Human whole genome sequencing is to determine the complete DNA sequence of human's genome at a single time, which generates large number of single nucleotide polymorphism loci (SNP), insertion/deletion (InDel), structure variations (SV), copy number variations (CNV). It provides much information for screening disease related genes to promote the development of medical technology.

Technical parameters

Project workflow

Library preparation

TruSeq Nano DNA library

Sequencing platform

HiSeq X

Sequencing strategy

PE150

Recommended sequencing depth
30~50×

Turnaround time
3 weeks

Bioinformatics analysis includes:

Standard bioinformatics analysis

  1. Data quality control: filtering out reads containing adapters or with low quality.

  2. Alignment with reference genome, depth and coverage statistics of sequencing.

  3. SNP/InDel/SV/CNV calling, annotation and statistics.

Advanced analysis

Tumor Monogenic disease
1. Somatic variations detection, annotation and statistics.
2. Variations annotation using database (such as COSMIC).
3. Prediction of harmful variations.
4. Screening of oncogene/suppressor genes/ susceptibility genes.
5. Analysis of tumor drive genes/ variations at high frequency analysis.
6. Function enrichment analysis (GO and KEGG).
7. Characteristics analysis ofvariations.
7.1 Variations rate, variations spectrum, distribution of variations (SNV/InDel).
7.2 SV breakpoints detection.
7.3 CNV feature analysis.
1. variations annotation and screening using disease databases such as omim.
2. Prediction of harmful variations.
3. Function enrichment analysis (GO and KEGG).
4. Annotation using non-coding regions database.
5.Screening of common variations in cases.
6. De novo analysis for family samples.
Complex disease Population genetics
1. Complex sample designs and power calculation.
2. SNP, statistics and annotation analysis for population.
3. Haplotype and linkage disequilibrium analysis.
4. Association analysis based on SNP.
5. Association analysis based on genes.
6. Function enrichment analysis (GO and KEGG).
7. Association analysis based on CNV.
1. SNP, statistics and annotation analysis for population.
2. Haplotype and linkage disequilibrium analysis.
3. Evolutionary analysis.
4. Feature analysis for population.