Open Access

Bioinformatics resources for deciphering the biogenesis and action pathways of plant small RNAs

Rice201710:38

https://doi.org/10.1186/s12284-017-0177-y

Received: 30 June 2017

Accepted: 3 August 2017

Published: 7 August 2017

Abstract

The next-generation sequencing (NGS) technology has revolutionized our previous understanding of the plant genomes, relying on its innate advantages, such as high throughput and deep sequencing depth. In addition to the protein-coding gene loci, massive transcription signals have been detected within intergenic or intragenic regions. Most of these signals belong to non-coding ones, considering their weak protein-coding potential. Generally, these transcripts could be divided into long non-coding RNAs and small non-coding RNAs (sRNAs) based on their sequence length. In addition to the well-known microRNAs (miRNAs), many plant endogenous sRNAs were collectively referred to as small interfering RNAs. However, an increasing number of unclassified sRNA species are being discovered by NGS. The high heterogeneity of these novel sRNAs greatly hampered the mechanistic studies, especially on the clear description of their biogenesis and action pathways. Fortunately, public databases, bioinformatics softwares and NGS datasets are increasingly available for plant sRNA research. Here, by summarizing these valuable resources, we proposed a general workflow to decipher the RDR (RNA-dependent RNA polymerase)- and DCL (Dicer-like)-dependent biogenesis pathways, and the Argonaute-mediated action modes (such as target cleavages and chromatin modifications) for specific sRNA species in plants. Taken together, we hope that by summarizing a list of the public resources, this work will facilitate the plant biologists to perform classification and functional characterization of the interesting sRNA species.

Keywords

DatabaseSoftwareNext-generation sequencing (NGS)BiogenesisActionPlant small RNA

Review

Introduction

Ten years after the accomplishment of the first plant genome project (Mozo et al., 1999), the advent of the next-generation sequencing (NGS) technology has uncovered an unprecedentedly intricate scene of genome-wide transcription in plants (Varshney et al., 2009; Kelly and Leitch, 2011; Jain, 2012). In addition to the already annotated protein-coding genes, the fact is emerging that millions of the non-coding RNAs (ncRNAs) are transcribed from the intergenic or the intragenic regions (Jiang, 2015; Wendel et al., 2016). These non-coding transcripts could be roughly classified into the long non-coding RNAs (lncRNAs; > 200 nt) (Chekanova, 2015) and the small non-coding RNAs (sRNAs; < 200 nt) (Chen, 2009). Owing to the relatively short read length of NGS, the sRNAs were easier to be cloned at the beginning of the plant ncRNA research. Expectedly, the explosive sRNA world immediately became a hot research topic for the plant biologists. Notably, some of these small transcription “noises”, which were once regarded as the degraded remnants, have been demonstrated to be generated through specific pathways and play essential roles in plant development (Chen, 2009).

One of the well studied sRNA species is microRNA (miRNA) (Jones-Rhoades et al., 2006; Voinnet, 2009). In plants, the transcription of most miRNA genes is driven by RNA polymerase II (Pol II), resulting in the production of the 5′ capped and 3′ poly(A) (polyadenylation)-tailed transcripts called primary microRNAs (pri-miRNAs). Relying on the highly complementary base pairing, stable hairpin-like structures could form within the specific regions of the pri-miRNAs. These local hairpin structures are the featured substrates of Dicer-like 1 (DCL1). Followed by the DCL1-mediated two-step cropping in the nucleus, the pri-miRNAs are sequentially processed into the secondary precursors named as precursor microRNAs (pre-miRNAs), and then into the miRNA/miRNA* duplexes. After exporting to the cytoplasm, the mature miRNAs are selectively loaded into specific Argonaute (AGO)-centered protein complexes. In most cases, the miRNAs will be recruited by AGO1, although some exceptional cases have been reported for “miR172—AGO10” and “miR165/166—AGO10” in Arabidopsis (Arabidopsis thaliana), “miR168—AGO18” in rice (Oryza sativa), and “miR390—AGO7” in both plants (Fang and Qi, 2016). The AGO complex is guided by the recruited miRNA to bind onto a specific target transcript containing a region highly complementary to the miRNA. There are two major action modes of miRNA-guided gene silencing in plants. One is target cleavage which is considered as the most common mode (Voinnet, 2009), and the other is translational repression which has been observed in several studies (Chen, 2004; Gandikota et al., 2007; Li et al., 2013). Another class of plant sRNAs is collectively referred to as the small interfering RNAs (siRNAs), which could be further classified into heterochromatic small interfering RNAs (hc-siRNAs), trans-acting small interfering RNAs (ta-siRNAs), natural antisense transcript-derived small interfering RNAs (nat-siRNAs), and phased small interfering RNAs (phasiRNAs). Specifically, hc-siRNAs are encoded within the heterochromatic loci transcribed by RNA Pol IV. The single-stranded Pol IV transcripts are converted to double-stranded precursors through the RDR2 (RNA-dependent RNA polymerase 2)-dependent pathway. Then, the precursors are processed by DCL3 for the production of the 24-nt hc-siRNAs. The hc-siRNAs are incorporated into AGO4 to perform site-specific chromatin modifications (Xie et al., 2004; Qi et al., 2005; Henderson et al., 2006). In Arabidopsis, there are four TAS gene loci encoding ta-siRNAs. MiRNA-mediated cleavages (miR173 for TAS1 and TAS2, miR390 for TAS3, and miR828 for TAS4) of the primary TAS transcripts are the prerequisite for initiating ta-siRNA production. Through the RDR6-dependent pathway, the cleaved TAS transcripts are converted to double-stranded precursors which will be subject to ta-siRNA processing by DCL4. Finally, most of the ta-siRNAs are loaded into AGO1 silencing complexes to guide target cleavages (Peragine et al., 2004; Vazquez et al., 2004; Allen et al., 2005; Gasciolli et al., 2005; Xie et al., 2005; Yoshikawa et al., 2005; Rajagopalan et al., 2006). For the nat-siRNAs, genome-wide studies in both Arabidopsis and rice showed that they were originated from the overlapping regions of the natural antisense transcript (NAT) pairs through the DCL1-dependent pathway or through the Pol IV-, RDR2-, and DCL3-dependent pathway (Zhang et al., 2012). Moreover, in Borsani et al.’s study (2005), 21- and 24-nt nat-siRNAs were demonstrated to be produced from a cis-NAT pair through the RDR6- and DCL1/2-dependent pathway (Borsani et al., 2005). A major class of phasiRNAs was identified in the reproductive tissues of Gramineae species, such as rice (Johnson et al., 2009) and maize (Zea mays) (Zhai et al., 2015). Notably, in rice, the processing of 21-nt phasiRNAs was highly dependent on DCL4, while the processing of 24-nt ones required the activity of DCL3b (Song et al., 2012). Komiya and his colleagues (2014) reported that a portion of the DCL4-dependent, 21-nt phasiRNAs preferentially associated with MEL1 (the ortholog of Arabidopsis AGO5), and these 5′ C-started phasiRNAs originated from hundreds of lincRNA (long intergenic non-coding RNA) loci. In addition to the above mentioned sRNAs, some non-canonical sRNA species have been discovered, such as the AGO4-associated long siRNAs of 25-nt in length (Zilberman et al., 2003), the DCL3-dependent, AGO4-associated, 24-nt miRNAs called long miRNAs (Wu et al., 2010), the Pol IV- and DCL2/3/4-dependent, AGO2-associated double-strand break-induced sRNAs (Wei et al., 2012), the DCL-independent, AGO4-associated, 20- to 60-nt siRNAs (Ye et al., 2016), and the intron-derived, DCL2/3/4-dependent siRNAs (Chen et al., 2011). For a clear summarization, Fig. 1a provides a brief framework of the biogenesis and action pathways of the plant sRNAs. However, all of the recent discoveries just witnessed the emergence of the unexpectedly huge and complicated RNA world. It is still far from thorough understanding of the biogenesis and action pathways of the enormous sRNA population.
Fig. 1

A general workflow for public bioinformatics resource-based investigation of small RNA (sRNA) biogenesis and action pathways in plants. a Presents a brief framework of the biogenesis and action pathways of plant sRNAs. AGO: Argonaute; Pol: RNA polymerase; RDR: RNA-dependent RNA polymerase; DCL: Dicer-like; sRNA: small RNA. b The analysis is divided into five sections according to the step-by-step instructions in the main text, including “genomic features”, “transcription”, “precursors and processing”, “sRNA action modes” and “functional studies”

Fortunately, the valuable public resources have become increasingly available for the mechanistic studies on the plant sRNAs. Here, by taking the two model plants Arabidopsis and rice as an example, we provided a list of the currently available resources to the plant biologists, including the public databases, the bioinformatics softwares and the NGS datasets. Notably, most of the bioinformatics softwares listed here are online tools with user-friendly interface. By proposing a workflow for analyzing the biogenesis and action pathways of the plant sRNAs, we made a clear description for the specific applications of different sequencing datasets and bioinformatics toolkits at each analytical step. Finally, we anticipate that this workflow along with the list could advance the efficiency of data analysis and interpretation, thus facilitating the experimental design for the functional studies on the plant sRNAs. Below, we will introduce the public resources step by step according to the workflow shown in Fig. 1b.

Genomic features and transcription

By using the BLAST tool provided by the plant genomic databases, such as TAIR (the Arabidopsis information resource) (Huala et al., 2001) for Arabidopsis and RGAP (rice genome annotation project) (Kawahara et al., 2013) or RAP-DB (the rice annotation project database) (Ohyanagi et al., 2006) for rice (Table 1), the genomic positions of the sRNA-coding loci could be obtained, facilitating the researchers to tell whether the sRNA loci are intergenic or intragenic. For mapping huge sRNA sequencing (sRNA-seq) datasets onto a plant genome, Bowtie should be selected as one of the powerful tools (Langmead et al., 2009). miRBase (the microRNA database) (Griffiths-Jones et al., 2006) and PLncDB (plant long non-coding RNA database) (Jin et al., 2013) are useful to check whether the sRNA is originated from a miRNA precursor or a lncRNA. Besides, ShortStack should be a useful tool to analyze the sRNA-seq data based on the available reference genomes (Axtell, 2013) (Table 2). It can output reports showing sRNA size distributions, repetitiveness, hairpin-association and phasing. One of its shortage is the requirement of bioinformatics experts for local installation and running.
Table 1

List of databases for plant small RNA research

Database

URL

Description

Reference

TAIR (the Arabidopsis information resource)

www.arabidopsis.org/

Genomic information database of Arabidopsis

(Huala et al., 2001)

RGAP (rice genome annotation project)

rice.plantbiology.msu.edu/

Genomic information database of rice

(Kawahara et al., 2013)

RAP-DB (the rice annotation project database)

rapdb.dna.affrc.go.jp/

(Ohyanagi et al., 2006)

Phytozome

phytozome.jgi.doe.gov/pz/portal.html

Genomic information of diverse plant species

(Goodstein et al., 2012)

miRBase (the microRNA database)

www.mirbase.org/

MicroRNA database of diverse organisms

(Griffiths-Jones et al., 2006)

PLncDB (plant long non-coding RNA database)

chualab.rockefeller.edu/gbrowse2/homepage.html

Long non-coding RNA database of Arabidopsis

(Jin et al., 2013)

PNRD (a plant non-coding RNA database)

(structuralbiology.cau.edu.cn/PNRD)

non-coding RNA database of diverse plant species

(Yi et al., 2015)

GEO (gene expression omnibus)

www.ncbi.nlm.nih.gov/geo/

Public repositories for next-generation sequence data

(Edgar et al., 2002)

SRA (sequence read archive)

www.ncbi.nlm.nih.gov/sra/

(Kodama et al., 2012)

DDBJ (DNA Data Bank of Japan) Sequence Read Archive

trace.ddbj.nig.ac.jp/dra/index_e.html

(Kaminuma et al., 2010)

Next-Gen Sequence Databases

mpss.danforthcenter.org/index.php

Public repositories for plant next-generation sequence data

(Nakano et al., 2006)

ASRP (Arabidopsis small RNA project)

asrp.danforthcenter.org/

(Gustafson et al., 2005)

CSRDB (cereal small RNAs database)

sundarlab.ucdavis.edu/smrnas/

(Johnson et al., 2007)

PlantNATsDB (plant natural antisense transcripts database)

bis.zju.edu.cn/pnatdb/

Database of plant natural antisense transcripts

(Chen et al., 2012)

mirEX (Arabidopsis pri-miRNA expression atlas)

www.combio.pl/mirex1/

Databases containing expression data of plant microRNA precursors

(Bielewicz et al., 2012)

PmiRKB (plant microRNA knowledge base)

bis.zju.edu.cn/pmirkb/

(Meng et al., 2011a)

AVT (AtGenExpress visualization tool)

jsp.weigelworld.org/expviz/expviz.jsp

Arabidopsis gene expression databases with user-friendly interface

(Kilian et al., 2007; Goda et al., 2008)

Arabidopsis eFP Browser

bar.utoronto.ca/efp_arabidopsis/cgi-bin/efpWeb.cgi

(Winter et al., 2007)

PceRBase (plant ceRNA database)

bis.zju.edu.cn/pcernadb/index.jsp

Database of plant competing endogenous RNAs

(Yuan et al., 2017)

Arabidopsis epigenome maps

neomorph.salk.edu/epigenome/epigenome.html

Arabidopsis epigenome maps

(Lister et al., 2008)

The SIGnAL Arabidopsis Methylome Mapping Tool

signal.salk.edu/cgi-bin/methylome

(Zhang et al., 2006)

UCSC Genome Browser on Arabidopsis thaliana (2004)

epigenomics.mcdb.ucla.edu/cgi-bin/hgTracks?clade = plant&org = A. + thaliana

(Zhang et al., 2006; Zhang et al., 2007)

UCSC Genome Browser on Arabidopsis thaliana (2009)

genomes.mcdb.ucla.edu/cgi-bin/hgTracks?db = araTha2

(Stroud et al., 2013)

Rice epigenome maps

(plantgenomics.biology.yale.edu)

Rice epigenome maps

(Li et al., 2008)

Plant Methylome DB

epigenome.genetics.uga.edu/PlantMethylome/

Database including epigenome data of 40 wild type plant species

(presented by the Schmitz lab at the University of Georgia)

 

Note: the currently invalid URL is noted by parentheses

Table 2

List of softwares for plant small RNA research

Software

URL

Description

Reference

PlantCARE (a plant cis-acting regulatory element database)

bioinformatics.psb.ugent.be/webtools/plantcare/html/

Plant gene promoter analysis

(Rombauts et al., 1999)

PLACE (a database of plant cis-acting regulatory DNA elements)

www.dna.affrc.go.jp/htdocs/PLACE/

(Higo et al., 1998)

JASPAR (an open-access database for eukaryotic transcription factor binding profiles)

jaspar.genereg.net

(Mathelier et al., 2016)

The MEME suite (containing motif-based sequence analysis tools)

meme-suite.org

(Bailey et al., 2015)

Bowtie

bowtie-bio.sourceforge.net/index.shtml

An ultrafast, memory-efficient short read aligner

(Langmead et al., 2009)

Bowtie 2

bowtie-bio.sourceforge.net/bowtie2/index.shtml

An ultrafast and memory-efficient tool for aligning relatively long sequencing reads to long reference sequences

(Langmead and Salzberg, 2012)

ShortStack

github.com/MikeAxtell/ShortStack/releases/

A Perl program for comprehensive annotation and quantification of small RNA genes

(Axtell, 2013)

NATpipe

www.bioinfolab.cn/NATpipe/NATpipe.zip

Natural antisense transcript prediction

(Yu et al., 2016a)

RNAfold webserver

rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi

RNA secondary structure prediction

(Hofacker, 2003)

RNAshapes

bibiserv.cebitec.uni-bielefeld.de/download/tools/rnashapes.html

(Steffen et al., 2006)

miTRATA (microRNA truncation and tailing analysis)

wasabi.ddpsc.org/~apps/ta/index.php

3′ modification analysis of plant small RNAs

(Patel et al., 2016)

WebLogo

weblogo.threeplusone.com/

Search for the conserved sequence motifs

(Crooks et al., 2004)

psRNATarget (a plant small RNA target analysis server)

plantgrn.noble.org/psRNATarget/

Target prediction tools for plant small RNAs

(Dai and Zhao, 2011)

Small RNA Target Prediction

wasabi.ddpsc.org/~apps/tp/

(Jones-Rhoades and Bartel, 2004)

TAPIR (target prediction for plant microRNAs)

bioinformatics.psb.ugent.be/webtools/tapir/

Not only target prediction, also target mimic prediction for plant microRNAs

(Bonnet et al., 2010)

comPARE (PARE validated miRNA targets)

mpss.danforthcenter.org/tools/mirna_apps/comPARE.php

Degradome-seq data-based validation for plant microRNA—target pairs

(Kakrana et al., 2014)

sPARTA-Web (small RNA-PARE target analyzer)

mpss.danforthcenter.org/tools/mirna_apps/sparta.php

Degradome-seq data-based validation for plant small RNA—target pairs

CleaveLand4

github.com/MikeAxtell/CleaveLand4/releases

A Perl program for degradome-seq data-based validation for plant small RNA—target pairs

(Addo-Quaye et al., 2009)

agriGO (a GO analysis toolkit for the agricultural community)

bioinfo.cau.edu.cn/agriGO/index.php

Functional analysis of target genes based on Gene Ontology annotations

(Du et al., 2010)

Cytoscape

www.cytoscape.org

Network data integration, analysis, and visualization

(Shannon et al., 2003)

Gephi

gephi.org

(Bastian et al., 2009)

Note: the currently invalid URL is noted by parentheses

Here, the workflow for analyzing the sRNA biogenesis and action pathways is proposed based on the scenario that the sRNAs are processed from their precursors transcribed from specific genomic loci (Fig. 1). If the sRNA precursor was experimentally cloned by using fine-scale methods such as RACE (rapid amplification of cDNA ends), the transcription boundary of the precursor-coding locus could be defined. In this case, the upstream region of user-defined length could be retrieved from the above mentioned genomic database, and be treated as the promoter region of this gene locus for cis-element analysis by using PlantCARE (a plant cis-acting regulatory element database) (Rombauts et al., 1999), PLACE (Plant cis-acting regulatory DNA elements database) (Higo et al., 1998), or the newly updated tools JASPAR (Mathelier et al., 2016) and the MEME suite (Bailey et al., 2015) (Table 2). The prediction results from these online tools might provide some valuable hints to infer the basic transcriptional features of this gene locus. For example, the coexistence of CAAT-box and TATA-box within the upstream region near to the transcription start site indicates the Pol II-drived transcription of the host gene (Lewin, 1990). Of course, we should acknowledge that the fine-scale cloning of the sRNA precursors is time consuming and laborious. The high-throughput solution is by analyzing the publicly available RNA sequencing (RNA-seq) data. Notably, distinct types of RNA-seq libraries were prepared with different purposes. For example, in a recent study, the poly(A)-tailed RNA-seq libraries were constructed for the detection of Pol II-dependent transcripts, while the rRNA-depleted total RNA-seq libraries were prepared for the identification of Pol IV-dependent transcripts (Li et al., 2015). After mapping such kind of RNA-seq data (Table 3) onto the plant genome by using a high-throughput alignment tool, Bowtie 2 (Langmead and Salzberg, 2012) for example, the transcription boundaries of the sRNA precursor-coding loci could be delineated. Also based on the mapping result, the RNA polymerase dependence could be partially determined for the loci. Moreover, some of the sRNA-seq datasets, such as those originated from the nrpd1 mutant (a Pol IV mutant) (Table 3), could also be used to investigate the polymerase dependence of the sRNA-coding loci. Notably, compared to Bowtie, Bowtie 2 is particularly efficient for long read (up to hundreds of nucleotides in length) mapping. Thus, Bowtie 2 is recommended to be employed for RNA-seq data analysis, while Bowtie is more suitable for sRNA-seq read mapping as mentioned above.
Table 3

List of sequencing data for plant small RNA research

Data type

Species

Dataset ID

Descriptiona

Reference

RNA-seq

Arabidopsis (Col-0)

GSE57215

GSM1377353

dcl234 rep1

(Li et al., 2015)

GSM1377354

dcl234 rep2

GSM1377355

dcl234 rep3

GSM1377356

dcl234 nrpd1 rep1

GSM1377357

dcl234 nrpd1 rep2

GSM1377358

dcl234 nrpd1 rep3

GSM1377359

dcl234 DSN

GSM1377360

dcl234 nrpd1 DSN

GSM1377361

dcl234 rdr2 DSN

GSM1377362

dcl234 PolyA+ rep1

GSM1377363

dcl234 PolyA+ rep2

GSM1377364

dcl234 nrpd1 PolyA+ rep1

GSM1377365

dcl234 nrpd1 PolyA+ rep2

GSM1377366

dcl234 PolyA- rep1

GSM1377367

dcl234 PolyA- rep2

GSM1377368

dcl234 nrpd1 PolyA- rep1

GSM1377369

dcl234 nrpd1 PolyA- rep2

Rice

GSE50778

GSM1229044

Nipponbare

(Wei et al., 2014)

GSM1229045

Nipponbare dcl3a RNAi-1

 

GSM1229046

Nipponbare dcl3a RNAi-3

 

DsRNA-seq

Arabidopsis

GSE23439

GSM575243

WT 1 × ribominus

(Zheng et al., 2010)

GSM575244

WT 2 × ribominus

GSM575245

rdr6

GSE57215

GSM1377347

dcl234 unopened flower buds rep1

(Li et al., 2015)

GSM1377348

dcl234 unopened flower buds rep2

GSM1377349

dcl234 unopened flower buds rep3

GSM1377350

dcl234 nrpd1 unopened flower buds rep1

GSM1377351

dcl234 nrpd1 unopened flower buds rep2

GSM1377352

dcl234 nrpd1 unopened flower buds rep3

Degradome-seq

Arabidopsis (Col-0)

GSE77549

GSM2054358

WT 11-day-old seedlings

(Hou et al., 2016)

GSM2054359

WT inflorescences

GSM2253889

WT inflorescences rep1

GSM2253892

WT inflorescences rep2

GSM2253890

rdr6 inflorescences rep1

GSM2253893

rdr6 inflorescences rep2

GSM2253891

ago7 inflorescences rep1

GSM2253894

ago7 inflorescences rep2

GSE52342

GSM1263708

WT inflorescences

(Creasey et al., 2014)

GSM1263709

ddm1-2 inflorescences

GSM1263710

rdr6–15 inflorescences

GSM1263711

ddm1–2 rdr6–15 inflorescences

GSE47121

GSM1145327

Flower buds rep1

(Willmann et al., 2014)

GSM1145328

Flower buds rep2

GSE11007

GSM278333

Inflorescences, dT primed

(Addo-Quaye et al., 2008)

GSM278334

Inflorescences, dT primed

GSM278335

Inflorescences, random primed

GSM278370

Seedlings, random primed

GSE11094

GSM280226

WT inflorescences

(German et al., 2008)

GSM280227

xrn4 inflorescences

GSE55151

GSM1330569

Young leaves

(Thatcher et al., 2015)

GSM1330570

Mature leaves

GSM1330571

Early senescence leaves rep 1

GSM1330573

Early senescence leaves rep 2

GSM1330572

Late senescence leaves rep 1

GSM1330574

Late senescence leaves rep 2

GSE11070

GSM284751

WT Flowers rep1

(Gregory et al., 2008)

GSM284752

ein5-6 flowers rep1

GSE71913

GSM1847333

Unopened flower buds, abh1–1

(Yu et al., 2016b)

GSM1847334

Unopened flower buds, abh1–8

GSM1939001

Leaves, treated with translation inhibitor CHX rep1

GSM1939002

Leaves, treated with translation inhibitor CHX rep2

GSE66224

GSM1617433

Immature inflorescences rep1

(Vandivier et al., 2015)

GSM1617434

Immature inflorescences rep2

Rice

GSE42467

GSM1040649

Young panicles of ZH11 (japonica) at high temperature

 

GSE66610

GSM1626143

Nipponbare Leaves

(Baldrich et al., 2015)

GSM1626145

Nipponbare Leaves

GSE62334

GSM1525457

Nipponbare leaves

 

GSE17398

GSM434596

Nipponbare seedlings

(Li et al., 2010)

GSE19050

GSM476257

93-11 (indica) young inflorescences

(Zhou et al., 2010)

GSE18251

GSM455938

Nipponbare seedlings

(Wu et al., 2009)

GSM455939

Nipponbare inflorescences

SRNA-seq

Arabidopsis (Col-0)

GSE57215

GSM1377370

WT rep1

(Li et al., 2015)

GSM1377371

WT rep2

GSM1377372

nrpd1 rep1

GSM1377373

nrpd1 rep2

GSM1377374

dcl3 rep1

GSM1377375

dcl3 rep2

GSM1377376

rdr2 rep1

GSM1377377

rdr2 rep2

GSM1377378

dcl234 rep1

GSM1377379

dcl234 rep2

GSM1377380

dcl234 nrpd1 rep1

GSM1377381

dcl234 nrpd1 rep2

GSE23439

GSM575246

WT

(Zheng et al., 2010)

GSM575247

rdr6

GSE14695

GSM366868

Whole aerials

(Fahlgren et al., 2009)

GSM366869

Whole aerials dcl1–7

GSM366870

Whole aerials dcl2–1dcl3-1dcl4–2

GSE44622

GSM1087973

WT, flowers rep1

(Jeong et al., 2013)

GSM1087974

WT, flowers rep2

GSM1087975

dcl1–7, flowers rep 1

GSM1087976

dcl1–7, flowers rep 2

GSM1087977

dcl234, flowers rep 1

GSM1087978

dcl234, flowers rep 2

GSM1087979

rdr2, flowers rep 1

GSM1087980

rdr2, flowers rep 2

GSE35562

GSM1178880

WT flowers, rep1

(Zhai et al., 2013)

GSM1178881

WT flowers, rep2

GSM1178882

WT flowers, rep3

GSM1178883

hen1–8 flowers, rep1

GSM1178884

hen1–8 flowers, rep2

GSM1178885

hen1–8 flowers, rep3

GSE26161

GSM642337

sRNAs cloned from total RNA

(Zhang et al., 2011)

GSM642338

sRNAs cloned from AGO2

GSE28591

GSM707678

WT, flowers

(Wang et al., 2011)

GSM707679

WT, leaves

GSM707680

WT, roots

GSM707681

WT, seedlings

GSM707682

AGO1-associated sRNAs, flowers

GSM707683

AGO1-associated sRNAs, leaves

GSM707684

AGO1-associated sRNAs, roots

GSM707685

AGO1-associated sRNAs, seedlings

GSM707686

AGO4-associated sRNAs, flowers

GSM707687

AGO4-associated sRNAs, leaves

GSM707688

AGO4-associated sRNAs, roots

GSM707689

AGO4-associated sRNAs, seedlings

GSE39885

GSM980695

sRNAs cloned from total RNA

(Zhu et al., 2011)

GSM980697

sRNAs cloned from AGO10

GSE16545

GSM415783

sRNAs cloned from total RNA, flowers

(Havecker et al., 2010)

GSM415784

sRNAs cloned from total RNA, flowers

GSM415785

sRNAs cloned from total RNA, flowers

GSM415791

sRNAs cloned from AGO9, flowers

GSM415792

sRNAs cloned from AGO9, flowers

GSE10036

GSM253622

sRNAs cloned from AGO1

(Mi et al., 2008)

GSM253623

sRNAs cloned from AGO2

GSM253624

sRNAs cloned from AGO4

GSM253625

sRNAs cloned from AGO5

GSE12037

GSM304282

sRNAs cloned from total RNA (AGO2 mock)

(Montgomery et al., 2008)

GSM304283

sRNAs cloned from AGO2

GSM304284

sRNAs cloned from total RNA (AGO7 mock)

GSM304285

sRNAs cloned from AGO7

Rice

GSE20748

GSM520640

Nipponbare WT seedlings

(Wu et al., 2010)

GSM520639

Nipponbare rdr2 seedlings

GSM520637

Nipponbare dcl1 seedlings

GSM520638

Nipponbare dcl3 seedlings

GSE26405

GSM648139

ZH11 (japonica) high temperature panicles

(Song et al., 2012b)

GSM648140

ZH11 (japonica) rdr6 high temperature panicles

GSM648141

ZH11 (japonica) low temperature panicles

GSM648142

ZH11 (japonica) rdr6 low temperature panicles

GSE22763

GSM562942

93–11 (indica) WT seedlings

(Song et al., 2012a)

GSM562944

93-11 (indica) dcl4–1 seedlings

GSM562943

93–11 (indica) WT panicles

GSM562945

93–11 (indica) dcl4–1 panicles

GSE35562

GSM913524

Dongjin hen1–3, leaves

(Zhai et al., 2013)

GSM913525

Dongjin WT, leaves

GSE50778

GSM1229047

Nipponbare WT

(Wei et al., 2014)

GSM1229048

Nipponbare dcl3a, RNAi-1

GSM1229049

Nipponbare dcl3a, RNAi-3

GSE32973

GSM816687

Nipponbare seedlings rep1

(Jeong et al., 2011)

GSM816688

Nipponbare seedlings rep2

GSM816689

Nipponbare seedlings rep3

GSM816700

Nipponbare seedlings dcl1 RNAi rep1–1

GSM816701

Nipponbare seedlings dcl1 RNAi rep1–2

GSM816702

Nipponbare seedlings dcl1 RNAi rep2–1

GSM816703

Nipponbare seedlings dcl1 RNAi rep2–2

GSM816730

Nipponbare panicles rep1–1

GSM816731

Nipponbare panicles rep1–2

GSM816732

Nipponbare panicles rep2

GSM816745

Nipponbare panicles dcl1 RNAi rep1–1

GSM816746

Nipponbare panicles dcl1 RNAi rep1–2

GSM816747

Nipponbare panicles dcl1 RNAi rep2–1

GSM816748

Nipponbare panicles dcl1 RNAi rep2–2

GSE20748

GSM520640

Nipponbare seedlings, sRNAs cloned from total RNA

(Wu et al., 2010)

GSM520634

Nipponbare seedlings, sRNAs cloned from AGO4a

GSM520635

Nipponbare seedlings, sRNAs cloned from AGO4b

GSM520636

Nipponbare seedlings, sRNAs cloned from AGO16

GSE18250

GSM455962

Nipponbare seedlings, sRNAs cloned from AGO1a

(Wu et al., 2009)

GSM455963

Nipponbare seedlings, sRNAs cloned from AGO1b

GSM455964

Nipponbare seedlings, sRNAs cloned from AGO1c

PRJNA273330

SRX847816

Nippbare sRNAs cloned from AGO1a Rep1

 

SRX847817

Nippbare sRNAs cloned from AGO1a Rep2

SRX847818

Nippbare sRNAs cloned from AGO1b Rep1

SRX847819

Nippbare sRNAs cloned from AGO1b Rep2

SRX847820

Nippbare sRNAs cloned from AGO18 Rep1

SRX847821

Nippbare sRNAs cloned from AGO18 Rep2

DRP000161

DRX000196

sRNA-IP in WT (Nipponbare)

(Komiya et al., 2014)

DRX000197

sRNA-IP in mel1 (Nipponbare)

DRX000198

Total sRNA in WT (Nipponbare)

DRX000199

Total sRNA in mel1 (Nipponbare)

aPlease see detailed descriptions of the datasets in the related references

Precursor formation and processing

As introduced above, there are two major forms of sRNA precursors that could be processed by DCLs, i.e. the long double-stranded RNA (dsRNA) precursors and the single-stranded RNAs with short internal double-stranded regions (Fig. 1). The former ones could be synthesized either through the RDR-dependent (such as the precursors of the hc-siRNAs or the ta-siRNAs) pathway or through the RDR-independent (such as the NATs) pathway. However, the later ones are unexceptionally generated through the RDR-independent pathways (such as the pri-miRNAs and the sirtrons). Thus, distinct bioinformatics toolkits should be selected to identify the sRNA precursors belonging to the two different types, respectively.

PlantNATsDB (plant natural antisense transcripts database) (Chen et al., 2012) provides the users with the genome-wide prediction results of both cis- and trans-NAT pairs of 70 plant species. Gene locus ID could be used as a query to see the possibility of this gene to form NAT pairs with other genes. Optionally, “batch download” could be selected to obtain the complete list of the predicted NAT pairs of a plant species. In the other way, the researchers could perform large-scale NAT prediction by using the program NATpipe (Yu et al., 2016a). The genome-independent feature of this software allows users to carry out NAT prediction solely based on the RNA-seq data. If the genomic information is available, then the predicted NATs could be classified into cis and trans ones. Additionally, if the sRNA-seq data is available, phase-distributed nat-siRNAs could be identified from the predicted NATs by using NATpipe.

RNAfold (Hofacker, 2003) and RNAshapes (Steffen et al., 2006) are both easy-to-use tools for local RNA secondary structure predictions. RNAfold is a web server allowing a query sequence of up to 10,000 nt in length, but its graphic outputs are difficult to be modified according to the users’ requirements. RNAshapes is a locally installed program with a strict length limitation (up to ~400 nt based our experience) of an input sequence. However, the outputs of RNAshapes could be graphically edited.

Recently, NGS-based, transcriptome-wide strategies have been developed to probe the RNA secondary structures, such as dsRNA sequencing (dsRNA-seq) (Kwok et al., 2015). The dsRNA-seq library is prepared by treating the total RNAs with the ribonuclease specific for the single-stranded RNAs, thus enabling researchers to detect the annealed region within an RNA sequence, or between two transcripts. Currently, the plant dsRNA-seq data is only available for Arabidopsis. These dsRNA-seq datasets were prepared not only from the wild type (WT) plants, but also from the nrpd1 and rdr6 mutants (Table 3). Thus, the Pol IV and RDR6 dependence of the dsRNA precursors could be interrogated by using these datasets. In addition to the dsRNA-seq data, sRNA-seq data of nrpd1, rdr2 and rdr6 should also be valuable to investigate the biogenesis pathways of the sRNA precursors (Table 3).

DCLs have been demonstrated to be widely implicated in the processing of diverse sRNA precursors in plants (Chen, 2009). Thus, by comparing to the sequencing data of WT, the public sRNA-seq data of the dcl mutants could be used to investigate the specific DCL-mediated sRNA processing pathways.

sRNA action modes and network construction

According to the current understanding, target cleavages and chromatin modifications are the two major action modes of the plant sRNAs. And, these two distinct regulatory pathways are largely predetermined by the association of the sRNAs with specific AGO complexes (Fang and Qi, 2016). Thus, AGO enrichment analysis is necessary for functional studies on the plant sRNAs. To date, sequencing data of the AGO-associated sRNA populations has been reported by several research groups (Table 3). In Arabidopsis, AGO1-, AGO2-, AGO4-, AGO5-, AGO7-, AGO9- and AGO10-associated sRNA sequencing datasets are available (Mi et al., 2008; Montgomery et al., 2008; Havecker et al., 2010; Wang et al., 2011; Zhu et al., 2011). And in rice, AGO1-, AGO4-, MEL1-, AGO16- and AGO18-associated sRNA sequencing datasets have been published (Wu et al., 2009; Wu et al., 2010; Komiya et al., 2014). By comparing the level of a sRNA in a specific AGO complex to that in the total RNA extract, whether this sRNA is enriched in the AGO complex could be determined. The result could facilitate the researchers to deduce the action mode of this sRNA.

A large portion of the miRNAs and some of the siRNAs such as the ta-siRNAs are incorporated into AGO1. These AGO1-associated sRNAs can recognize the highly complementary regions on the target transcripts, and inhibit gene expression through target cleavages. The high complementarity between the sRNA and its target forms an essential basis for the development of the target prediction tools. For plants, there are several user-friendly online tools for target prediction (Table 2), such as psRNATarget (a plant small RNA target analysis server) (Dai and Zhao, 2011), Small RNA Target Prediction (Jones-Rhoades and Bartel, 2004), and TAPIR (target prediction for plant microRNAs) (Bonnet et al., 2010). Compared to TAPIR, the former two tools are more flexible for different users’ purposes. By using psRNATarget or Small RNA Target Prediction, the users can select one of the cDNA libraries provided by the tools, or can upload their own cDNA sequences for target prediction. However, TAPIR does not provide the pre-stored cDNA libraries for the users. Additionally, much more parameters are adjustable before performing analysis by using the former two tools. Thus, psRNATarget and Small RNA Target Prediction should be the efficient and easy-to-use tools for plant sRNA target prediction.

The 3′ cleavage remnants from the target transcripts are relatively stable in vivo, and could be detected by sequencing. This kind of high-throughput sequencing technology was called GMUCT (global mapping of uncapped and cleaved transcripts) (Willmann et al., 2014) or PARE (parallel analysis of RNA ends) (German et al., 2008; German et al., 2009), which is collectively referred to as degradome sequencing (degradome-seq) here. As listed in Table 3, there are many degradome-seq datasets available to perform large-scale validation of the predicted sRNA targets. For analyzing the degradome-seq data, comPARE (PARE validated miRNA targets) and sPARTA-Web (small RNA-PARE target analyzer) (Kakrana et al., 2014) might be the easy-to-use online tools for the wet-lab researchers (Table 2). The difference between comPARE and sPARTA-Web is that the former was designed specifically for the miRNA target validation whereas the latter was developed for all of the sRNA target candidates. CleaveLand4 (Addo-Quaye et al., 2009) is also suitable for degradome-seq data-based validation of the sRNA targets. However, it is a Perl program, which requires extensive support from bioinformatics experts for local installation and running. Besides, our previously proposed workflow could also be referenced for degradome-seq data-based sRNA target validation (Meng et al., 2011b).

The AGO4-associated sRNAs, such as the hc-siRNAs, repress gene expression through chromatin modifications (Chen, 2009; Fang and Qi, 2016). By using BLAST or Bowtie, the genomic regions highly complementary to the AGO4-associated sRNAs could be identified with a genome-wide scale. Then, it will be interesting to investigate the chromatin status surrounding the complementary sites. Several epigenome databases are available for Arabidopsis, such as Arabidopsis epigenome maps (Lister et al., 2008), the SIGnAL Arabidopsis Methylome Mapping Tool (Zhang et al., 2006), and the Arabidopsis epigenome data displayed in the UCSC Genome Browser (Zhang et al., 2006; Zhang et al., 2007; Stroud et al., 2013). In some databases, in addition to the WT data, the epigenomes of diverse mutants are also available, which might be valuable to inspect the sRNA-guided chromatin modification pathways in detail. Although the rice epigenome data was reported nearly ten years ago (Li et al., 2008), and the database was established at that time, the web link is no long active. Fortunately, Plant Methylome DB provides researchers with the WT epigenomes of 40 species including Arabidopsis and rice.

“Target mimicry” was reported as a novel pathway for the regulation of the miRNA activities by the target mimics (Franco-Zorrilla et al., 2007). Although the online server TAPIR is not superior in sRNA target prediction, it provides a unique functional module for target mimic prediction (Bonnet et al., 2010). Besides, the recently established PceRBase (plant ceRNA database) stores the lists of the competing endogenous RNAs (similar to the target mimics) of 26 plant species for the users (Yuan et al., 2017).

Finally, researchers could construct a sRNA-centered regulatory network involving sRNA targets and target mimics by using Cytoscape (Shannon et al., 2003) or Gephi (Bastian et al., 2009). The expression levels of the sRNA precursors, the sRNA target genes and the target mimics could be partially uncovered by visiting PmiRKB (plant microRNA knowledge base) (Meng et al., 2011a), mirEX (Arabidopsis pri-miRNA expression atlas) (Bielewicz et al., 2012), AVT (AtGenExpress visualization tool) (Kilian et al., 2007; Goda et al., 2008), and Arabidopsis eFP Browser (Winter et al., 2007). The biological functions of the sRNA target genes could be analyzed by using agriGO (Du et al., 2010).

Conclusions

In the present work, we proposed a general workflow for deciphering the biogenesis and action pathways of the plant sRNAs by using a series of publicly available resources. Most of the recently reported sRNA-seq and dsRNA-seq datasets of Arabidopsis and rice were summarized in Table 3, emphasizing their importance for elucidating the RDR- and DCL-dependent biogenesis pathways of the plant endogenous sRNAs. However, we should acknowledged that several useful toolkits have not been included in the list of softwares for plant small RNA research. For example, the UEA sRNA workbench, that is downloadable for local installation, provides an user-friendly platform for sRNA-seq data processing (Stocks et al., 2012). It contains several useful tools, such as “adaptor remover” and “Filter” for sRNA-seq data pre-treatment, “miRCat” and “hairpin annotation” for miRNA prediction, and the ta-siRNA prediction tool. Besides, as noticed in Tables 1 and 2, several valuable databases and bioinformatics tools, including Rice epigenome maps and PNRD, are currently terminated for unknown reason. We hope that these useful resources could be activated again for the plant biologists. Summarily, more research efforts, from both the bioinformaticians and the experimental practitioners, are anticipated to devote to the plant sRNA research.

Abbreviations

AGO: 

Argonaute

AVT: 

AtGenExpress visualization tool

DCL: 

Dicer-like

degradome-seq: 

degradome sequencing

dsRNA: 

double-stranded RNA

dsRNA-seq: 

double-stranded RNA sequencing

GMUCT: 

Global mapping of uncapped and cleaved transcripts

hc-siRNA: 

heterochromatic small interfering RNA

lincRNA: 

long intergenic non-coding RNA

lncRNA: 

long non-coding RNA

miRNA: 

microRNA

NAT: 

Natural antisense transcript

nat-siRNA: 

natural antisense transcript-derived small interfering RNA

ncRNA: 

non-coding RNA

NGS: 

Next-generation sequencing

PARE: 

Parallel analysis of RNA ends

PceRBase: 

Plant ceRNA database

phasiRNA: 

phased small interfering RNA

PlantCARE: 

a plant cis-acting regulatory element database

PlantNATsDB: 

Plant natural antisense transcripts database

PLncDB: 

Plant long non-coding RNA database

PmiRKB: 

Plant microRNA knowledge base

Pol II: 

RNA polymerase II

poly(A): 

polyadenylation

pri-miRNA: 

primary transcript of microRNA

psRNATarget: 

a plant small RNA target analysis server

RACE: 

rapid amplification of cDNA ends

RAP-DB: 

the rice annotation project database

RDR: 

RNA-dependent RNA polymerase

RGAP: 

Rice genome annotation project

RNA-seq: 

RNA sequencing

rRNA: 

ribosomal RNA

siRNA: 

Small interfering RNA

sRNA: 

Small RNA

sRNA-seq: 

sRNA sequencing

TAIR: 

the Arabidopsis information resource

TAPIR: 

Target prediction for plant microRNAs

ta-siRNA: 

trans-acting small interfering RNA

WT: 

Wild type

Declarations

Acknowledgements

The authors would like to apologize to those whose original works could not be cited in this paper. This work was funded by the National Natural Science Foundation of China [31571349] and [31601062], Zhejiang Provincial Natural Science Foundation of China [LQ16C060003] and [LY15C060006], and Hangzhou Scientific and Technological Program [20170432B04].

Authors’ contributions

YM conceived the idea and prepared the manuscript, DY contributed to drafting and revising the manuscript, XM, ZZ and WS helped in preparing the tables, figures and formatting of the manuscript, HW reviewed and provided comments on the manuscript. All of the authors read and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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.

Authors’ Affiliations

(1)
College of Life and Environmental Sciences, Hangzhou Normal University
(2)
Zhejiang Provincial Key Laboratory for Genetic Improvement and Quality Control of Medicinal Plants, Hangzhou Normal University

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