运用NGS进行正常细胞的体细胞突变检测

5. Pitfalls in the Detection of Mosaic Variants
False Positive Mosaic Calls Can Arise from Multiple Sources
5. Pitfalls in the Detection of Mosaic Variants
4. Increasing Accuracy by Haplotype Phasing
single-cell WGS data phasing ① To infer the genome-wide mutation rate ② To characterize the sequence features of the mutational processes
3. Mosaic Mutation Calling
Approaches for Filtering Germline Variants
A. Paired tissue strategy B. Panel of normals strategy ①public variation databases ②‘panel of normals’
2. Strategies for Profiling Mosaic Variants
Detectable variants and the range of detectable VAFs ①Whole-genome sequencing (WGS) ②whole-exome sequencing (WES) ③customized (targeted) panel
1.Somatic Mosaicism and Challenges in Detecting Mosaic Variants
Genetic mutation and recombination
① ② ③ ④ Single-nucleotide variants (SNVs) copy-number variants (CNVs) transposable element (TE) insertions structural variants
4. Increasing Accuracy by Haplotype Phasing
Applications ①correlating genetic variation with disease ②detecting genotyping error ③ inferring evolutionary history ④ examining the effect of cis-regulatory elements on gene expression
5. Pitfalls in the Detection of Mosaic Variants
(a). DNA Contamination ①during sample handling and sequencing ②human subjects is perhaps the most dangerous (b). DNA Damage ①UV radiation ：pyrimidine dimers ②high temperature：C > T transitions ③reactive oxygen species ：oxodG mis-pair with A ④ionizing radiation：double stranded DNA breaks
2. Strategies for Profiling Mosaic Variants
the Single-Cell Level—somatic mutations discovered in single cells Produce sufficient input DNA ① clonal expansion ② whole genome amplification
Detecting Somatic Mutations in Normal Cells
正常细胞的体细胞突变检测 研发部 2018年8月17日
Background
Vterozygous : 0.5 ; homozygous : 1
blue, 50% of cells; red,25%; yellow, 12.5%
3. Mosaic Mutation Calling
Detection of Mosaic Structural Variations
A. large, Mb-scale CNVs ① single-cell sequencing data ② WES B. small copy-number change ① genotype data ② Haplotype phasing
4. Increasing Accuracy by Haplotype Phasing
Read-Based Phasing for Mosaic SNVs
① Not rely on inheritance ② Applied to individual ③ Effective when consecutive variant loci are close
6. Concluding Remarks and Future Perspectives
Remarks :
Somatic mutations are being implicated in a growing number of diseases
New challenges: ① lower mutation rates ② low frequency of the majority of variants ③ artifacts Pitfalls Perspectives : Bioinformatics algorithms that incorporate refined filtering criteria
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Validation Methods for Mosaic Mutations
①droplet digital PCR (ddPCR)
②deep sequencing ：unique molecular barcodes and sheer sequencing depth ③Single-cell sequencing
1. Somatic Mosaicism and Challenges in Detecting Mosaic Variants
Strategy for VAFs Tumor tissue is compared to non-cancerous (‘normal’) tissue
Blue circle, mutation present; empty circle, mutation absent; blue shading, likely spatial distribution of mutation.
1. Somatic Mosaicism and Challenges in Detecting Mosaic Variants
Challenges ① Lower-frequency somatic mutations require higher sequencing depth ② Difficult to distinguish artifactual mutations
Heterogeneity
① Germline mutations (zygote ) ② somatic mutations (post-zygotically )
1.Somatic Mosaicism and Challenges in Detecting Mosaic Variants
Target ① Better understand the mutational processes in normal cells ② Explore cell lineages in development ③ Examine potential associations with age-related disease.
(c). Read-Mapping Problems ①Improperly aligned reads ②Reads are misplaced
5. Pitfalls in the Detection of Mosaic Variants
(d). Sequencing Artifacts ①homopolymer runs and high GC content ②early amplification errors ③uneven read depth ④platform specific errors ⑤misclassification