Mapping sequence reads & Calling variants

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Birgitta Eklund
för 4 år sedan
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1 Universitair Medisch Centrum Utrecht Mapping sequence reads & Calling variants Laurent Francioli

2 Next Generation Sequencing

3 Data processing pipeline

5 Mapping to reference Human Reference Sequence GTGCCAGGACCAGATCGTGCCAACGGACAGGTGGTAAGGAAGGAG Sequence reads GTGCCAAGGA CAACGGACAG AGGTGGTAAG

6 Mapping to reference Human Reference Sequence GTGCCAGGACCAGATCGTGCCAACGGACAGGTGGTAAGGAAGGAG GTGCCAAGGA GTGCCAAGGA

7 Mapping to reference Human Reference Sequence GTGCCA-GGACCAGATCGTGCCAACGGACAGGTGGTAAGGAAGGAG GTGCCA-AGGA GTGCCAAGGA GTGCCAAGGA GTGCCAA-GGA

8 Mapping to reference Human Reference Sequence GTGCCAGGACCAGATCGTGCCAACGGACAGGTGGTAAGGAAGGA Millions of sequence reads GTGCCAAGGA CAACGGACAG AGGTGGTAAG

9 Coverage Human Reference Sequence GTGCCAGGACCAGATCGTGCCAACGGACAGGTGGTAAGGAAGGAG GTGCCAGGAC A C CCAGGAT G A C GAT CAGATCG ACCAGATCGT GTGCCAA-GGA CAACGGACAG GGAAGGAG

10 Real NGS aligned data in IGV

11 Marking Duplicates

12 Indel realignment

13 Indel realignment - example

14 Indel realignment - example

15 Base qualities ACTGCCAGGTNTCAGTACA

16 Base quality recalibration Downstream models use base qualities as input Base qualities have systematic machine-specific biases

17 Analysis-ready reads Reads are correctly placed in the genome Mapping qualities model the uncertainty of mapping Reads are independent Clonal (duplicate) reads are marked Base qualities are calibrated Base qualities model the uncertainty of the base call

18 Data processing pipeline

19 SNPs and indels Single Nucleotide Variants (SNVs) ATATTATTGCCCAGACCAATGTCTTGGAGTTATTCCCCTGT ATATTATTGCCCAGACCAATGGAGTTATTCCCCTGT

20 NGS data is noisy

21 Diploid is difficult Homozygous All reads should show the same allele Heterozygous ~50% of the reads should show each allele

22 SNP Calling and Genotyping Human Reference Sequence GTGCCAGGACCAGATCG GTGCCAG GTGCCAGG G G GTGCCAT T GA GTGCCAGGACC GCCAGGACCAGA GCCAGGACCAGAT G G CCAGGACCAGA CCAGGACCAGAT AGGACCAGATCG GGACCAGATCG G

23 SNP Calling and Genotyping Human Reference Sequence GTGCCAGGACCAGATCG GTGCCAG GTGCCAGG G G GTGCCAT T GA GTGCCAGGACC GCCAT GACCAGA GCCAGGACCAGAT G G CCAT GACCAGA CCAGGACCAGAT AGGACCAGATCG T GACCAGATCG

24 Genotyping Formalism (GATK) Bi-Allelic Model A: Reference allele B: Alternate allele Homozygous Heterozygous P(b AA) = 1 e, if b = A b e b / 3, if b A P(b AB) = (1 e )/2+ e b b / 6, if b {A, B} e b / 3, if b {A, B}

25 Interpreting NGS genotypes

26 Interpreting NGS genotypes

27 Interpreting NGS genotypes

28 Interpreting NGS genotypes

29 Two approaches for calling SNPs and indels Unified Genotyper Haplotype Caller Calls SNPs and INDELS separately Evaluates each base independently Calls SNPs and INDELs simultaneously Uses all bases connected through local de novo assembly Fast! No need for indel realignment More accurate, especially for indels Supports N+1 genotyping

30 Data processing pipeline

31 Variant Annotations Reads Strand bias Quality / Depth Mapping quality ranksum Biology / Population Hardy-Weinberg Equilibrium Haplotype Score

32 Example: strand bias

33 Example: strand bias

34 Two ways of filtering Thresholds Pros Work for small datasets No need for external resources Cons Does not adapt to particular data Manual tuning Human bias Machine-learning Pros Multi-dimensional and complex model can fit the data better Ranking of variants using a single metric Cons Need training data Need enough data

35 Machine learning filtering

36 Filtering Evaluation Transition / Transversion (Ti/Tv) Proportion of novel variants Heterozygosity Allele frequency spectrum of variants Genotype concordance against chip (if available)

37 Ti/Tv-based filtering threshold Sensitivity (proportion of sites in training set) Specificity (known TiTv/novel TiTv)

38 Genome of the Netherlands (GoNL) FR 62 GR 57 ZH 168 NH 91 UT 48 FL 0 GD 57 DR 56 OV 58 ZL 46 NB 68 LI 58

39 GoNL SNPs

40 Variation captured by GoNL

41 Resources GATK homepage GATK video workshop events?id=2038#materials GoNL homepage

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