Inferring genetic diversity from nextgeneration

Transcription

1 Inferring genetic diversity from nextgeneration sequencing data Milano-Bicocca April 16-17, 2012

2 Outline Part I: Detecting low-frequency single-nucleotide variants (SNVs) Genetic diversity NGS platforms and their error patterns Read alignment SNV calling Part II: Local haplotype inference and global quasispecies assembly Local haplotype inference via read clustering Global haplotype assembly 2

3 Intra-host genetic diversity of RNA viruses Evolutionary dynamics high mutation rate high turn-over large population size Disease progression Vaccine design (immune escape) Antiretroviral therapy (drug resistance) 3

4 RNA viruses exist as quasispecies population consensus sequence _x i = X a j q ij x j Áx i 4

5 Intra-patient genetic diversity of tumors Evolutionary dynamics mutation rate can be elevated (genetic instability) high turn-over large population size Disease progression Drug resistance Within-patient tumor phylogeny Gerlinger et al (N Engl J Med 2012) 5

6 Subclonal tumor cell populations Ene & Fine (Cancer Cell 2011) 6

7 Deep sequencing of heterogeneous samples deep sequencing Mixed sample Aligned reads 7

8 Deep sequencing of heterogeneous samples deep sequencing Mixed sample Aligned reads Single nucleotide variants Sequencing error 8

9 Sources of genetic variation in read data Biological variation Contamination PCR errors: single-base error recombination selective amplification Emulsion PCR errors: single-base error Sequencing errors: single-base errors indels GAGGTAGGCTTG CAGTTAGGGTAG CAGGTAGGCTAG GAGATAGGCTAG CAGTTAGGCTAG GAGGTAAGCTTG TAGATAGGCTTG CAGTTAGGGTAG errors 9

10 Most reads are incorrect Let us assume (454 data) a sequencing error of 0.2% per base pair, and an average read length of 500 bp. Then the fraction of reads with at least one error is 1 ( ) 500 = Thus, over 60% of the reads are incorrect! 10

11 NGS-based diversity estimation: Main challenges 1. Alignment (mapping) uncertainty 2. Confounding sources of variation (errors) of multiple types 3. Short read length relative to genomic region of interest 11

12 Spatial scale of diversity estimation global local SNV 12

13 Pyrosequencing sample preparation DNA isolation Fragmentation Strand separation Adapter ligation Emulsion PCR on beads ~1 fragment/bead ~10 million copies Enzymes for pyrophophate sequencing added on smaller beads ~1 bead loaded with single-stranded DNA clone per well ~1.6 million wells 44µm Margulies et al (Nature 2005) 13

14 Pyrosequencing base calling Sequencing by synthesis Determine DNA by detecting light emission associated with base incorporation The four bases are cycled in the order T A C G camera (Signal intensity) (No. of nucloetides incorporated) Margulies et al (Nature 2005) 14

15 Flowgram key Margulies et al (Nature 2005) 15

16 Pyrosequencing errors In long homopolymeric regions, linearity between signal intensity and number of nucleotides incorporated fails. Insertion errors (overcalls): some beads get ahead of the others (carry forward) due to left-over nucleotides Deletion errors (undercalls): some beads get behind the others (incomplete extension) Margulies et al (Nature 2005), Balzer et al (Bioinf 2010) 16

17 Low-quality reads contribute disproportionally to the overall error rate Huse et al (Genome Biol 2007) 17

18 Error rates increase with read length homopolymeric regions (A, T, G, C) Gilles et al (BMC Genomics 2011) 18

19 Additional factors Position in reference sequence Sequence type (organism, genomic region) Region on PicoTiterPlate Distance to region center (chemicals) Distance to plate center (camera) Gilles et al (BMC Genomics 2011) 19

20 Quality scores, Q = log 10 (error rate) Original 454 score (v1.1.*) represents only the probability of overcall, given the observed signal intensity. Improved quality score accounts for Local noise (compare flow with rounded flow) Overall read noise (overlap of 0-mer and 1-mer distributions) Homopolymer count Loss-of-synchrony noise (likelihood of incomplete extension) Position on read Brockman et al (Genome Res 2008) 20

21 Illumina bridge amplification Multiple cycles of annealing, extension and denaturation to form clusters Initial priming and extending of single strand template Bridge amplification of immobolized template with adjacent primers Metzker (Nat Rev Genet 2011) 21

22 Illumina reversible terminators C A T C G T C C C C C C Metzker (Net Rev Genet 2011) 22

23 Mismatch rate increases with cycle number Bustard Ibis Kircher et al (Genome Biol 2009) 23

24 Mismatch rate depends on Sequence context and Substitution type Dohm et al (Nucl Acids Res 2008) 24

25 Quality scores Kircher et al (Genome Biol 2009) 25

26 Read coverage vs. GC content Dohm et al (Nucl Acids Res 2008) 26

27 NGS platforms Metzker (Nat Rev Genet 2011) 27

28 Comparing NGS platforms Harismendy et al (Genome Biol 2009) 28

29 Filtering reads ~300x Reumers et al (Nat Biotech 2011) 29

30 Read alignment (mapping) Task: Find the location of each read in a given reference genome in the presence of errors millions to billions dominated by sequencing errors, genetic diversity of the sequenced species, and uncertainty in reference genome assembly By comparison, traditional alignment finds matches in (remote) homologous sequences in large databases (Smith-Waterman, BLAST, FASTA) uses evolutionary models (DNA/protein substitution models, phylogenetic trees) 30

31 Read mapping Challenges: many short reads long genomes errors repetitive DNA Read mappers are based on indexing techniques to locate reads, followed by high-quality local alignments. Around 50 mapping programs currently available see wikipedia list Trapnell & Salzberg (Nat Biotech 2009) 31

32 De novo read assembly successful with Sanger sequencing data (800bp reads) better data structure for NGS data MacLean et al (Nat Rev Microbiol 2009) 32

33 Read mapping of diverse populations at high coverage Goals: 1. Multiple alignment of all reads 2. Error correction Strategies: Mapping to reference genome Mapping to consensus sequence Pairwise local alignments (Smith-Waterman) to reference or consensus Multiple sequence alignment (MSA) De novo assembly Account for quality scores, error patterns In practice, strategies are often combined. 33

34 MSA of reads 34

35 Example: HIV env gene 1. Locate reads by k-mer matching on reference (template) sequence (here: HIV-1 HXB2) 2. Build MSA in windows of size 70nt with overlap 20nt 3. Generate in-frame consensus sequences, concatenate 4. Align reads locally to consensus sequence (Smith-Waterman) 5. Remove indels causing frameshifts Archer et al (PLoS Comput Biol 2010) 35

36 Consensus versus reference template Archer et al (PLoS Comput Biol 2010) 36

37 SNV detection 37

38 SNV detection in a mixed sample Let q be the per-site error rate. The number of errors at position i is n i = 19 X i» Binom(n i ; q) where n i is the coverage. With i := n i q = E[X i ], approximately, X i» Pois( i) x = 3 For calling an allele observed x times, consider P (X i x) = 1 x 1 X k=0 ki e i k! 38

39 SNV detection via comparative sequencing Task: For each allele, decide whether its frequency in the tumor is higher than in normal control tissue. If so, the allele is called (i.e., likely to be a true biological variant), otherwise it is more likely to be an experimental error (i.e., noise). Requires a statistical framework for comparing allele counts 39

40 Simple approach (Varscan 2) For each allele, Fisher s exact test on No. of variant alleles No. of all other alleles Tumor Normal / 16 P = 0.34 Correct for multiple testing 1 / 18 Koboldt et al (Genome Res 2012) 40

41 Independent Poisson distributions (vipr) Allele count in tumor: X i» Pois(¹ i = n i q) Allele count in normal control: Y i» Pois( i = m i q) 3 / 16 For calling an allele observed x times in the tumor and y times in the control, consider P (X i Y i x y) = 1 x y 1 X k= 1 where Skel is the Skellam distribution. Skel(k j ¹ i ; i) 1 / 18 Altmann et al (Bioinformatics 2011) 41

42 Strand specificity Sequencing errors often occur predominantly on one strand, GTAAAAA 5 GTAAAAGCGTATG 3 3 CATTTTCGCATAC 5 CGCATAC whereas true variants do not. Altmann et al (Bioinformatics 2011) 42

43 Independent, but non-identical, error rates Let q ik be the error rate at position i on read k. If E ik indicates an error at position i on read k, P(E ik = 1) = q ik, then X i = X n i E ik k=1 is, in general, not binomial, but its distribution can be computed recursively using the discrete convolution formula. In the special case that q ik = q i for all reads k, X i» Binom(n i ; q i ) Macalalad et al (PLoS Comput Biol 2012) 43

44 Non-independent and non-identical error rates Let E ijk indicate the joint occurrence of errors at positions i and j on read k, P(E ijk = 1) = q ijk. Then the distribution of the number of joint errors X ij = n ij X k=1 E ijk Position j can still be computed recursively using the discrete convolution formula. Positions i and j are phased. Software: V-Phaser Macalalad et al (PLoS Comput Biol 2012) 44

45 Unphased versus phased SNV calling X ij = n X ij E ijk k=1 Macalalad et al (PLoS Comput Biol 2012) 45

46 Beta-binomial model (deepsnv) For each strand s, position i, and nucleotide b: ½ s;i;b ½ s;i;b» Beta(q s;i;b ; ) Y s;i;b» Binom(m s;i ; ½ s;i;b ) ) Y s;i;b» BetaBin(m s;i ; q s;i;b ; ) E[Y s;i;b ] = m s;i q s;i;b Var[Y s;i;b ] = m s;i q s;i;b + (m s;i q s;i;b ) 2 = ; and CV ¼ 1= Gerstung et al (Nat Commun 2012) 46

47 Minor allele frequency test 2 log L(X s;i;b; Y s;i;b j H 0 ) L(X s;i;b ; Y s;i;b j H 1 )» Â2 1 Gerstung et al (Nat Commun 2012) 47

48 Overdispersion phix replicates tumor vs. normal = 1000 = 137 Gerstung et al (Nat Commun 2012) 48

49 Null model Gerstung et al (Nat Commun 2012) 49

50 Test data: Known mix of 5 clones mix control 1,512 nt, ~ 50 SNVs PCR amplification 33x Alignment with novoalign Base calling if Q>25 PHRED Coverage ~10 5 Gerstung et al (Nat Commun 2012) 50

51 Performance: ROC curves Gerstung et al (Nat Commun 2012) 51

52 Performance comparison Gerstung et al (Nat Commun 2012) 52

53 Application: Renal cell carcinoma SNVs SNPs Gerstung et al (Nat Commun 2012) 53

54 Application: Renal cell carcinoma Gerstung et al (Nat Commun 2012) 54

55 Loss of heterozygosity 14 SNPs Allele frequency ratio r = f A / f a ¼ 2 on chr. 3 ½ = r=r 0 1 r=r 0 + n 1 Gerstung et al (Nat Commun 2012) 55

56 Evolutionary history Gerstung et al (Nat Commun 2012) 56

57 Local haplotype inference 57

58 Phasing improves limit of detection Under simplifying assumptions, including an i.i.d. error rate q, the probability of miscalling a d-tuple of phased SNVs (i.e., a haplotype) of frequency p is smaller than calling it correctly, if and only if q < ³ 1 p p 1=d q or, approximately, q < p 1=d d = degree of phasing p 58

59 Clustering reads locally local 59

60 Local haplotype inference via read clustering INPUT OUTPUT GAGGTAGGCTTG CAGTTAGGGTAG CAGGTAGGCTAG GAGATAGGCTAG CAGTTAGGCTAG GAGGTAAGCTTG TAGATAGGCTTG CAGTTAGGGTAG GAAGTAGGCTTG GAGATAGGCTAG GAGGTAGGCTTG GAGGTAAGCTTG GAAGTAGGCTTG CAGTTAGGGTAG CAGGTAGGCTAG CAGTTAGGCTAG CAGTTAGGGTAG GAGATAGGCTAG TAGATAGGCTTG GAGATAGGCTAG GAGGTAGGCTTG 3/10 CAGTTAGGGTAG 4/10 GAGATAGGCTAG 3/10 reads read clusters haplotypes frequencies 60

61 Probabilistic clustering We assume an i.i.d. error rate 1 µ. Main problem: number of clusters (haplotypes) unknown Bayesian approach Dirichlet process mixture (Chinese restaurant process) Gibbs sampler GTCTGGTACGCCCGAA GTCCCGTACGTACGAA ATCCCCGTCCGTTCGAA Zagordi et al (J Comput Biol 2010) 61

62 Likelihood The probability of observed reads r, given haplotype assignments c and haplotypes h, is P (r j c; h) = Y k µ m k µ m 0 1 µ k jb 1j ; m k = X i;j I(r i;j = h k;j )I(c i = k) matches m 0 k = X i;j I(r i;j 6= h k;j )I(c i = k) mismatches where B is the alphabet size. 62

63 Chinese restaurant process new

64 Finite mixture model, K < 1 y i» KX j=1 ¼ j F (y i j µ j ) ; M G 0 ¼ c i µ i y i N y i j c i ; µ» F (y j µ ci ) c j ¼» Discrete(¼) µ i» G 0 (µ) ¼» Dir( ; M) 64

65 Dirichlet process mixture, K 1 G y 0 i j µ i» F (y j µ i ) G µ i y i µ i j G» G(µ) N G» DP( ; G 0 (µ)) P (c i = c j c 1:i 1 )! P (c i 6= c j for all j < i j c 1:i 1 )! n i;c i 1 + i

66 Example: 19 reads of length 3 66

67 Gibbs sampler: 2000 reads, 100 haplotypes 67

68 Output: haplotypes, frequencies, posterior Haplotype frequency Posterior probability (confidence) haplotype index Zagordi et al (Nucl Acids Res 2010) 68

69 Performance of haplotype reconstruction, ROC curve (control experiment of 10 cloned viruses) Recall (ability to detect haplotypes) cut-off ( default 50) posterior ( default 0.9) PCR --- no PCR Precision (accuray of all haplotype calls) Zagordi et al (Nucl Acids Res 2010) 69

70 Haplotype frequency estimation no PCR PCR Frequency (estimated) Frequency (direct mapping) Zagordi et al (Nucl Acids Res 2010) 70

71 Clustering flograms (AmpliconNoise) CCAAATG Observed flowgrams f are obtained from ideal flowgrams u subject to noise (which is estimated from control data). P (f j u = n) T: A: C: G: Flowgrams are clustered (around ideal flowgram centers) with the distance measure d(f; u) = 1 M using an EM algorithm. MX i=1 log P (f i j u i ) Quince et al (Nat Meth 2009, BMC Bioinformatics 2011) 71

72 Application: HIV-1 B/C superinfection subtype B subtype C HIV pol gene average 72

73 Hepatitis C virus (HCV) Transmission bottlenecks Sequential bottlenecks during early acute infection Wang et al (J Virol 2010), Bull et al (PLoS Pathogens 2011) 73

74 Application: Dating infections with the coalescent Poon et al (AIDS 2011) 74

75 Application: rare variants 75

76 Application: quasispecies phenotypes predicted level of resistance 76

77 Application: HIV coreceptor use HIV can use CCR5 (R5 viruses) or CXCR4 (X4 viruses). The entry inhibitor Maraviroc targets only R5 viruses. Coreceptor use can be predicted from the env V3-loop sequence. Does the viral quasispecies contain X4 viruses? 77

78 X4 viruses emerge from pre-existing subpopulations prior to maraviroc treatment R5 X4 Archer et al (PLoS Comput Biol 2010) 78

79 Global haplotype assembly 79

80 Global haplotype assembly global 80

81 Global haplotype reconstruction: read graph GAGGTAGC GAGGTAGG CAGGTAAG GAGG GAGG CAGG GGTA GGTA GGTA TAGC TAGG TAAG GAGG GAGG CAGG GGTA GGTA GGTA TAGC TAGG TAAG GAGG CAGG GGTA TAGC TAGG TAAG begin GAGG CAGG GGTA TAGC TAGG TAAG end 81

82 Each path is a potential haplotype begin GAGG CAGG GGTA TAGC TAGG TAAG end begin GAGG CAGG GGTA TAGC TAGG TAAG end begin GAGG CAGG GGTA TAGC TAGG TAAG end begin GAGG CAGG GGTA TAGC TAGG TAAG end begin GAGG CAGG GGTA TAGC TAGG TAAG end begin GAGG CAGG GGTA TAGC TAGG TAAG end 82

83 Global quasispecies assembly methods Combinatorial assembly, based on the read graph Network flow Minimal path cover Greedy paths sampling (Prosperi et al., 2011, 2012) Graph coloring (Huang et al., 2012) Probabilistic assembly, based on HMM Integrating alignment (Jojic et al., 2008) Local-to-global using constraint-based DPM (Prabhakaran et al., 2010) Modeling recombinants 83

84 Probability of an edge u v in the read graph (which is transitively reduced, i.e., the Hasse diagram) length L q haplotypes u v N reads overhang Δ P (u! v; > k) = Ã 1 N Lq! k ¼ e kn Lq P (u! v; = k) = N Lq P (u! v; > k 1) Westbrooks et al (ISBRA 2008) 84

85 Quasispecies assembly via network flows (ViSpA) The value of the flow f through a read edge (b r, e r ) is the number of haplotypes that contain the read r. The cost function is derived from the overhang probability. Minimum Cost Parsimonious Quasispecies Assembly: Westbrooks et al (ISBRA 2008), Astrovskaya et al (BMC Bioinf 2011) 85

86 Quasispecies assembly as a minimal path cover Theorem (Dilworth, 1950; Hopcroft and Karp, 1973) (1) Every minimal cover of the read graph has the same cardinality (2) A minimal path cover can be computed in time O(N 3 ) Eriksson et al (PLoS Comput Biol 2008) 86

87 Chain decomposition 1000 reads from 5 haplotypes at 3% diversity 87

88 Chain decomposition 1000 reads from 5 haplotypes at 5% diversity 88

89 Chain decomposition 1000 reads from 5 haplotypes at 7% diversity 89

90 Haplotype frequencies: Generating reads from a (small) set of candidate haplotypes Estimate haplotype frequencies ¼p h using the EM algorithm ¼ h p(r) = X h2h ¼ h p(r j h) Eriksson et al (PLoS Comput Biol 2008) 90

91 ShoRAH Local Global Zagordi et al (BMC Bioinformatics 2010) 91

92 ShoRAH: Performance of haplotype reconstruction Ten haplotypes at equal frequencies, varying distances Eriksson et al (PLoS Comput Biol 2008) 92

93 Hidden Markov model generators haplotypes observed reads 93

94 Recombination generators haplotypes observed reads 94

95 Jumping hidden Markov model Many parameters requires strong regularization generators generator choice haplotypes observed reads Zagordi, Töpfer et al (RECOMB 2012) 95

96 Example: 3 generators + 24 recombinants Model selection Performance BIC score Fraction explained recombination no recombination Number of generators Hamming distance Zagordi, Töpfer et al (RECOMB 2012) 96

97 Summary Next-generation sequencing can be used to assess the diversity of mixed populations (e.g., tumors, RNA viruses) if sequencing errors are treated properly. Detecting low-frequency SNVs relies on careful modeling of count data and benefits from clonal control experiments. Local haplotype inference exploits phasing and results in improved error correction and in local diversity estimates of the population. Global quasispecies assembly is based either on HMMtype models or on combinatorial analysis of the read graph. It is limited by sequencing errors and population diversity. Software is available, but not yet fail-safe to use. 97

98 Review articles Beerenwinkel, N. & Zagordi, O. Ultra-deep sequencing for the analysis of viral populations. Current Opinion in Virology, 2011, 1, Vrancken, B.; Lequime, S.; Theys, K. & Lemey, P. Covering all bases in HIV research: unveiling a hidden world of viral evolution. AIDS Rev, 2010, 12, Barzon, L.; Lavezzo, E.; Militello, V.; Toppo, S. & Palù, G. Applications of next-generation sequencing technologies to diagnostic virology. Int J Mol Sci, 2011, 12, Metzker, M. L. Sequencing technologies - the next generation. Nat Rev Genet, 2010, 11,

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