Whole Exome Sequencing

Figure 1. An example of a gene structure

Service Introduction

The exome constitutes only 1–2 % of the human genome yet harbors approximately 85 % of known pathogenic variants. Whole-exome sequencing (WES) is a high-throughput strategy that uses probe-based hybridization capture to enrich for exonic DNA across the entire genome, followed by deep sequencing and analysis to obtain variant information within the protein-coding regions. Compared with whole-genome sequencing (WGS), WES offers higher sequencing depth (≥100×), lower cost, and superior detection of low-frequency variants. Consequently, WES is widely employed in studies of germline mutations, cancer genomics (e.g., tumor neoantigens), and other related fields.

Figure 2. Workflow of WES

Workflow

Advantages

1. Utilization of high-efficiency exome probes and an optimized hybrid-capture system ensures superior capture efficiency and exceptional uniformity. 2. Deep sequencing delivers higher coverage at a cost markedly lower than that of WGS. 3. Accurate detection of diverse variant types, including single-nucleotide variants (SNVs), copy-number variants (CNVs), and insertions/deletions (InDels).

Application

1. Genetic Disease Diagnosis: Detection of gene mutations associated with rare or inherited diseases to enable precise diagnosis. 2. Cancer research: Identification of cancer-driving gene mutations, including studies on tumor somatic mutations, neoantigen detection, and hereditary predisposition genes. 3. Personalized Medicine: Analysis of individual genetic variations to provide insights to guide drug selection and treatment strategies.

For detailed sample submission guidelines, required sample volumes, and pre-treatment protocols, please contact us directly.

Sample Requirements

Bioinformatic Analysis

Basic Analysis

1. Raw reads trimming and quality control 2. Alignment to the reference genome 3. Computing sequencing depth and coverage 4. SNPs/InDels/CNVs detection and annotation 5. PPI protein interaction network 6. GO/KEGG enrichment analysis

Advanced Analysis

1. Screening of harmful mutation sites 2. Filter known database sites 3. Variant site annotation in coding or splice site regions 4. Conservation prediction of amino acids 5. Analysis of harmfulness of mutation sites 6. Structural variation harmfulness analysis 7. HLA typing analysis 8. Neoantigen prediction

Figure 3. Actual performance of gDNA samples. Compared with other brands, the capture efficiency is higher and the uniformity is better

Figure 4. Left: Homogeneity index reached 1.24; right: The capture efficiency was higher than other brands, reaching 93.7%

Figure 5. Performance metrics of the service using the kit across different sequencing platforms. Sequencing was performed on NovaSeq6000 and DNBSEQ-T7 (30 μL, 15 μL hybridization volume)

Figure 6. Data performance of the EPI-WES hybridization system in different Panel sizes. Libraries obtained with 750 ng input were captured using Illumina PE150 with different Panel sizes.

Figure 7. Analysis of TACC2 copy number variation

1. Mol Cancer：Loss of TACC2 impairs chemokine CCL3 and CCL4 expression and reduces response to anti-PD-1 therapy in soft tissue sarcom (1)

This study establishes TACC2 as a tumor suppressor in soft-tissue sarcoma (STS) that augments antitumor immunity by modulating the expression of the chemokines CCL3 and CCL4. High TACC2 expression is associated with enhanced immune-cell infiltration and improved response to anti-PD-1 therapy. Whole-exome sequencing (WES) was employed to identify TACC2-related genomic alterations—particularly copy-number variations (CNVs)—in STS patient samples and to characterize TACC2 mutational profiles across distinct STS subtypes.

Figure 8. Genetic characteristics and related clinical and pathological characteristics of EEC patients in 229 TJFD studies

2. Nat Genet：Proteogenomic landscape of early-onset endometrial cancer reveals molecular features and predictive biomarkers for fertility-sparing therapy (2)

Using an integrated multi-omics approach, this study delineates the molecular landscape of early-onset endometrioid endometrial cancer (EEEC), with a particular focus on predicting fertility-sparing progestin response in patients under 40 years of age. The investigators uncovered exposure-related mutational signatures specific to EEEC and identified biomarkers capable of forecasting progestin sensitivity. Whole-exome sequencing (WES) was performed on 215 EEEC tumors to catalogue the mutational differences between EEEC and late-onset endometrioid endometrial cancer (LEEC). Samples were then stratified according to The Cancer Genome Atlas (TCGA) schema into POLE-mutant, microsatellite-instable (MSI), copy-number low (CNL), and copy-number high (CNH) molecular subtypes. Finally, mutational signatures linked to chronological age and environmental exposures were systematically characterized.

References

[1]Yang J, Lu X, Cai Q, et al. Loss of TACC2 impairs chemokine CCL3 and CCL4 expression and reduces response to anti-PD-1 therapy in soft tissue sarcoma. Mol Cancer. 2025 May 30;24(1):158. [2]Hu, Z., Wu, Z., Liu, W. et al. Proteogenomic insights into early-onset endometrioid endometrial carcinoma: predictors for fertility-sparing therapy response. Nat Genet 56, 637–651 (2024).