Digital RNA Sequencing

Digital RNA-Seq (or UMI-RNA-Seq) is a next-generation sequencing (NGS)-based method that eliminates sequence-dependent PCR bias by barcoding RNA molecules prior to amplification. Digital RNA-Seq can not only realize the transcriptomic sequencing as conventional RNA-Seq, but also reduce the biases and errors in sequencing.


In a digital RNA sequencing assay, a unique molecular identifier (UMI) or as so-called unique identifier (UID) is added to each cDNA before library amplification. The UMI accompany the whole process of amplification, sequencing, and analysis, which contributes to the traceability during the whole process. By combining fragments from the same source (with the same sequence and UMI), the PCR amplification repeats can be accurately removed and the original state of the sample before amplification can be accurately restored. In this process, errors in PCR amplification and sequencing can also be corrected by comparing the similarities of the sequences tagged with the same UMI.

Although Conventional RNA-Seq can reflect the expression level to a certain extent, deviations or even bias between the sequencing result and the true value still exist. According to the quantitative analysis based on UMI, Digital RNA-Seq can accurately distinguish the copies generated by amplification or from the original sample, which guarantees a result close to the true value. In addition, the promising error correction function of Digital RNA-Seq significantly reduces the errors induced by amplification and sequencing procedure, allowing for more precise sequencing and deeper transcriptomics research.


High Sensitivity High Accuracy Bioinformatics Analysis One-stop Service
Detection of low-copy number RNA and single-copy resolution Low bias during amplification and reducing errors in sequencing Our integrated bioinformatics pipeline can be tailored to suit your project. Provides one-stop service for library construction, sequencing, sample QC and data analysis.

Project Workflow

Sample Preparation

1. Sample Preparation

RNA purification;
quality assessment and quantification.

Library Preparation

2. Library Preparation

RNA fragmentation;
cDNA library preparation.


3. Sequencing

Illumina HiSeq;
PE 50/75/100/150.

Data Analysis

4. Data Analysis

Visualize and preprocess results, and perform custom bioinformatics analysis.

Digital RNA Sequencing

Bioinformatics Analysis Pipeline

In-depth data analysis:

  • Gene fusion detection
  • Differential expression analysis
  • Pathway analysis
  • SNV & InDel detection
  • Splicing analysis
  • Prediction of novel miRNAs, lncRNAs and circRNAs
  • Discovery of novel transcripts and mutations, even in FFPE tissue

Sample Requirements

Cell sample ≥ 50,000 cells;
RNA sample (concentration ≥ 50 ng/µL, quantity ≥ 1 µg)
1.8 ≤ OD260/280 ≤ 2.2, OD260/230 ≥ 2.0, RIN ≥ 6.5, 28S:18S ≥ 1.0.
Please make sure that the RNA is not degraded nor contaminated.

Sample storage: RNA can be dissolved in ethanol or RNA-free ultra-pure water and stored at -80°C. RNA should avoid repeated freezing and thawing.

Shipping Method: When shipping RNA samples, the RNA sample is stored in a 1.5 mL Eppendorf tube, sealed with sealing film. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.

Deliverable: FastQ, BAM, coverage summary, QC report, full statistical analysis & alignments, custom analysis reports on customer request.


  1. Melsted P, Ntranos V, Pachter L. The barcode, UMI, set format and BUStools. Bioinformatics. 2019;35(21):4472-3.
  2. Shiroguchi K, Jia TZ, Sims PA, Xie XS. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proceedings of the National Academy of Sciences. 2012;109(4):1347-52.
* For Research Use Only. Not for use in diagnostic procedures.


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