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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.

Applications

  • Genetic disease study
  • Cancer research
  • Human population evolution

Advantages

  • Unsurpassed data quality: We guarantee a Q30 score ≥ 80%, exceeding Illumina’s official guarantee of ≥ 75%.
  • Accurate variant calling with longer read length up to 150 bp.
  • Extraordinary informatics expertise: Novogene uses its cutting-edge bioinformatics pipeline and internationally recognized best-in-class software to provide customers with 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

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Note: For detailed information, please contact us.

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)

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Note: Sequencing depths and bioinformatic analysis(or advanced analysis for Cancer or Disease) requests can be customized based on the project needs. Please contact us for more information.

Project Workflow

Sample Quality Control

Library Quality Control

Data Quality Control

Sample Preparation

Exome Capture & Library Preparation

Sequencing

Bioinformatics Analysis

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:
• 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 3). 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 4). 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 1. Genomic alterations and mutational signatures in 49 Chinese primary HCCs that had tumor early.

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
itronic 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