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Human Whole Exome Sequencing


Exome sequencing provides a cost-effective alternative to whole genome sequencing, as it targets only the protein coding region of the human genome responsible for a majority of known disease-related variants. Whether you are conducting studies in rare mendelian disorders, complex disease, cancer research, or human population studies, Novogene’s comprehensive human whole exome sequencing (hWES) service provides a high-quality, affordable, and convenient solution.

Service Specifications


  • Genetic disease study
  • Cancer research
  • Human population evolution


  • State-of-the-art NGS technologies: Novogene is a world leader in sequencing capacity using state-of-the-art technology, including Illumina HiSeq and NovaSeq 6000 Systems.
  • Highest data quality: We guarantee a Q30 score ≥ 80%, exceeding Illumina’s official guarantee of ≥ 75%. See our data example.
  • Extraordinary informatics expertise: Novogene uses its cutting-edge bioinformatics pipeline and internationally recognised, best-in-class software to provide customers with highly reliable, publication-ready data.

Sample Requirements

Sample Type Amount (Qubit®) Purity
Genomic DNA ≥ 400 ng OD260/280=1.8-2.0
MDA product/Single Cell Amplified DNA ≥ 1 μg
Genomic DNA from FFPE ≥ 0.8 μg

Sequencing Parameters And Analysis Contents

Platform Type Illumina Novaseq 6000
Read Length Paired-end 150 bp
Recommended Sequencing Depth
For Mendelian disorder/rare disease: effective sequencing depth above 50× (6G)
For tumor sample: effective sequencing depth above 100× (12G)
Standard Data Analysis
  • Data quality control
  • Alignment with reference genome
  • SNP and InDel detection
  • Somatic SNP/InDel/CNV detection (paired tumor samples)
  • Note: For detailed information, please refer to the Service Specifications and contact us for customized requests.

    Project Workflow

    Sampling & Sequencing Strategy:

    108 newly collected sGBM patient samples from AGGA
    80 published datasets

    Sequencing Strategy:
    Human whole exome sequencing, targeted region sequencing, and mRNA sequencing on Illumina HiSeq platform

    Results & Conclusion:

    By studying the mutational landscape (Figure 1) of 188 sGBMs, this study shows significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. In addition, this study demonstrated the safety and efficacy of PLB-1001 (a MET-specific inhibitor) in patient treatment. Taken together, this paper described a comprehensive somatic mutation landscape of sGBM and provided a MET-targeted therapy for precision neuro-oncology.


    Figure 1. Mutational landscape of secondary glioblastoma

    Genomic sequencing identifies WNK2 as a driver in hepatocellular carcinoma and a risk factor for early recurrence


    Hepatocellular carcinoma (HCC) is a relatively common type of cancer with rising incidence and mortality rates. Although advances in the treatment and management of patients with HCC have improved survival rates, HCC still has a high rate of early recurrence. This study aimed to systematically define genomic alterations in Chinese patients with HCC and to identify mutations associated with early tumor recurrence in those patients.

    Sampling & Sequencing Strategy:

    • 182 Chinese primary HCC samples

    Sequencing Strategy:
    • Human whole genome sequencing (49 cases), whole exome sequencing (18 cases), and targeted region sequencing (115 cases) on Illumina platforms (PE150)

    Results & Conclusion:

    By using WGS, this study described the genomic landscape, including somatic SNVs/InDels, CNVs and SVs, and identified five prominent mutational signatures in 49 Chinese patients with HCC (Figure 2). Through WGS, WES, and targeted sequencing of 182 primary HCC samples, the results suggest that WNK2, RUNX1T1, CTNNB1, TSC1, and TP53 may play roles in HCC invasion and metastasis, and that WNK2 had the most significant difference in mutation frequency (Figure 3). Biofunctional investigations revealed a tumor-suppressor role for WNK2; its inactivation led to ERK1/2 signaling activation in HCC cells, tumor-associated macrophage infiltration, and tumor growth and metastasis. This study describes the genomic events that characterize Chinese HCCs and identify WNK2 as a driver of HCC that was associated with early tumor recurrence after curative resection.

    Figure 2. Genomic alterations and mutational signatures in 49 Chinese primary HCCs that had tumor early recurrence.

    Figure 3. The mutational spectrum in HCCs with or without early recurrence.

    Whole-exome sequencing reveals the origin and evolution of hepato-cholangiocarcinoma


    Hepatocellular-cholangiocarcinoma (H-ChC) is a rare subtype of liver cancer with clinicopathological features of both hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Currently, the cellular origins of HCC and iCCA in H-ChC (viz. whether HCC and iCCA differentiate from the same cell origin or from distinct clones) and the underlying mechanisms remain largely unknown.


    • 75 patients (15 with H-ChC, 32 with HCC, and 28 with iCCA)
    • 21 samples (HCC, iCCA, and adjacent noncancerous tissues) from seven H-ChC patients

    Sequencing Strategy:

    • Human whole exome sequencing on Illumina platform (PE150)

    Results & Conclusion:

    Whole exome sequencing analysis suggest a monoclonal origin (Figure 4) of H-ChC, which may promote the molecular classification of primary liver cancer on the basis of cell origin. In addition, the substantial intratumor heterogeneity (Figure 5) noted in H-ChC suggests that further multiregional sequencing analysis is necessary to find the common driver mutations that play an important role in carcinogenesis. This knowledge can be used to improve the precision and effectiveness of target drug selection.

    Figure 4. Mutation spectra, mutation signatures, CNVs, and SMGs among H-ChC samples.

    Figure 5. Distribution of nonsynonymous SNVs between H-ChC component (red circle) and iCCA component (green circle) in every H-ChC patient.

    Error Rate Distribution

    Novogene hWES Error Rate Distribution
    Note: The x-axis represents position in reads, and the y-axis represents the average error rate of bases of all reads at a position.

    GC Content Distribution

    Novogene hWGS GC Content Distribution
    Note: The x-axis is position in reads, and the y-axis is percentage of each type of bases (A, T, G, C); different bases are distinguishable by different colors.

    Sequencing Depth & Coverage Distribution

    Novogene hWES Sequencing Depth & Coverage Distribution
    Note: Average sequencing depth (bar plot) and coverage (dot-line plot) in each chromosome. The x-axis represents chromosome; the left y-axis is the average depth; the right y-axis is the coverage (proportion of covered bases).

    SNP Detection

    Novogene hWES SNP Detection

    Sample Sample_1 Sample_2 Sample_3 Sample_4 Sample_5
    CDS 22948 22726 22681 22679 22496
    Synonymous SNP 11491 11441 11416 11408 11532
    missense SNP 10697 10657 10628 10639 10359
    stopgain 91 87 87 87 79
    stoploss 12 12 12 13 15
    unknown 564 535 544 536 520
    intronic 130230 128685 129046 132820 182248
    UTR3 6431 6217 6301 6413 7612
    UTR5 3177 3150 3163 3234 3730
    splicing 81 81 81 81 76
    ncRNA exonic 3328 3289 3312 3343 4037
    ncRNA intronic 11066 10967 10946 11426 17658
    ncRNA splicing 8 10 13 13 13
    upstream 4488 4204 4270 4458 6344
    downstream 2392 2352 2436 2406 3501
    intergenic 66631 64399 64589 68470 137307
    Total 250922 246335 247081 255588 385335

    Heatmap of significantly mutated genes

    Novogene hWES Heatmap of Significantly Mutated Genes