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SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
SNP Detection for Massively Parallel Whole-genome Sequencing
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SNP Detection for Massively Parallel Whole-genome Sequencing

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  • Transcript

    • 1. Genome Research 19:1124-1132, 2009Speaker: Eric C.Y., LEE
    • 2. Aim• They want to developed a SNP calling method for Illumina platform.• Consider the data quality, alignment and experimental error common to this platform.
    • 3. Applications of NGS• From whole genome sequence to know the gene variations between individuals. • Disease • Drug • Environment
    • 4. Workflow Sequencing reads Map reads onto reference genome Prior probability of each genotypeRecalibrate sequencing quality scoreCalculate likelihood of each genotype Inferred genotype via Bayes theorem
    • 5. Traditional Method• Phred score is a universal standard.• Compare the sample sequence with reference genome and filter low score mismatch.• A method to detect heterozygous polymorphisms.
    • 6. Prior Probability• According to existing researches • The estimated SNP rate between two human haploid chromosome is about 0.001. (Sachidanandam et al. 2001). • Human reference genome sequence has an error rate of 0.00001. (Collins et al. 2004) Set the homozygous SNP at 0.0005, and the hetrozygous rate is 0.001.
    • 7. Prior Probability• According to a previous study on dbSNP, transitions are four times more frequent than transversions among the substitution mutations. (Zhao and Boerwinkle 2002)
    • 8. Alignment• Indels is the error source.• Using SOAP for alignment.
    • 9. Recalibration• 3’ -end of reads have a much higher error rate than earlier cycles.• Original quality score can’t represent the true error rate.• Check the mismatch in dbSNP.
    • 10. Recalibration• Illumina uses two lasers. • A and C use the same laser, G and T use another. • A-C and G-T substitution were 58%-72% overestimated.• Duplicate reads • Penalty for these reads.
    • 11. Likelihood Calculation• Observed allele type• Quality score• Sequencing cycle• Observation of the same allele from reads with the same mapping location.
    • 12. Evaluation• Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed genotyped alleles on the X chromosome and autosomes were covered at 99.97% and 99.84% consistency, respectively.

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