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    Research Services

    Human Whole Exome Sequencing

    Overview

    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

    Applications

  • Genetic disease study
  • Cancer research
  • Human population evolution
  • Advantages

  • 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

    Mutational landscape of secondary glioblastoma guides MET-targeted trial in brain tumor

    Background:

    Grade IV gliomas, or glioblastoma (GBM), can be further classified into primary GBM (pGBM) and secondary GBM (sGBM). The limitations in current chemotherapy using temozolomide (TMZ), which functions by nonselective DNA damage, includes side effects and chemo-resistance. Under this therapy, almost all patients will recur, and the recurrent tumors usually carry distinct alterations that might lead to drug-resistance. To improve glioma treatment, it is essential to identify new oncogenic alterations and design therapies to specifically target them.

    Sampling & Sequencing Strategy:

    Sampling:
    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

    Background:

    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:

    Sampling:
    • 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

    Background:

    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.

    Sampling:

    • 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.

    Sequencing 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


    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


    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

    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