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Research Services

Target Region Sequencing

Overview

Targeted Region Sequencing focuses on a subset of genes or specific regions of the genome, which are most likely to be involved in the phenotype under study. Targeted sequencing is a cost-effective method for investigating areas of interest, and can also deliver much higher coverage levels, allowing identification of rare variants. Novogenes’s human targeted region sequencing (hTRS) provides comprehensive services for your project with custom panel design based on regions or genes of your interest, capture of target regions, sequencing and bioinformatic analysis.

Service Specifications Demo Reports

Applications

  • Cancer research
  • Human population studies
  • Linkage analysis for inherited diseases
  • Discovery of biomarkers and therapeutic targets

Advantages

  • Unsurpassed data quality: We guarantee a Q30 score ≥80%, exceeding Illumina’s official guarantee of ≥75%. See our data example.
  • 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
cfDNA / ctDNA ≥50 ng Fragments should be in multiples of 170bp, with no genomic contamination

Sequencing Parameters And Analysis Contents

Platform Type Illumina Novaseq 6000
Read Length Paired-end 150 bp
Recommended sequencing depth Above 200× (Average effective sequencing depth)
Standard analysis Data quality control
Alignment with reference genome, statistics of sequencing depth and coverage
SNP and InDel calling, annotation and statistics
Somatic SNP/InDel/CNV calling, annotation and statistics (paired tumor samples)

Note: For detailed information, please refer to the Service Specifications & Demo Reports and contact us for customized requests.

Project Workflow

Sample Quality Control

Library Quality Control

Data Quality Control

Sample Preparation

Exome Capture & Library Preparation

Sequencing

Bioinformatics Analysis

Genomic sequencing identifies WNK2 as a driver in hepatocellular carcinoma and a risk factor for early recurrence (Zhou et al., 2019)

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:

• 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 1). 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 2). 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 recurrence.

Figure 2. The mutational spectrum in HCCs with or without early recurrence.

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