SNP Detection for Massively Parallel Whole-genome Sequencing
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This document summarizes a new method for calling SNPs from Illumina sequencing data. The method uses a Bayesian approach that incorporates prior probabilities of SNPs, quality score recalibration based on mismatch patterns, and likelihood calculations based on multiple observations of alleles. Evaluation showed over 99.8% consistency between called SNPs and genotypes from Illumina microarray data.
SNP Detection for Massively Parallel Whole-genome Sequencing
- 1. Genome Research 19:1124-1132, 2009 Speaker: 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 genotype Recalibrate sequencing quality score Calculate 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.