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mRNA Sequencing

Overview

Messenger RNA (mRNA) is the RNA that carries information from DNA to the ribosome, the sites of protein synthesis (translation) in the cell. The coding sequence of the mRNA determines the amino acid sequence in the protein that is produced. The eukaryotic mRNA sequencing aims at the mRNA (protein-coding RNA) of all kinds of eukaryotes, short as mRNA-Seq.

mRNA-Seq uses next-generation sequencing (NGS) to reveal the presence and quantity of messenger RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome. Novogene’s mRNA-Seq, based on state-of-the-art Illumina NovaSeq platforms with paired-end 150 bp sequencing strategy, offers complete solutions for gene expression quantification and differential gene expression analysis among groups of samples, as well as for identification of novel transcripts, alternative splicing, and gene fusion events, etc. Our experienced bioinformaticians work closely with customers to provide standard and customized data analysis and publication ready results for species with and without a reference genome.

Service Specifications

Applications

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

Advantages

  • 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)
Purity
(NanoDrop)
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 and contact us for customized requests.

    Project Workflow

    Armadillo repeat containing 12 promotes neuroblastoma progression through interaction with retinoblastoma binding protein 4

    Background:

    Neuroblastoma (NB), one of the most common malignant solid tumors in pediatric population that arises from neural crest-derived cells, constitutes 15% of cancer related mortality in childhood. Poor clinical outcome in patients suffering from high risk NB. The mechanisms essential for the aggressiveness and progression of NB still warrant further investigation.

    Sampling & Sequencing Strategy:

    Sample Preparation
    • Tumor cells with/ without MYCN amplification

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

    Results:

    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.

    Conclusion:

    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

    Background:

    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

    Results:

    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.

    Conclusion:

    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

    Background:

    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

    Results:

    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.

    Conclusion:

    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

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

    GC Content Distribution

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

    Classification of raw reads


    Reads distribution on reference genome


    Quantification

    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

    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

    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.