0: inference of RNA alignments. Bioinformatics. 2009;25(10):1335–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25(5):955–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Bompfunewerer AF, Backofen R, Bernhart SH, Hertel J, Hofacker IL, Stadler PF, Will S. Variations on RNA folding and alignment: lessons from Benasque. J Math Biol. 2008;56(1-2):129–44.View ArticlePubMedGoogle Scholar
- Gruber AR, Lorenz R, Bernhart SH, Neubock R, Hofacker IL. The Vienna RNA websuite. Nucleic Acids Res. 2008;36(Web Server issue):W70–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, Wilkinson AC, Finn RD, Griffiths-Jones S, Eddy SR, et al. Rfam: updates to the RNA families database. Nucleic Acids Res. 2009;37(Database issue):D136–40.View ArticlePubMedGoogle Scholar
- Zhang BH, Pan XP, Cox SB, Cobb GP, Anderson TA. Evidence that miRNAs are different from other RNAs. Cell Mol Life Sci. 2006;63(2):246–54.View ArticlePubMedGoogle Scholar
- Adai A, Johnson C, Mlotshwa S, Archer-Evans S, Manocha V, Vance V, Sundaresan V. Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res. 2005;15(1):78–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10(10):1507–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Nishide K, Mugitani M, Kumano G, Nishida H. Neurula rotation determines left-right asymmetry in ascidian tadpole larvae. Development. 2012;139(8):1467–75.View ArticlePubMedGoogle Scholar