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 successfully sequenced.
• Unsurpassed data quality with a guaranteed Q30 score ≥ 90% that exceeds Illumina’s official guarantee.
• Comprehensive analysis using widely accepted mainstream software and mature in-house pipeline.
• Free, powerful Novofinder software that enables Novogene customers to easily access and visualize data analysis results and annotations through a user-friendly interface.
• Free correlation analysis of small RNA and mRNA expressions.

Project Workflow

Sequencing Strategy

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

Data Quality Guarantee

• Our data quality guarantee, as measured by the percentage of bases with a sequencing quality score above Q30 (SE50, ≥ 90%), exceeds Illumina’s official guarantee. See examples of our high quality data.

Sample Requirements

• Total RNA amount: ≥ 3.0 μg; RNA concentration: ≥ 50 ng/μl
• RIN value ≥ 7 for plants and fungi; RIN value ≥ 8.0 for animals
• RNA volume: 20 μl
• Purity: No degradation, no DNA contamination

Turnaround Time

• Within 35 working days from verification of sample quality (without data analysis)
• The data analysis turnaround is project-dependent.

Recommended Sequencing Depth

• ≥ 10 M reads

Analysis Pipeline

Here is an example analysis pipeline for animals with a reference genome. Novogene also provides correlation analysis for small RNA + mRNA projects at no additional cost (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. Figure. Hierarchical clustering of microRNAs that exhibit (left panel) nonadditive expression and (right panel) parental patterns of expression level dominance.