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 SpecificationsApplications
- Cancer research
- Human population studies
- Linkage analysis for inherited diseases
- Discovery of biomarkers and therapeutic targets
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 recognized, 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 | |
| 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) |
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Project Workflow
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.
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
