Name: a bioinformatics pipeline to accurately and efficiently analyze the microrna transcriptomes in plants
Uploaded: 2020-01-21T12:30:00
Description: Watch this Scientific Journal Video about A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants at JoVE.com

This work has been supported by Beijing Academy of Agriculture and Forestry Sciences (KJCX201917, KJCX20180425, and KJCX20180204) to XY and National Natural Science Foundation of China (31621001) to LL.

1. Installation and testing
<ol>
	<li>Download required dependencies: Bowtie222 and RNAfold23. Compiled packages are recommended.
	<ol>
		<li>Download Bowtie2, a read mapping tool, from its home site (<a href="http://bowtie-bio.sourceforge.net/bowtie2/index.shtml" target="_blank">http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</a>).</li>
		<li>Download RNAfold, a tool of the Vienna package used to predict RNA secondary structure, from <a href="http://www.tbi.univie....

<ol>
	<li>Ghildiyal, M., Zamore, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+silencing+RNAs:+an+expanding+universe.">Small silencing RNAs: an expanding universe.</a> Nature Reviews Genetics. 10 (2), 94-108 (2009).</li><li>Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs:+target+recognition+and+regulatory+functions.">MicroRNAs: target recognition and regulatory functions.</a> Cell. 136 (2), 215-233 (2009).</li><li>Moran, Y., Agron, M., Praher, D., Technau, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+evolutionary+origin+of+plant+and+animal+microRNAs.">The evolutionary origin of plant and animal microRNAs.</a> Nature Ecology Evolution. 1 (3), 27 (2017).</li><li>Xie, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expression+of+Arabidopsis+MIRNA+genes.">Expression of Arabidopsis MIRNA genes.</a> Plant Physiology. 138 (4), 2145-2154 (2005).</li><li>Zhao, X., Zhang, H., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+analysis+of+the+proximal+promoters+of+microRNA+genes+in+Arabidopsis.">Identification and analysis of the proximal promoters of microRNA genes in Arabidopsis.</a> Genomics. 101 (3), 187-194 (2013).</li><li>Bologna, N. G., Mateos, J. L., Bresso, E. G., Palatnik, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+loop-to-base+processing+mechanism+underlies+the+biogenesis+of+plant+microRNAs+miR319+and+miR159.">A loop-to-base processing mechanism underlies the biogenesis of plant microRNAs miR319 and miR159.</a> EMBO JOURNAL. 28 (23), 3646-3656 (2009).</li><li>Rogers, K., Chen, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biogenesis,+turnover,+and+mode+of+action+of+plant+microRNAs.">Biogenesis, turnover, and mode of action of plant microRNAs.</a> Plant Cell. 25 (7), 2383-2399 (2013).</li><li>Voinnet, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Origin,+biogenesis,+and+activity+of+plant+microRNAs.">Origin, biogenesis, and activity of plant microRNAs.</a> Cell. 136 (4), 669-687 (2009).</li><li>Iwakawa, H. O., Tomari, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Functions+of+MicroRNAs:+mRNA+Decay+and+Translational+Repression.">The Functions of MicroRNAs: mRNA Decay and Translational Repression.</a> Trends in Cell Biology. 25 (11), 651-665 (2015).</li><li>Lee, R. C., Feinbaum, R. L., Ambros, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+C.+elegans+heterochronic+gene+lin-4+encodes+small+RNAs+with+antisense+complementarity+to+lin-14.">The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.</a> Cell. 75 (5), 843-854 (1993).</li><li>Wightman, B., Ha, I., Ruvkun, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Posttranscriptional+regulation+of+the+heterochronic+gene+lin-14+by+lin-4+mediates+temporal+pattern+formation+in+C.+elegans.">Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans.</a> Cell. 75 (5), 855-862 (1993).</li><li>Friedlander, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovering+microRNAs+from+deep+sequencing+data+using+miRDeep.">Discovering microRNAs from deep sequencing data using miRDeep.</a> Nature Biotechnology. 26 (4), 407-415 (2008).</li><li>Yang, X., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRDeep-P:+a+computational+tool+for+analyzing+the+microRNA+transcriptome+in+plants.">miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants.</a> Bioinformatics. 27 (18), 2614-2615 (2011).</li><li>Meyers, B. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Criteria+for+annotation+of+plant+MicroRNAs.">Criteria for annotation of plant MicroRNAs.</a> Plant Cell. 20 (12), 3186-3190 (2008).</li><li>Yang, X., Zhang, H., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+analysis+of+gene-level+microRNA+expression+in+Arabidopsis+using+deep+sequencing+data.">Global analysis of gene-level microRNA expression in Arabidopsis using deep sequencing data.</a> Genomics. 98 (1), 40-46 (2011).</li><li>Kuang, Z., Wang, Y., Li, L., Yang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRDeep-P2:+accurate+and+fast+analysis+of+the+microRNA+transcriptome+in+plants.">miRDeep-P2: accurate and fast analysis of the microRNA transcriptome in plants.</a> Bioinformatics. , (2018).</li><li>Kozomara, A., Birgaoanu, M., Griffiths-Jones, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+from+microRNA+sequences+to+function.">miRBase: from microRNA sequences to function.</a> Nucleic Acids Research. 47 (1), 155-162 (2019).</li><li>Griffiths-Jones, S., Saini, H. K., van Dongen, S., Enright, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+tools+for+microRNA+genomics.">miRBase: tools for microRNA genomics.</a> Nucleic Acids Research. 36, 154-158 (2008).</li><li>Axtell, M. J., Meyers, B. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revisiting+Criteria+for+Plant+MicroRNA+Annotation+in+the+Era+of+Big+Data.">Revisiting Criteria for Plant MicroRNA Annotation in the Era of Big Data.</a> Plant Cell. 30 (2), 272-284 (2018).</li><li>Taylor, R. S., Tarver, J. E., Hiscock, S. J., Donoghue, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolutionary+history+of+plant+microRNAs.">Evolutionary history of plant microRNAs.</a> Trends in Plant Science. 19 (3), 175-182 (2014).</li><li>Kozomara, A., Griffiths-Jones, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+annotating+high+confidence+microRNAs+using+deep+sequencing+data.">miRBase: annotating high confidence microRNAs using deep sequencing data.</a> Nucleic Acids Research. 42, 68-73 (2014).</li><li>Langmead, B., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+gapped-read+alignment+with+Bowtie+2.">Fast gapped-read alignment with Bowtie 2.</a> Nature Methods. 9 (4), 357-359 (2012).</li><li>Lorenz, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ViennaRNA+Package+2.0.">ViennaRNA Package 2.0.</a> Algorithms for Molecular Biology. 6, 26 (2011).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biology. 10 (3), 25 (2009).</li><li>An, J., Lai, J., Sajjanhar, A., Lehman, M. L., Nelson, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRPlant:+an+integrated+tool+for+identification+of+plant+miRNA+from+RNA+sequencing+data.">miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data.</a> BMC Bioinformatics. 15, 275 (2014).</li><li>Lei, J., Sun, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miR-PREFeR:+an+accurate,+fast+and+easy-to-use+plant+miRNA+prediction+tool+using+small+RNA-Seq+data.">miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data.</a> Bioinformatics. 30 (19), 2837-2839 (2014).</li><li>Evers, M., Huttner, M., Dueck, A., Meister, G., Engelmann, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRA:+adaptable+novel+miRNA+identification+in+plants+using+small+RNA+sequencing+data.">miRA: adaptable novel miRNA identification in plants using small RNA sequencing data.</a> BMC Bioinformatics. 16, 370 (2015).</li><li>Mathelier, A., Carbone, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MIReNA:+finding+microRNAs+with+high+accuracy+and+no+learning+at+genome+scale+and+from+deep+sequencing+data.">MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data.</a> Bioinformatics. 26 (18), 2226-2234 (2010).</li><li>Zhu, Q. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+diverse+set+of+microRNAs+and+microRNA-like+small+RNAs+in+developing+rice+grains.">A diverse set of microRNAs and microRNA-like small RNAs in developing rice grains.</a> Genome Research. 18 (9), 1456-1465 (2008).</li><li>Fahlgren, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNA+gene+evolution+in+Arabidopsis+lyrata+and+Arabidopsis+thaliana.">MicroRNA gene evolution in Arabidopsis lyrata and Arabidopsis thaliana.</a> Plant Cell. 22 (4), 1074-1089 (2010).</li><li>Fromm, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Uniform+System+for+the+Annotation+of+Vertebrate+microRNA+Genes+and+the+Evolution+of+the+Human+microRNAome.">A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome.</a> Annual Review of Genetics. 49, 213-242 (2015).</li><li>Blevins, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+Pol+IV+and+RDR2-dependent+precursors+of+24+nt+siRNAs+guiding+de+novo+DNA+methylation+in+Arabidopsis.">Identification of Pol IV and RDR2-dependent precursors of 24 nt siRNAs guiding de novo DNA methylation in Arabidopsis.</a> Elife. 4, 09591 (2015).</li><li>Zhai, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+One+Precursor+One+siRNA+Model+for+Pol+IV-Dependent+siRNA+Biogenesis.">A One Precursor One siRNA Model for Pol IV-Dependent siRNA Biogenesis.</a> Cell. 163 (2), 445-455 (2015).</li><li>Werner, S., Wollmann, H., Schneeberger, K., Weigel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+determinants+for+accurate+processing+of+miR172a+in+Arabidopsis+thaliana.">Structure determinants for accurate processing of miR172a in Arabidopsis thaliana.</a> Current Biology. 20 (1), 42-48 (2010).</li><li>Mateos, J. L., Bologna, N. G., Chorostecki, U., Palatnik, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+microRNA+processing+determinants+by+random+mutagenesis+of+Arabidopsis+MIR172a+precursor.">Identification of microRNA processing determinants by random mutagenesis of Arabidopsis MIR172a precursor.</a> Current Biology. 20 (1), 49-54 (2010).</li><li>Vitsios, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mirnovo:+genome-free+prediction+of+microRNAs+from+small+RNA+sequencing+data+and+single-cells+using+decision+forests.">Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.</a> Nucleic Acids Research. 45 (21), 177 (2017).</li></ol>

With the advent of NGS, a large number of miRNA loci have been identified from an ever-increasing amount of sRNA sequencing data in diverse species29,30. In the centralized community database miRBase21, the deposited miRNA items have increased almost 100 times in the last decade. However, in comparison to miRNAs in animals, plant miRNAs have many unique features that make the identification/annotation more complicated13</...

One of the most exciting discoveries in the last two decades in biology is the proliferating role of sRNA species in regulating diverse functions of the genome1. In particular, miRNAs constitute an important class of 20- to 24-nt sRNAs in eukaryotes, and mainly function at post-transcriptional level as prominent gene regulators throughout life cycle development stages as well as in stimulus and stress responses2,3. In plants, miRNAs arise from primary transcripts called pri-miRNAs, which are generally transcribed by RNA polymerase II as individual transcription units4,5. Processed by evolutionarily conserved cellular machinery (Drosha RNase III in animals, DICER-like in plants), pri-miRNAs are excised into the immediate miRNA precursors, pre-miRNAs, which contain sequences forming intra-molecular stem-loop structures6,7. Pre-miRNAs are then processed into double-stranded intermediates, namely miRNA duplexes, consisting of the functional strand, mature miRNA, and the less frequently functional partner, miRNA*2,8. After loaded into the RNA-induced silencing complex (RISC), the mature miRNAs could recognize their mRNA targets based on sequence complementarity, resulting in a negative regulatory function2,8. miRNAs could either destabilize their target transcripts or prevent target translation but the former manner is dominated in plants8,9.
Since the fortuitous discovery of the first miRNA in the nematode Caenorhabditis elegans10,11, much research has been committed to miRNA identification and its functional analysis, especially after the availability of NGS method. The wide application of the NGS method has greatly promoted the utilization of computational tools that were designed to capture the unique feature of miRNAs, such as the stem-loop structure of precursors and their preferential accumulation of sequence reads on mature miRNA and miRNA*. As a result, researchers have achieved remarkable success in identifying miRNAs in diverse species. Based on a previously described probability model12, we developed miRDeep-P13, which was the first computational tool for discovering plant miRNAs from NGS data. miRDeep-P was specifically aimed at conquering the challenges of decoding plant miRNAs featuring more variable precursor length and large paralogous families13,14,15. After its release, this program has been downloaded thousands of times and used to annotate miRNA transcriptomes in more than 40 plant species16. Propelled by NGS-based tools like miRDeep-P, there has been a dramatic increase in the number of registered miRNAs in the public miRNA repository miRBase17, where over 38,000 miRNA items are currently hosted (release 22.1) in comparison to only ~500 miRNA items (release 2.0) in 200818.
However, two new challenges have arisen from plant miRNA annotation. First, high ratios of false-positives have heavily impacted the quality of plant miRNA annotations16,19 for the following reasons: 1) a deluge of endogenous short interfering RNAs (siRNAs) from NGS sRNA libraries were erroneously annotated as miRNAs due to lacking of a stringent miRNA annotation criteria; 2) for species without a priori miRNA information, false-positives predicted based on NGS data are difficult to eliminate. Using miRBase as an example, Taylor et al.20 found one third of plant miRNA entries in the public repository21 (release 21) lacked convincing supporting evidence and even three-fourths of plant miRNA families were questionable. Second, it becomes an extremely time-consuming process for predicting plant miRNAs with large and complex genomes16. To overcome these challenges, we updated miRDeep-P by adding a new filtering strategy, overhauling the scoring algorithm and integrating new criteria for plant miRNA annotation, and released the new version miRDP2. In addition, we tested miRDP2 using NGS sRNA datasets with gradually increasing genome sizes: Arabidopsis, rice, tomato, maize and wheat. Compared to other five widely used tools and its old version, miRDP2 parsed these sRNA data and analyzed miRNA transcriptomes faster with improved accuracy and sensitivity.
Contents of the miRDP2 package 
The miRDP2 package consists of six documented Perl scripts that should be run sequentially by the prepared bash script. Of the six scripts, three (convert_bowtie_to_blast.pl, filter_alignments.pl, and excise_candidate.pl) are inherited from miRDeep-P. The other scripts are modified from the original version. Functions of the six scripts are described in the following:
preprocess_reads.pl filters input reads, including reads that are too long or too short (&#60;19 nt or &#62;25 nt), and reads correlated with Rfam ncRNA sequences, as well as reads with RPM (Reads Per Million) less than 5. The script then retrieves reads correlated to known miRNA mature sequences. The input files are original reads in FASTA/FASTQ format and bowtie2 output of reads mapping to miRNA and ncRNA sequences.
The formula for calculating RPM is as the following:
<img alt="Equation 1" src="/files/ftp_upload/59864/59864equ01.jpg" />
convert_bowtie_to_blast.pl changes the bowtie format into BLAST-parsed format. BLAST-parsed format is a custom tabular separated format derived from standard NCBI BLASToutput format.
filter_alignments.pl filters the alignments of deep sequencing reads to a genome. It filters partial alignments as well as multi-aligned reads (user-specified frequency cutoff). The basic input is a file in BLAST-parsed format.
excise_candidate.pl cuts out potential precursor sequences from a reference sequence using aligned reads as guidelines. The basic input is a file in BLAST-parsed format and a FASTA file. The output is all potential precursor sequences in FASTA format.
mod-miRDP.pl needs two input files, signature file and structure file, which is modified from the core miRDeep-P algorithm by changing the scoring system with plant specific parameters. The input files are dot-bracket precursor structure file and reads distribution signature file.
mod-rm_redundant_meet_plant.pl needs three input files: chromosome_length, precursors and original_prediction generated by mod-miRDP.pl. It generates two output files, non-redundant predicted file and predicted file filtered by newly updated plant miRNA criteria. Details on the format of output file are described in section 1.4.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer/computing node</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Perl is required; at least 8 GB RAM and 100 GB storage are recommended</td>
		</tr>
	</tbody>
</table>

a bioinformatics pipeline to accurately and efficiently analyze the microrna transcriptomes in plants

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in regulating gene expression at the post-transcriptional level. Sequencing sRNA libraries by Next Generation Sequencing (NGS) methods has been widely employed to identify and analyze miRNA transcriptomes in the last decade, resulting in a rapid increase of miRNA discovery. However, two major challenges arise in plant miRNA annotation due to increasing depth of sequenced sRNA libraries as well as the size and complexity of plant genomes. First, many other types of sRNAs, in particular, short interfering RNAs (siRNAs) from sRNA libraries, are erroneously annotated as miRNAs by many computational tools. Second, it becomes an extremely time-consuming process for analyzing miRNA transcriptomes in plant species with large and complex genomes. To overcome these challenges, we recently upgraded miRDeep-P (a popular tool for miRNA transcriptome analyses) to miRDeep-P2 (miRDP2 for short) by employing a new filtering strategy, overhauling the scoring algorithm and incorporating newly updated plant miRNA annotation criteria. We tested miRDP2 against sequenced sRNA populations in five representative plants with increasing genomic complexity, including Arabidopsis, rice, tomato, maize and wheat. The results indicate that miRDP2 processed these tasks with very high efficiency. In addition, miRDP2 outperformed other prediction tools regarding sensitivity and accuracy. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing plant miRNA transcriptomes, therefore a useful tool in helping the community better annotate miRNAs in plants.

The miRNA annotation pipeline, miRDP2, described herein is applied to 10 public sRNA-seq libraries from 5 plant species with gradually increased genome length, including Arabidopsis thaliana, Oryza sativa (rice), Solanum lycopersicum (tomato), Zea mays (maize) and Triticum aestivum (wheat) (Figure 1A). Overall, for each species, 2 representative sRNA libraries from different tissues (collapsed into unique reads, details in the pro...

Watch this Scientific Journal Video about A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants at JoVE.com

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A bioinformatics pipeline, namely miRDeep-P2 (miRDP2 for short), with updated plant miRNA criteria and an overhauled algorithm, could accurately and efficiently analyze microRNA transcriptomes in plants, especially for species with complex and large genomes.

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in ...

miRDeep2 can be used to accurately identify plant microRNAs that are important transcriptional regulators in plant development and are crucial for responding to environmental challenges. miRDeep2 requires a markedly short running time and exhibits an excellent performance in both sensitivity and accuracy especially for predicting microRNA in plants with a large genome. False positives and long processing time are challenges in plant microRNA annotation.By adding a new fielding strategy, overhauling the scoring algorithm and integrating stringent criteria, miRDeep2 can overcome these issues. High confidence microRNA annotation is fundamental for discovering the role of microRNA in regulating diverse function of the genome. Step-by-step guidance can be helpful for first time microRNA annotators.For experienced researchers, the method is useful for understanding the advantages and the benefits of using miRDeep2 over other tools. To install the miRDeep2 package, navigate to the miRDeep2 webpage and fetch the tarball files. Then extract all contents of the downloaded file into one folder and set the folder path to path.To test the miRDeep2 pipeline, download the test data and the expected output containing one formatted GSM sequencing file and one Arabidopsis thaliana genome file and move all of the downloaded files to the current working directory. After extracting the compressed tarball files, build the Arabidopsis genome reference index and the noncoding RNA reference index. A folder will be automatically generated in the user_selected_folder containing all of the intermediate files and results.The miRDeep2 pipeline can then be run using the test data. To check the testing outputs, view the tab delimited output file. The final output of the predicted microRNAs will contain columns that indicate the chromosome ID, strand direction, representative reads ID, precursor ID, mature miRNA location, precursor location, mature sequence and precursor sequence.Then check the progress_log file which provides information about the finished steps and the script_log and script_err files which contain program outputs and warnings. Before running the pipeline, to ensure that the input reads are preprocessed into the proper format, remove adapters from the five and three prime ends of the deep sequencing reads and ensure that all of the FAST A identifiers are unique. Each sequence identifier must end with an underscore x and an integer indicating the copy number of the exact sequence that was retrieved in the deep sequencing datasets.To ensure unique FAST A identifier, include a running number in the ID.To build a reference index, if the genome sequences of the species of interest have been indexed, download Bowtie 2 index files from the iGenomes website. Next, build a non-microRNA noncoding RNA index containing the main noncoding sequences from RNA fam including ribosomal RNA, transfer RNA, small nuclear RNA and small nucleolar RNA to filter out noisy sequences from other noncoding RNA fragments. To use miRDeep2 to detect new microRNAs from deep sequencing data, run the bash script in the package to start the analysis pipeline.The number of different locations a read could be mapped to, the mismatch number for running Bowtie 2 and the threshold of the reads per million can be modified as necessary. To check the miRDeep2 outputs, view the data in the automatically generated output_folder. In this representative analysis, the miRDeep2 microRNA annotation pipeline was applied to 10 public sRNA sequence libraries from five plant species with a gradually increased genome size as indicated.For each species, two representative small RNA libraries from different tissues and their index genome sequences are processed as two inputs. Using previous methods, the genome processing could take over 100 hours or would sometimes halt in the middle of the analysis due to the length of the genome. miRDeep2, however, finish these prediction processes in a markedly shorter time period from minutes to hours.For the two sequenced Arabidopsis small RNAs used in this test, miRDeep2 performed better in both sensitivity and accuracy compared to other tools. Please make sure that the input index for the program is correct. For example, use Bowtie only with a Bowtie index and use a large index option for large genomes.The targets of the resulting microRNA can be predicted using sequencing data which can provide insights into microRNA function. As miRDeep2 can be used to accurately and sensitively identify most microRNA in a specific plant species, the role of microRNA function as a whole can be studied.

Research

JoVE Journal

Genetics

Peptide-derived Method to Transport Genes and Proteins Across Cellular and Organellar Barriers in Plants

The capacity to introduce exogenous proteins and express (or down-regulate) specific genes in plants provides a powerful tool for fundamental research as ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Systemic Delivery of MicroRNA Using Recombinant Adeno-associated Virus Serotype 9 to Treat Neuromuscular Diseases in Rodents

RNA interference via the endogenous miRNA pathway regulates gene expression by controlling protein synthesis through post-transcriptional gene silencing. ...

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been ...

Isolation, Characterization and MicroRNA-based Genetic Modification of Human Dental Follicle Stem Cells

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

Characterizing Histone Post-translational Modification Alterations in Yeast Neurodegenerative Proteinopathy Models

Neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Parkinson&#39;s disease (PD), cause the loss of hundreds of thousands of lives ...

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and ...