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Small RNA Sequencing (sRNA-seq)

Introduction to Small RNA Sequencing

Small RNAs (sRNAs) are short RNA molecules, usually non-coding, involved with gene silencing and the post-transcriptional regulation of gene expression. sRNA Sequencing (sRNA-seq) is a method that enables the in-depth investigation of these RNAs, in special microRNAs (miRNAs, 18-40nt in length).

Novogene sRNA-seq service is an effective approach to selectively target any species of sRNAs with unprecedented sensitivity and high resolution, all in a single analysis. Coupled with a robust in-house bioinformatics pipeline, Novogene sRNA-seq service has been assisting researchers to describe the differential expression of miRNAs, structural alterations, and to discover novel sRNAs.

Applications of small RNA sequencing

sRNA-seq has been supporting many compelling research avenues, including:

  • Profiling known and novel transcripts and expression quantifications of small RNA
  • Predicting targeting genes of miRNA candidates
  • Identifying tissue, stage, or cell type-specific biomarkers for cancer/disease diagnostics and classification
  • Discovering regulatory networks and understanding the mechanisms of tissue or organism development based on transcript profiles

Benefits of Novogene small RNA Sequencing

  • The Novogene sRNA-seq is a well-established service that relies on extensive experience, with numerous samples successfully sequenced. It allows access to expression profiles and transcript characterizations for multiple small RNA species, with a tried-and-tested workflow for both input sample of total RNA and exosomal RNA.
  • Novogene offers comprehensive analyses using top-notch software and a mature in-house pipeline, meeting all your bioinformatics needs. Researchers can obtain the regulation network, the deep regulation mechanism of gene expression, and more, with Novogene’s high quality services.

sRNA-seq Specifications: RNA Sample Requirements

Library Type Sample Type Amount RNA Integrity Number (Agilent 2100) Purity (NanoDrop)
Small RNA Library Total RNA ≥ 2 μg Animal ≥ 7.5, Plant ≥ 7, with smooth baseline OD260/280≥ 2.0; 0D260/230≥ 2.0;
no degradation, no contamination
Exosomal Small RNA Library Exosomal RNA ≥ 10 ng Fragments between
25-200nt, FU>10

Note: Sample amounts are listed for reference only. Download the Service Specifications or Sample Requirements to learn more. For detailed information, please contact us with your customized inquiry.

sRNA-seq Specifications: Sequencing and Analysis

Sequencing Platform Illumina NovaSeq 6000
Read Length Single-end 50 bp
Recommended Sequencing Depth ≥ 10 million read pair per sample
Standard Analysis (miRNA)
  • Data Quality Control
  • Summary of Length Distribution
  • Common & Specific Sequence Summary
  • Identification & Characterization of miRNAs
  • Classification & Annotation of miRNA
  • Quantification & Differential Expression Analysis
  • Functional Enrichment Analysis
  • Note: Sequencing depths and analysis contents displayed are for reference only. Download the Service Specifications to learn more. For detailed information, please contact us.

    Project Workflow of Novogene sRNA-seq Services

    The project workflow starts with sample quality control (Sample QC) to ensure that your samples meet the criteria of the sRNA-Seq technique. Then, the appropriate library is prepared according to your target organism and application, and subsequently tested for its quality (Library QC). Next, a single-end 50 bp sequencing strategy is used to sequence the samples and the resulting data is also checked for its quality (Data QC). Finally, bioinformatic analyses are performed and publication-ready results are provided.


    Novogene’s comprehensive sRNA-seq empowers researchers to investigate the differential expression and regulatory network of miRNAs, structural alterations, and to discover novel small RNAs. Here is a list of publications below that have used Novogene’s sRNA-seq services.

    sRNA Length Distribution

    sRNA Classification-Repeat Sequence


    miRNA-base bias

    TPM Boxplot

    TPM Density Distribution

    Hierarchical Cluster