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Degradome Sequencing

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CD Genomics is now able to provide the degradome sequencing service to facilitate a more comprehensive insight into plant microRNA landscape. By using our service, you can detect the mRNA targets of the microRNA in a highly sensitive and accurate manner.

The Introduction of Degradome Sequencing

MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs of 20-24 nucleotides (nt) in length, produced by highly precise excision from stem-loop precursors. MicroRNAs are important regulators of gene expression at the transcriptional and post-transcriptional levels. The mature miRNA is recruited into an RNA-induced silencing complex (RISC) to degrade mRNA targets and suppress their translation. miRNAs are highly conserved among species based on comparisons among different species. There are also non-conserved and species-specific miRNAs in plants. Non-conserved miRNAs are often expressed at low levels, and, therefore, many are not identified in small-scale sequencing projects. Small RNA sequencing technology has allowed the identification of low-abundance miRNAs.

Modified 5’ RACE (rapid amplification of cDNA ends) was widely used for target confirmation and cleavage site mapping. Nevertheless, this approach is laborious, time-consuming, costly, and only applicable for fine-scale investigations. Recently, Degradome sequencing, also referred to as parallel analysis of RNA ends (PARE), has emerged as a powerful method that combines both high-throughput with modeified 5’ RACE to screen for miRNA and ta-siRNA (trans-acting siRNA) targets in a large scale. In this method, degraded capped mRNA is adapter-ligated and reverse-transcribed. Fragments are then Mmel-digested, purified, 3’-adapter-ligated, and PCR-amplified. Deep sequencing of the cDNA and degradome analysis provide comprehensive information about uncapped transcripts that undergo degradation in plants.

Advantages of Degradome Sequencing

  • Identification of known and novel miRNAs and ta-siRNA
  • Identification of miRNA targets and regulatory networks
  • Statistical summary of mRNA degradation sites
  • Explores novel biomarkers and circRNAs regulatory networks
  • High-throughput and high-resolution

Degradome Sequencing Workflow

The general workflow for degradome sequencing is outlined below. To construct degradome sequencing library, the first step is to ligate polyA-RNA samples to RNA adaptor containing a 3’Mme I site, and transcribed. After second-strand synthesis, Mme I digestion, gel purification, and PCR amplification, 3’ adaptor is ligated for deep sequencing. Our highly experienced expert team executes quality management, following every procedure to ensure confident and unbiased results.

Service Specifications

Sample requirements and preparation
  • Sample type: Total RNA without degradation or DNA contamination
  • Starting amount of total RNA ≥ 15 µg
  • Sample concentration ≥ 100 ng/µl
  • Sample purity: OD260/280 = 1.8~2.2
  • All RNA samples are validated for purity and quantity
Sequencing
  • Degradome library preparation
  • Illumina HiSeq SE50
  • More than 80% of bases with a ≥Q30 quality score
Bioinformatics Analysis
We provide customized bioinformatics analysis including:
  • Raw data quality control
  • Reference-based mapping
  • Identification of known and novel non-coding RNAs
  • Distribution analysis of degradation fragments on selected region of genome
  • Identification of target mRNAs
  • Statistical summary of mRNA degradation site
  • GO/KEGG analysis

Analysis pipeline

Supported by our experienced scientists and advanced technology, CD genomics can help you identify small RNA targets single-base resolution at one time through the high-throughput sequencing by strict quality control and advanced bioinformatics analyses. If you have additional requirements or questions, please feel free to contact us.

1. Principles of degradome sequencing.

In animals, protein repression is believed to occur by translational inhibition along with mRNA degradation. In plants, miRNAs degrade their mRNA targets by precise cleavage between the 10th and 11th nucleotide s from the 5’ end of the miRNA in the complementary region of the target transcript, generating a distinct peak of degradome sequence tag at the predicted cleavage site relative to other regions of the transcript. Based on this principal, the degradome sequencing is primarily used to globally identify remnants of small RNA-directed cleavage by sequencing the 5’ ends of uncapped RNAs in plants.

2. What are the advantages of degradome sequencing for identification of miRNA targets?

Table 1. The comparison of degradome sequencing and traditional methods for miRNA target prediction.

  Degradome sequencing Luciferase reporter gene assay Argonaute-RNA Immunoprecipitation (AGO-RIP)  5’ RACE (rapid amplification of cDNA ends)
Throughput High Low Low Low
operation Easy Complicated Complicated Easy
period Short Long Long Long
accuracy High High Relatively high Relatively high

The tools for identifying or predicting miRNA target.

Table 2. The tools for miRNA sequence-based prediction.

Type Name URL

Web-based

TargetScan http://www.targetscan.org/
Diana Tools http://diana.imis.athena-innovation.gr/DianaTools/index.php
miRanda http://www.microrna.org/microrna/getGeneForm.do
PITA http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html
PicTar http://pictar.mdc-berlin.de/
RNA22 https://cm.jefferson.edu/rna22/
RNAhybrid http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/
miRTar mirtar.mbc.nctu.edu.tw/
TargetS http://liubioinfolab.org/targetS/mirna.html
miRU http://plantgrn.noble.org/psRNATarget/
EIMMo http://www.mirz.unibas.ch/

Downloadable programs

miRPlant http://sourceforge.net/projects/mirplant/
MiRNA-EMBL http://www.russelllab.org/miRNAs/
miRspring http://mirspring.victorchang.edu.au/
miRNA Digger http://www.bioinfolab.cn/
miRanda http://www.microrna.org/microrna/getGeneForm.do
miRge http://atlas.pathology.jhu.edu/baras/miRge.html

R-packages

microRNA https://bioconductor.org/packages/release/bioc/html/microRNA.html
miRNApath https://bioconductor.org/packages/release/bioc/html/miRNApath.html
AgiMicroRna https://bioconductor.org/packages/release/bioc/html/AgiMicroRna.html
mirIntegrator https://bioconductor.org/packages/release/bioc/html/mirIntegrator.html
miRNAtap https://bioconductor.org/packages/release/bioc/html/miRNAtap.html
TargetScore https://bioconductor.org/packages/release/bioc/html/TargetScore.html
ExiMiR https://bioconductor.org/packages/release/bioc/html/ExiMiR.html
LVSmiRNA https://bioconductor.org/packages/release/bioc/html/LVSmiRNA.html
MiRaGE https://bioconductor.org/packages/release/bioc/html/MiRaGE.html
miRcomp https://bioconductor.org/packages/release/bioc/html/miRcomp.html
miRLAB https://bioconductor.org/packages/release/bioc/html/miRLAB.html
miRNApath https://bioconductor.org/packages/release/bioc/html/miRNApath.html
miRNAtap https://bioconductor.org/packages/release/bioc/html/miRNAtap.html
MmPalateMiRNA https://bioconductor.org/packages/release/bioc/html/MmPalateMiRNA.html
oneChannelGUI https://bioconductor.org/packages/release/bioc/html/oneChannelGUI.html
RmiR https://bioconductor.org/packages/release/bioc/html/RmiR.html
Roleswitch https://bioconductor.org/packages/release/bioc/html/Roleswitch.html
ssviz https://bioconductor.org/packages/release/bioc/html/ssviz.html

Reference:

Riffo-Campos, Á.L., et al. Tools for sequence-based miRNA target prediction: What to choose?. International journal of molecular sciences, 2016, 17(12): 1987.

Small RNA and degradome sequencing reveals important microRNA function in Astragalus chrysochlorus response to selenium stimuli

Journal: Plant biotechnology journal
Impact factor: 6.305
Published: 21 May 2015

Abstract

Astrgalus species are known as hyperaccumulator of Se by converting it to nonaminoacid compounds. But we have no idea about the Se-metabolism-related hyperaccumulation. The authors attempted to understand whether miRNAs play a role in Se accumulation in plants. In this study, they identified 418 known miRNAs and 151 novel miRNAs induced by Se exposure in Astragalus chrysochlorus. Through deep degradome sequencing the authors revealed important miRNA function in A. chrysochlorus response to selenium stimuli.

Materials & Methods

1. Samples

Callus tissues of A. chrysochlorus seeds;
Selenium treatment.

2. Sequencing

RNA isolation;
RNA quality and quantity measurements;
Small RNA sequencing;
Degradome sequencing.

3. Data analysis

miRNA identification;
Target prediction;
Function classification based on GO and KEGG analyses.

Results

1. miRNA identification and expression profiles.

A total of 418 known and 151 novel miRNAs was identified. The distribution of miRNAs between control and Se treatment is depicted in figure 1. The 418 miRNAs belong to 380 miRNA families, and 160 miRNA families were differently expressed in both control and Se-treated samples. 30 novel miRNAs were differently expressed after Se treatment.


Figure 1. Distribution of miRNAs between control and Se treatment: (c) conserved miRNAs; (d) novel miRNAs.


Figure 2. Small RNA expression profiles of control and Se-treated callus of A. chrysochlorus.


Figure 3. Expression profiles of randomly selected miRNAs with different abundance in Se-treated A. chrysochlorus calli.

2. Degradome sequencing analysis

A total of 1339 predicted sites were identified and determined to be cleaved by 499 miRNAs. The target genes were annotated and classified as transcription factors and their subunits, enzyme coding genes, resistance proteins, leucine-rich repeat, leucine zipper, zinc finger proteins, and other structural and functional proteins.


Figure 4. Target plots (t-plots) of miRNAs and their targets. The red arrows indicate the most abundant peaks or cleavage sites. (a) miR162-3p targeting endonuclease Dicer homologue-1-like protein; (b) miR1513a targeting blue light-activated histidine kinase; (c) miR2118b targeting hypoxanthine-guanine phosphoribosyltransferase-like protein; (d); miR172c targeting putative ethylene-responsive transcription factor RAP-2-7-like protein; (e) miR159b-3p targeting hypothetical protein 11M9.5; (f) miR166 h-3p targeting homeobox leucin zipper protein ATHB-15-like protein.

3. GO and KEGG pathway analyses

The targets of identified miRNAs were subjected to GO and KEGG analysis to perceive their biological roles. The target genes are involved in 47 types of cellular component, 103 types of molecular function and 144 types of biological process.

Figure 5. GO classifications of miRNA targets in A. chrysochlorus.

Figure 6. KEGG plant-pathogen interaction pathway and novel and known miRNAs which obtained in this study possibly targeting the genes involved in this pathway.

ReferenceCakir O, Candar-Cakir B, Zhang B. Small RNA and degradome sequencing reveals important microRNA function in Astragalus chrysochlorus response to selenium stimuli. Plant biotechnology journal, 2016, 14(2): 543-556.

Related Services
RNA-Seq

Transcriptome mapping and quantification

Small RNA Sequencing

Expression profiling of small RNAs

LncRNA Sequencing

Expression profiling of long non-coding RNAs

CircRNA Sequencing

Expression profiling of circular RNAs

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