- Open Access
MicroPC (μPC): A comprehensive resource for predicting and comparing plant microRNAs
© Mhuantong and Wichadakul; licensee BioMed Central Ltd. 2009
- Received: 20 March 2009
- Accepted: 7 August 2009
- Published: 7 August 2009
Plant microRNA (miRNA) has an important role in controlling gene regulation in various biological processes such as cell development, signal transduction, and environmental responses. While information on plant miRNAs and their targets is widely available, accessible online plant miRNA resources are limited; most of them are intended for economically important crops or plant model organisms. With abundant sequence data of numerous plants in public databases such as NCBI and PlantGDB, the identification of their miRNAs and targets would benefit researchers as a central resource for the comparative studies of plant miRNAs.
MicroPC (μPC) is an online plant miRNA resource resulted from large-scale Expressed Sequence Tag (EST) analysis. It consists of 4,006 potential miRNA candidates in 128 families of 125 plant species and 2,995 proteins (4,953 EST sequences) potentially targeted by 78 families of miRNA candidates. In addition, it is incorporated with 1,727 previously reported plant mature miRNA sequences from miRBase. The μPC enables users to compare stored mature or precursor miRNAs and user-supplied sequences among plant species. The search utility allows users to investigate the predicted miRNAs and miRNA targets in detail via various search options such as miRNA family and plant species. To enhance the database usage, the prediction utility provides interactive steps for determining a miRNA or miRNA targets from an input nucleotide sequence and links the prediction results to their homologs in the μPC.
The μPC constitutes the first online resource that enables users to comprehensively compare and predict plant miRNAs and their targets. It imparts a basis for further research on revealing miRNA conservation, function, and evolution across plant species and classification. The μPC is available at http://www.biotec.or.th/isl/micropc.
- miRNA Target
- miRNA Sequence
- Plant miRNAs
- miRNA Candidate
- Mature miRNA Sequence
MicroRNA (miRNA) is a class of non-coding small RNAs with a single strand molecule of 18–22 nucleotides. It plays a pivotal role in gene regulation in various species by targeting mRNAs at the post-transcriptional levels . In plants, miRNA involves in organ development and environmental responses [2–4].
Despite accumulating studies in the identification of plant miRNAs and their targets based on both computational [5–12] and experimental [13–16] approaches, existing public resources focusing on plant miRNAs and their targets are limited to economically important crops or specially implemented for plant model organisms such as Arabidopsis, rice, and maize. Among them, miRU  is a web server for plant miRNA target prediction utilizing the concepts of nearly perfect binding between a miRNA and its targets. Plant MPSS (Massively Parallel Signature Sequencing) databases  of Arabidopsis, rice, grape, and rice blast fungus (Magnaporthe grisea) were developed to provide an online resource for mRNA and small RNA analyses. ASRP (Arabidopsis Small RNA Project) [19, 20] provides a repository with tools for analysis and visualization of small RNA sequences cloned from various ecotypes and tissues of Arabidopsis. CSRDB (Cereal Small RNA Database)  consists of maize and rice small RNA sequences generated by high-throughput pyrosequencing with the provided maps to the available rice and maize genomes.
Here, we developed MicroPC (μPC) as a comprehensive online resource for predicting and comparing plant miRNAs and their targets. The μPC is resulted from applying previous steps and criteria [7, 22–25] with known plant mature miRNA sequences from miRBase [26, 27] and large-scale EST sequences from PlantGDB [28, 29]. The μPC database consists of 4,006 predicted miRNA candidates in 128 families of 125 plant species and 2,995 proteins (4,953 EST sequences) potentially targeted by 78 families of potential miRNA candidates. It characterizes the predicted miRNAs and their targets according to plant classification (e.g., monocots, dicots, mosses) and incorporated with previously discovered 1,727 plant mature miRNAs from miRBase. With stored data, it offers users a comparative and integrative view of previously reported miRNAs and our predicted miRNA candidates among various plant species. The sequence alignment feature enables users to explore the relation and evolution of either mature or precursor miRNA sequences within a family across plant species. Moreover, the search utility provides various search options for users to explore the predicted miRNA candidates and their potential targets in detail. The prediction utility helps to predict potential miRNA candidates for an input nucleotide sequence and to scan for EST sequences of a plant species that might be targeted by an input mature miRNA sequence.
We obtained 1,727 (1,022 with experiments and 705 without experiments) known mature miRNA sequences in 418 families of 21 plant species from miRBase release 12.0 (September 2008) [see Additional file 1]. To prepare and filter source sequences, we used UNAFold  to predict the secondary structures of the obtained non-experimental stem-loop sequences and applied the criteria in  to remove structural sequences which have (1) the number of mismatches between miRNA and miRNA* greater than 4, and/or (2) the number of asymmetric bulges greater than 1, and/or (3) the size of the asymmetric bulge greater than 2 [see excluded sequences in Additional file 2]. We then manually curated remaining 513 sequences and deployed them together with all sequences with experiments as source sequences for miRNA prediction. All 5,306,503 EST sequences of 173 plant species were downloaded from PlantGDB in November, 2008 [see Additional file 3]. We compiled 1,372 RNA families from Rfam 9.1 (January, 2009)  to filter out non-coding RNAs (ncRNAs) that are not miRNAs in the step of miRNA prediction. For EST functional annotation, the UniProtKB/Swiss-Prot and UniProtKB/TrEMBL protein data sets release 14.9 were obtained from UniProt . These data sets will be updated periodically when their new releases are available.
Prediction of potential miRNA candidates
Prediction of ESTs targeted by miRNA candidates
The prediction of plant miRNA targets is a straightforward process since plant mature miRNAs can bind with specific mRNA targets through perfect or nearly-perfect complementarities [13, 34]. Sequences of potential miRNA candidates were scanned for complementary sites on EST sequences that significantly hit some proteins in UniProt database based on BLASTX (E-value < e-20). An EST sequence would be considered as a potential target if it passes the criteria used in Rhoades et al. [22, 35]: (1) the number of mismatches (including G:U pair ) between sequence of potential miRNA candidate and its complementary site on the EST is fewer than 4, and (2) there is no gap in the complementary site. As specific positions of mismatches affect miRNA targeting [37, 38], we also collected these positions to enable the query of predicted targets with specific mismatch positions of interest. Total 2,995 proteins (4,953 EST sequences) potentially targeted by 78 families of miRNA candidates were assigned with Gene Ontology (GO) , categorized by GO ID (e.g., GO:0000166) and GO term (e.g., nucleotide binding), and stored in the μPC database. Figure 1B shows the procedure of potential miRNA target prediction.
Prediction accuracy and coverage
To assess the prediction accuracy and coverage, 936 experimental precursor miRNA sequences from miRBase were used as input for the miRNA prediction procedure and 761 out of 936 (81.3%) were correctly predicted as miRNA genes [see Additional file 4]. Other sequences were mostly ruled out during the secondary structure prediction. With one thousand repeats of 936 randomly generated sequences, the average number of random sequences predicted as miRNA genes is extremely low (0.00064%). To assess whether variable source mature miRNA sequences affect prediction results, we applied the same prediction steps to the 936 precursor miRNA sequences with leave-one-out of source mature miRNA sequences. The removal of common miRNAs or miRNAs with several members in a family has little or no effects on prediction results. In contrast, a removed species-specific miRNA sequence such as miRNA 1063 in moss will reduce the number of prediction coverage [see Additional files 5 and 6]. These measurements suggest that our miRNA prediction procedure could predict potential miRNA candidates with confidence and has limitation in coverage due to homology-based approach.
The μPC offers three main interactive pages for comparing, searching, and predicting plant miRNAs and their targets as described below.
Tools for miRNA and miRNA target predictions
The μPC represents the first online resource that facilitates users with the inclusive comparing and predicting plant miRNAs and miRNA targets across plant species and classification. The comparative utility enables users to explore the conservation and evolution of stored miRNAs and user-provided sequences across plant species. Users may explore miRNAs and their targets predicted by large-scale EST analysis via search utility and predict a miRNA or its possible targets for an input sequence via prediction utility. With stored data and incorporated utilities, the μPC conveys a basis for additional research on plant miRNA functions and evolution.
MicroPC (μPC) is freely available at http://www.biotec.or.th/isl/micropc.
The comparative miRNA sequence requires Java applet to display output via Jalview.
The web browser requires a PDF viewer plug-in (e.g., Acrobat Reader) to view and print the PDF files of secondary structures.
The authors would like to thank Drs. Anan Jongkaewwattana, Sithichoke Tangphatsornruang, and Supawadee Ingsriswang for helpful comments and discussions on the results. We thank Eakasit Pacharawongsakda and Somrak Numnark for their technical assistances in web interfaces and database construction. This work was supported by grant FC0033 B21 (SPA B1-1) from Cluster Program Management Office (CPMO), National Science and Technology Development Agency (NSTDA), Thailand.
- Bartel DP: MicroRNAs. Genomics, biogenesis, mechanism, and function. Cell. 2004, 116: 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Zhang B, Pan X, Cobb GP, Anderson TA: Plant microRNA: A small regulatory molecule with big impact. Developmental Biology. 2006, 289 (1): 3-16. 10.1016/j.ydbio.2005.10.036.View ArticlePubMedGoogle Scholar
- Sunkar R, Chinnusamy V, Zhu J, Zhu J-K: Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends in Plant Science. 2007, 12 (7): 301-309. 10.1016/j.tplants.2007.05.001.View ArticlePubMedGoogle Scholar
- Mallory AC, Vaucheret H: Functions of microRNAs and related small RNAs in plants. Nat Genet. 2006, 38: S31-S36. 10.1038/ng1791.View ArticlePubMedGoogle Scholar
- Wang X-J, Reyes J, Chua N-H, Gaasterland T: Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biology. 2004, 5 (9): R65-10.1186/gb-2004-5-9-r65.PubMed CentralView ArticlePubMedGoogle Scholar
- Bonnet E, Wuyts J, Rouze P, Peer Van de Y: Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes. Proc Natl Acad Sci U S A. 2004, 101 (31): 11511-11516. 10.1073/pnas.0404025101.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang B, Pan X, Wang QL, Cobb GP, Anderson TA: Identification and characterization of new plant microRNAs using EST analysis. Cell Res. 2005, 15 (5): 336-360. 10.1038/sj.cr.7290302.View ArticlePubMedGoogle Scholar
- Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA: Conservation and divergence of plant microRNA genes. The Plant Journal. 2006, 46: 243-259. 10.1111/j.1365-313X.2006.02697.x.View ArticlePubMedGoogle Scholar
- Zhang B, Wang Q, Wang K, Pan X, Liu F, Guo T, Cobb GP, Anderson TA: Identification of cotton microRNAs and their targets. Gene. 2007, 397: 26-37. 10.1016/j.gene.2007.03.020.View ArticlePubMedGoogle Scholar
- Xie FL, Huang SQ, Guo K, Xiang AL, Zhu YY, Nie L, Yang ZM: Computational identification of novel microRNAs and targets in Brassica napus. FEBS Letters. 2007, 581 (7): 1464-1474. 10.1016/j.febslet.2007.02.074.View ArticlePubMedGoogle Scholar
- Yin Z, Li C, Han X, Shen F: Identification of conserved microRNAs and their target genes in tomato (Lycopersicon esculentum). Gene. 2008, 414: 60-66. 10.1016/j.gene.2008.02.007.View ArticlePubMedGoogle Scholar
- Sunkar R, Jagadeeswaran G: In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biology. 2008, 8 (1): 37-10.1186/1471-2229-8-37.PubMed CentralView ArticlePubMedGoogle Scholar
- Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP: MicroRNAs in plants. Genes Dev. 2002, 16 (13): 1616-1626. 10.1101/gad.1004402.PubMed CentralView ArticlePubMedGoogle Scholar
- Park W, Li J, Song R, Messing J, Chen X: CARPEL FACTORY, a Dicer Homolog, and HEN1, a Novel Protein, Act in microRNA Metabolism in Arabidopsis thaliana. Current Biology. 2002, 12 (17): 1484-1495. 10.1016/S0960-9822(02)01017-5.View ArticlePubMedGoogle Scholar
- Sunkar R, Zhu J-K: Novel and Stress-Regulated MicroRNAs and Other Small RNAs from Arabidopsis. Plant Cell. 2004, 16 (8): 2001-2019. 10.1105/tpc.104.022830.PubMed CentralView ArticlePubMedGoogle Scholar
- Sunkar R, Girke T, Jain PK, Zhu JK: Cloning and Characterization of MicroRNAs from Rice. Plant Cell. 2005, 17 (5): 1397-1411. 10.1105/tpc.105.031682.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Y: miRU: an automated plant miRNA target prediction server. Nucl Acids Res. 2005, 33 (suppl_2): W701-704. 10.1093/nar/gki383.PubMed CentralView ArticlePubMedGoogle Scholar
- Nakano M, Nobuta K, Vemaraju K, Tej SS, Skogen JW, Meyers BC: Plant MPSS databases: signature-based transcriptional resources for analyses of mRNA and small RNA. Nucl Acids Res. 2006, 34 (suppl_1): D731-735. 10.1093/nar/gkj077.PubMed CentralView ArticlePubMedGoogle Scholar
- Gustafson AM, Allen E, Givan S, Smith D, Carrington JC, Kasschau KD: ASRP: the Arabidopsis Small RNA Project Database. Nucl Acids Res. 2005, 33 (suppl_1): D637-640.PubMed CentralPubMedGoogle Scholar
- Backman TWH, Sullivan CM, Cumbie JS, Miller ZA, Chapman EJ, Fahlgren N, Givan SA, Carrington JC, Kasschau KD: Update of ASRP: the Arabidopsis Small RNA Project database. Nucl Acids Res. 2008, 36 (suppl_1): D982-985.PubMed CentralPubMedGoogle Scholar
- Johnson C, Bowman L, Adai AT, Vance V, Sundaresan V: CSRDB: a small RNA integrated database and browser resource for cereals. Nucl Acids Res. 2007, 35 (suppl_1): D829-833. 10.1093/nar/gkl991.PubMed CentralView ArticlePubMedGoogle Scholar
- Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP: Prediction of Plant MicroRNA Targets. Cell. 2002, 110 (4): 513-10.1016/S0092-8674(02)00863-2.View ArticlePubMedGoogle Scholar
- Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones SAM, Marshall M, et al: A uniform system for microRNA annotation. RNA. 2003, 9 (3): 277-279. 10.1261/rna.2183803.PubMed CentralView ArticlePubMedGoogle Scholar
- Lai E: Predicting and validating microRNA targets. Genome Biology. 2004, 5 (9): 115-10.1186/gb-2004-5-9-115.PubMed CentralView ArticlePubMedGoogle Scholar
- Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ, et al: Criteria for Annotation of Plant MicroRNAs. Plant Cell. 2008, 20 (12): 3186-3190. 10.1105/tpc.108.064311.PubMed CentralView ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucl Acids Res. 2006, 34 (suppl_1): D140-144. 10.1093/nar/gkj112.PubMed CentralView ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucl Acids Res. 2008, 36 (suppl_1): D154-158.PubMed CentralPubMedGoogle Scholar
- Dong Q, Lawrence CJ, Schlueter SD, Wilkerson MD, Kurtz S, Lushbough C, Brendel V: Comparative Plant Genomics Resources at PlantGDB. Plant Physiol. 2005, 139 (2): 610-618. 10.1104/pp.104.059212.PubMed CentralView ArticlePubMedGoogle Scholar
- Duvick J, Fu A, Muppirala U, Sabharwal M, Wilkerson MD, Lawrence CJ, Lushbough C, Brendel V: PlantGDB: a resource for comparative plant genomics. Nucl Acids Res. 2008, 36 (suppl_1): D959-965.PubMed CentralPubMedGoogle Scholar
- Markham NR, Zuker M: UNAFold: software for nucleic acid folding and hybriziation. Methods Mol Biol. Edited by: Keith JM, Totowa NJ. 2008, Humana Press, 453: 3-31.Google Scholar
- Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A: Rfam: annotating non-coding RNAs in complete genomes. Nucl Acids Res. 2005, 33 (suppl_1): D121-124.PubMed CentralPubMedGoogle Scholar
- The UniProt C: The Universal Protein Resource (UniProt). Nucl Acids Res. 2008, 36 (suppl_1): D190-195.Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. Journal of Molecular Biology. 1990, 215: 403-410.View ArticlePubMedGoogle Scholar
- Llave C, Xie Z, Kasschau KD, Carrington JC: Cleavage of Scarecrow-like mRNA Targets Directed by a Class of Arabidopsis miRNA. Science. 2002, 297 (5589): 2053-2056. 10.1126/science.1076311.View ArticlePubMedGoogle Scholar
- Jones-Rhoades MW, Bartel DP: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell. 2004, 14: 787-799. 10.1016/j.molcel.2004.05.027.View ArticlePubMedGoogle Scholar
- Strobel SA, Cech TR: Minor groove recognition of the conserved G.U pair at the Tetrahymena ribozyme reaction site. Science. 1995, 267: 675-679. 10.1126/science.7839142.View ArticlePubMedGoogle Scholar
- Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, Weigel D: Specific Effects of MicroRNAs on the Plant Transcriptome. Developmental Cell. 2005, 8 (4): 517-527. 10.1016/j.devcel.2005.01.018.View ArticlePubMedGoogle Scholar
- Brodersen P, Sakvarelidze-Achard L, Bruun-Rasmussen M, Dunoyer P, Yamamoto YY, Sieburth L, Voinnet O: Widespread Translational Inhibition by Plant miRNAs and siRNAs. Science. 2008, 320 (5880): 1185-1190. 10.1126/science.1159151.View ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene Ontology: tool for the unification of biology. Nature Genet. 2000, 25: 25-29. 10.1038/75556.PubMed CentralView ArticlePubMedGoogle Scholar
- Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressivemultiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22 (22): 4673-4680. 10.1093/nar/22.22.4673.PubMed CentralView ArticlePubMedGoogle Scholar
- Clamp M, Cuff J, Searle SM, Barton GJ: The Jalview Java alignment editor. Bioinformatics. 2004, 20 (3): 426-427. 10.1093/bioinformatics/btg430.View ArticlePubMedGoogle Scholar
- Zhang B, Pan X, Cox S, Cobb G, Anderson T: Evidence that miRNAs are different from other RNAs. Cellular and Molecular Life Sciences (CMLS). 2006, 63 (2): 246-254. 10.1007/s00018-005-5467-7.View ArticleGoogle 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.