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Small RNA-Seq

Service Overview
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Small RNA Sequencing Small RNAs are a type of non-coding RNA of 18-40 nucleotides in length that regulate the translation of target mRNAs. Small RNA sequencing, including the sequencing of microRNA (miRNA-Seq), is a valuable method for investigating the function of small RNAs and for constructing regulatory networks of miRNAs and their target genes. Novogene offers comprehensive small RNA sequencing and bioinformatics analysis for miRNA and other types of small RNAs, including siRNA and piRNA. Scientists at Novogene use the cutting-edge Illumina platform, with the SE50 sequencing strategy and widely accepted analysis software, to support RNA research on a wide range of biological systems. Please contact us for more information.

The Novogene Advantage

  • Extensive experience with thousands of samples being successfully sequenced.
  • Unsurpassed data quality with a guaranteed Q30 score ≥ 85% that exceeds Illumina’s official benchmarks.
  • Comprehensive analysis using mainstream software and mature in-house pipeline to meet multiple bioinformatic requests.
  • Free correlation analysis for both small RNA and mRNA expression levels to investigate the regulatory networks.

Project Workflow

small RNA sequencing project workflow

Sample Requirements

LIBRARY TYPE SAMPLE TYPE AMOUNT (QUBIT®) VOLUME CONCENTRATION RNA INTEGRITY NUMBER (AGILENT 2100) PURITY (NANODROP™/AGAROSE GEL)
Small RNA-Seq Total RNA ≥ 2 μg ≥ 20 μl ≥ 20 ng/μl Animal ≥ 7.5, Plant/Fungi ≥ 7, with the smooth baseline No degradation, no contamination

Sequencing Strategy

  • 18-40 bp insert cDNA library
  • NovaSeq platform, single-end 50 bp

Recommended Sequencing Depth

  • ≥ 10 M reads

Turnaround Time

  • Within 5 weeks from project verification to data releasing without bioinformatic analysis
  • The turnaround for data analysis is project-dependent

Analysis Pipeline

small RNA sequencing analysis pipeline
  • Figure 1: Analysis Workflow of Small RNA Sequencing for animals with reference
small RNA sequencing analysis pipeline
  • Figure 2: Analysis Workflow of Small RNA Sequencing for plants with reference
Table 1: Analysis Contents for association analysis of both small RNA and mRNA (Only for Sample ≥4)
Standard Analysis for correlation analysis between mRNA and miRNA
Cluster analysis of common genes between differentially expressed mRNAs and differentially expressed miRNAs' target mRNAs.
GO enrichment analysis of common genes between differentially expressed mRNAs and differentially expressed miRNAs' target mRNAs.
KEGG enrichment analysis of common genes between differentially expressed mRNAs and differentially expressed miRNAs' target mRNAs.
Interaction analysis between differentially expressed mRNAs and differentially expressed miRNAs.
Table. Representative data quality results of small RNA sequencing from Novogene.
Sample Reads Bases Error Rate (%) Q 20 (%) Q 30 (%) GC Content (%)
A1 10146979 0.51G 0.01 98.60 96.94 48.90
A2 10262275 0.51G 0.01 98.52 96.83 48.96
A3 11310590 0.57G 0.01 98.53 96.83 48.98
B1 10852108 0.54G 0.01 98.38 96.77 49.07
B2 14936611 0.75G 0.01 98.43 96.82 48.96
B3 12674317 0.63G 0.01 98.51 96.97 49.00

Project Example

The following study utilized Novogene's expert small RNA sequencing services. mRNA and small RNA transcriptomes reveal insights into dynamic homoeolog regulation of allopolyploid heterosis in nascent hexaploid wheat The Plant Cell, 26:1878-1900 (2014) As published online in The Plant Cell, Novogene, in cooperation with the Chinese Academy of Agriculture Sciences, reported that mRNA and small RNA transcriptomes provide insights into small RNA–mediated dynamic homoeolog regulation mechanisms that may contribute to heterosis in nascent hexaploid wheat. Expression level dominance, in which genes exhibit the expression levels of a dominant subgenome, was observed for a large number of genes associated with development and adaptation. Results also suggested that microRNAs and small interfering RNAs contribute to the differential gene expression patterns observed in hybrid progeny. The findings advanced our understanding of the regulation of key agronomic traits in a major food crop. small rna data graph Figure. Hierarchical clustering of microRNAs that exhibit (left panel) nonadditive expression and (right panel) parental patterns of expression level dominance.