Target Region Sequencing (TRS)

What is Target Region Sequencing?

Target Region Sequencing (TRS) focuses on a subset of genes or specific regions of the genome, which are most likely to be associated with a disease or phenotype-related studies. It is used for analyzing mutations in a given sample. Compared to Whole Genome Sequencing and Whole Exome Sequencing, target region sequencing generates more comprehensive and manageable data and is more cost-effective in investigating regions of interest, achieving delivery of much higher coverage and identification of rare variants.

Novogene’s target region sequencing provides comprehensive services including customized panel design, library preparation, sequencing, and bioinformatics analyses based on your research purpose.

Applications of Target Region Sequencing

Cancer research
Human population studies
Linkage analysis for inherited diseases
Detection and quantification of low-frequency alleles and rare variants
Detection of somatic and germline mutations
Discovery of biomarkers and therapeutic targets

Benefits of Target Region Sequencing

Agilent SureSelectXT Custom Kit and NovaSeq 6000 platforms are available for TRS service.
Data quality with a guarantee of Q30≥85%.
Compact and manageable data set for bioinformatics analyses.
Much more economical with a capacity of processing large number of samples.
Ready-for-publication data and figures bring the simple insights for the result overview based on Novogene cutting-edge bioinformatics pipeline and database.

TRS Specifications: Sample Requirements

Sample Type	Amount (Qubit®)	Purity
Genomic DNA	≥ 400 ng	A260/280=1.8-2.0 no degradation, no contamination
Genomic DNA from FFPE	≥ 800 ng	Fragments should be longer than 1000 bp
cfDNA / ctDNA	≥ 50 ng	Fragments should be in multiples of 170bp, with no genomic contamination

TRS Specifications: Sequencing and Analysis

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

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

Novogene Workflow of TRS Service

Featured Publications of TRS Sequencing

The Spectrum of mitochondrial genomic variation in parathyroid neoplasms

Endocrine Date: July 2021IF: 21.674DOI: 10.1007/s12020-021-02825-8
- Reference information
  
  Hu, Y., Zhang, X., Wang, O., Xing, X. P., Cui, M., Wang, M. Y., … & Liao, Q. (2021). Spectrum of mitochondrial genomic variation in parathyroid neoplasms. Endocrine, Online ahead of print.
Genomic sequencing identifies WNK2 as a driver inhepatocellular carcinoma and a risk factor for early recurrence

Journal of HepatologyIDecember Date: 2019IF: 20.582DOI: http://dx.doi.org/10.1016/j.jhep.2019.07.014
- Reference information
  
  Zhou, S. L., Zhou, Z. J., Hu, Z. Q., Song, C. L., Luo, Y. J., Luo, C. B., … & Huang, X. W. (2019). Genomic sequencing identifies WNK2 as a driver in hepatocellular carcinoma and a risk factor for early recurrence. Journal of hepatology, 71(6),1152-1163.
Genetic Alterations in Esophageal Tissues From Squamous Dysplasia to Carcinoma

Gastroenterology Date: July 2017IF: 18.392DOI: 10.1053/j.gastro.2017.03.033
- Reference information
  
  Liu, X., Zhang, M., Ying, S. M., Zhang, C., Lin, R. H., Zheng, J. X., … Su, M. (2017). Genetic Alterations in Esophageal Tissues From Squamous Dysplasia to Carcinoma. Gastroenterolo, 153(1):166-177.

Data Quality Control

Sequencing Error Rate Distribution

The sequencing error rate is the major confounding factor of precise detection of low-frequency variations by deep sequencing. It determines the quality of the sequencing data. The sequencing error rate is highly associated with the sequencing cycle, escalating towards the end of each read because of the consumption of chemical reagents, which is a common feature of the Illumina high throughput sequencing platform.

Novogene hWES Error Rate Distribution

Note:
The x-axis represents the position in reads, and the y-axis indicates the average error rate of bases of all reads at a position.

GC Content Distribution

GC content distribution aims to check the potential of AT/GC separation. Sample contamination, sequencing bias, and errors during library preparation can impact on the sequencing results.

Novogene hWGS GC Content Distribution

Note:
The x-axis represents the position in reads, and the y-axis indicates the percentage of each type of bases (A, T, G, C); different bases can be distinguished by different colors.

SNP and InDel calling, annotation and statistics

Single nucleotide polymorphisms (SNPs), also known as single nucleotide variants (SNVs), constitute the largest class of genetic variants in the genome. Another class of genetic variations includes small insertions and deletions (InDels) which are <50 bp in length.

Novogene hWES SNP Detection

The number of SNPs/InDels in various genomic regions

Novogene hWES SNP Detection
The number of different types of SNPs/InDels in coding region