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mRNA Sequencing (mRNA-seq)


Messenger RNA (mRNA) is one type of the transcripts that carries information from DNA to ribosomes, instructing the protein synthesis (translation) in cells. Eukaryotic mRNA sequencing (mRNA-Seq), utilizing the technology of next-generation sequencing (NGS), reveals the expression profiles of mRNA in a biological sample and illustrates the continuous variations in the cellular transcriptome. Novogene’s mRNA-Seq, based on state-of-the-art Illumina NovaSeq platforms with paired-end 150 bp sequencing strategy, offers comprehensive solutions for analysis of gene expression quantification and differential gene expression among sample groups, as well as for identification of novel transcripts, alternative splicing, gene fusion events, and etc.. Our experienced bioinformaticians work closely with customers to provide standard and customized data analysis and publication-ready results for species with or without a reference genome.

With multiple research needs, please find more services of Prokaryotic RNAs, Non-coding RNAs, Full-length RNAs, Whole Transcriptome and Metatranscriptome for your study.

Service Specifications demo report


For medical research:

  • Pathological mechanism
  • Tumor-subtypes classification
  • Molecular markers
  • Human evolution
  • Drug target
  • Clinical diagnostics
  • Personal health care

For agricultural research:

  • Development
  • Adaptability
  • Agronomic traits
  • Crop evolution


  • Extensive experience with thousands of projects successfully completed and multiple articles published in journals of high Impact Factors.
  • Unsurpassed data quality with a guaranteed Q30 score ≥ 80% that exceeds Illumina’s official benchmarks.
  • Comprehensive data analysis using widely accepted industry standard software and mature in-house pipeline to detect differential expressions, to discover novel transcripts and to make functional annotations.
  • Easily visualize data analysis results with Novogene’s user-friendly in-house software.

Sample Requirements

Library Type Sample Type Amount RNA Integrity Number
(Agilent 2100)
Eukaryotic RNA-Seq
(cDNA library)
Total RNA ≥ 0.4 μg
≥ 6.8 (Animal), with smooth base line
≥ 6.3 (Plant and Fungus), with smooth base line
OD260/280 = 1.8-2.2;
OD260/230 ≥ 1.8;
Total RNA (Blood) ≥ 0.8 μg
Total RNA (Single Cell) ≥ 100 ng
Amplified cDNA (double-stranded) ≥ 100 ng Fragments between 400bp and 5000bp with main peak at ~2000bp OD260/280 = 1.8-2.0;
OD260/230 ≥ 1.8;
Eukaryotic RNA-Seq
(strand specific library)
Total RNA ≥ 0.8 μg ≥ 6.8 (Animal), with smooth base line
≥ 6.3 (Plant and Fungus), with smooth base line
OD260/280 = 1.8-2.2;
OD260/230 ≥ 1.8;

Sequencing Parameters and Analysis Contents

Sequencing Platform Illumina NovaSeq 6000
Read Length Paired-end 150
Recommended Data Output ≥ 20 million read pair per sample for species with reference genome;
≥ 50 million read pairs per sample for species without reference genome (de novo transcriptome assembly projects)
Standard Data Analysis
  • Data Quality Control
  • Transcriptome assembly & Gene functional annotation (only for species without reference genome)
  • Mapping to reference genome/assembled genome
  • Gene expression quantification & Differential expression profiling & Enrichment analysis
  • Protein-Protein Interaction (PPI) analysis
  • Transcription factors functional annotation analysis
  • Oncogene functional annotation analysis
  • SNP & InDel analysis
  • Alternative splicing analysis
  • Fusion gene prediction (Only for tumor sample and cancer cell line)
  • Note: For detailed information, please refer to the Service Specifications & Demo Reports and contact us for customized requests.

    Project Workflow

    Sampling & Sequencing Strategy:

    Sample Preparation
    • Tumor cells with/ without MYCN amplification

    Sequencing Strategy
    • Library preparation: RNA-seq library
    • Sequencing: Illumina HiSeq Platform


    Figure 1 Ectopic expression of ARMC12 represses the expression of PRC2 downstream tumor suppressive genes in NB cells.

    A Volcano plots (left panel), Venn diagram (middle panel), and heatmap (right panel) revealing the alteration of gene expression (fold change > 2.0, FDR < 0.05) in SH-SY5Y cells stably transfected with empty vector (mock) or ARMC12. Red indicates high expression, and blue indicates low expression in heatmap.


    ARMC12 plays a crucial role in tumor progression and could be a potential therapeutic approach for NB. Mechanistically, ARMC12 physically interacts with retinoblastoma binding protein 4 (RBBP4) to facilitate the formation and activity of polycomb repressive complex 2, resulting in transcriptional repression of tumor suppressive genes.

    Targeting epigenetic crosstalk as a therapeutic strategy for ezh2-aberrant solid tumors


    Mutations or aberrant upregulation of EZH2 occur frequently in human cancers, yet clinical benefits of EZH2 inhibitor (EZH2i) remain unsatisfactory and limited to certain hematological malignancies. Addressing how EZH2i modulates global epigenetic signatures and, more importantly, how the new insights can be translated into a better therapeutic strategy using EZH2is in a variety of solid tumors is quite meaningful.

    Sampling & Sequencing Strategy:

    Sample Preparation
    • U2932, SMMC-7721 and Pfeiffer cells

    Sequencing Strategy
    • Library Preparation: mRNA library, NEBNext UltraTM RNA Library Prep it for Illumina
    • Sequencing: Illumina platform


    Figure 2. Feedback H3K27 Acetylation Change Drives Oncogenic Transcriptional Reprogramming

    A) GSEA analysis of H3K27ac ChIP-seq data, RNA-seq data, and proteome data affected by EPZ-6438. The global heatmap showing the enriched pathways in the oncogenic signatures from the Molecular Signatures Database (MSigDB) with EPZ-6438 compared to DMSO treated in U2932, SMMC-7721, and Pfeiffer cell lines. The color is according to FDR q value, and the darkest blue represents q R 0.1 or N/A. (B and C) Venn diagram showing the overlap of the statistically (FDR q < 0.05) enriched pathways among the insensitive cell lines (U2932, SMMC-7721) based on RNA-seq data (B) and proteome data (C), respectively.


    Together, the epigenetic interplay revealed in this study enabled us to expand the therapeutic potential of EZH2is from hematological malignances to solid tumors. The insights reveal that EZH2i caused the crosstalk between H3K27me and H3K27ac and leads to oncogene activation. This may suggest that targeting this crosstalk could provide therapeutic promise.

    mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat


    Nascent allohexaploid wheat may represent the initial genetic state of common wheat (Triticumaestivum), which arose as a hybrid between Triticum turgidum (AABB) and Aegilops tauschii (DD) and by chromosome doubling and outcompeted its parents in growth vigor and adaptability. The molecular basis for this success remains unclear.

    Sampling & Sequencing Strategy:

    Sample Preparation
    • Tissues of Hexaploid Wheat, Chinese spring, Triticum Turgidum, Aegilops Tauschii

    Sequencing Strategy
    • Library preparation: mRNA-seq and sRNA-seq libraries
    • Sequencing: Illumina HiSeq 2000


    Figure 3. Nonadditively Expressed Genes in Young Spikes of Nascent Allohexaploid Wheat.

    (A) Genes differentially expressed in S3 progeny and their tetraploid (AABB) and diploid (DD) progenitors. Numbers close to the species (colored) represent upregulated genes compared with the neighboring species. Percentages indicate those among all expressed genes in young spikes. The total number of genes differentially expressed between two species is given (black).

    (B) GO enrichment analysis of nonadditively expressed genes. Shown are significantly enriched GO terms (Fisher test FDR < 0.05). BP, biological process; MF, molecular function; CC, cellular component.


    Allohexaploid wheat combines the AB genomes from tetraploid wheat with the D genome from Ae. tauschii, resulting in the union of genomes from varieties previously adapted to different environments and thus providing the potential for further adaptation to a wider range of growth environments. Overall, the molecular underpinnings established during the early allopolyploidization events laid the groundwork for the successful advent of common wheat.

    Error Rate Distribution

    error rate for Novogene RNA-seq

    The x-axis shows the base position along each sequencing read and the y-axis shows the base error rate.

    GC Content Distribution

    GC Content Distribution for Novogene RNA-seq

    Horizontal axis for reads position, vertical axis for single base percentage. Different color for different base type.

    Classification of Raw Reads

    Classification of Raw Reads for Novogene RNA-seq

    Reads Distribution on Reference Genome

    Reads Distribution on Reference Genome for Novogene RNA-seq

    Gene Expression Quantification

    Gene Expression Quantification for Novogene RNA-seq

    X axis represents the name of sample, Y axis indicates the log10(FPKM+1), parameters of box plots are indicated, including maximum, upper quartile, mid-value, lower quartile and minimum.

    Volcano Plot of changes on Gene Expression

     Volcano Plot of changes on Gene Expression for Novogene RNA-seq

    Horizontal axis for the fold change of genes in different samples. Vertical axis for statistically significant degree of changes in gene expression levels, the smaller the corrected pvalue, the bigger -log10(corrected pvalue), the more significant the difference. The point represents gene, blue dots indicate no significant difference in genes, red dots indicate upregulated differential expression genes, green dots indicate downregulated differential expression genes.

    Hierarchical Clustering Heatmap of Differential Expression

    Hierarchical Clustering Heatmap of Differential Expression for Novogene RNA-seq

    The overall results of FPKM cluster analysis, clustered using the log10(FPKM+1) value. Red denotes genes with high expression levels, and blue denotes genes with low expression levels. The color ranging from red to blue indicates log10(FPKM+1) value from large to small.


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