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 SpecificationsApplications
- 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
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For Mendelian disorder/rare disease: effective sequencing depth above 50× (6G) | |||
For tumor sample: effective sequencing depth above 100× (12G) | ||||
Standard Data Analysis |
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Note: For detailed information, please refer to the Service Specifications and contact us for customized requests.
Project Workflow

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.
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.
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
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