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Degradome Sequencing

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CD Genomics is now able to provide 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.

Degradome Sequencing

microRNAs (miRNAs) are a class of endogenous 20-24 nt non-coding RNAs produced by highly precise excision from stem-loop precursors, which are important regulators of gene expression at the transcriptional and post-transcriptional levels. The mature miRNA is recruited into a RNA-induced silencing complex (RISC) which degrades mRNA targets and suppresses their translation. The complementarity between a miRNA and its targeted mRNA determines whether miRNAs modulate gene expression by targeting mRNAs for cleavage. Plant miRNAs have been largely implicated in degradation of their RNA targets by slicing precisely between the 10th and 11th nucleotides (nt) from the 5’ end of miRNAs.

Next-generation sequencing and bioinformatics prediction provide effective methods for plant miRNA discovery and analysis. Degradome sequencing (Degradome-Seq), also referred to as parallel analysis of RNA ends sequencing (PARE-seq), is a modified version of 5'-Rapid Amplification of cDNA Ends (RACE) by utilizing advanced NGS technology. Degradome sequencing is an efficient tool to identify miRNA-guided cleaved sites in mRNAs, which allows directly identification of their prospective miRNA targets on 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 provides information about uncapped transcripts that undergo degradation. The workflow of our Degradome sequencing process is demonstrated in Figure 1.

Figure 1. Schematic workflow of small RNA sequencing process. Figure 1. Schematic workflow of small RNA sequencing process.

Sequencing Strategy and Recommended Depth

  • Illumina HiSeq SE50
  • 5-10 M reads

Data analysis

We have a team with the expert knowledge and computational resources to help you achieve your data analysis objectives. Our bioinformatics analysis pipeline includes mapping to reference, identification of rRNAs, tRNAs, snRNAs, snoRNAs, polyN and other non-coding RNAs, distribution analysis of degradation fragments on selected region of genome, identification of target mRNAs, statistical summarization of mRNA degradation sites, GO/KEGG analysis, and identification of degradation mRNA related microRNAs from miRBase.

Sample Requirements

  1. Sample type: Total RNA without degradation or DNA contamination.
  2. Starting amount of total RNA: ≥ 15 µg
  3. Sample conc.: ≥ 100 ng/µl
  4. Sample purity: OD260/280 = 1.8~2.2

Key Features and Advantages

  • Novel library preparation technology. High efficiency and fast turnaround time.
  • High throughput. Simultaneously sequences and analyses all the miRNA-cleaved fragments.
  • High Accuracy. Eliminate the significant false-positive hits in miRNA target gene by using advanced bioinformatics approaches.
  • Comprehensive data analysis. Identification of microRNA and targeted mRNA and their distributions.

By harness of Illumina next-generation sequencing technology and experienced technicians, our advanced Degradome-Seq is able to effectively identify miRNA cleavage sites from the degradome and accurately infer target genes of miRNA by leveraging the analytic power NGS data bioinformatics.

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


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.


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.

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