INTRODUCTION TO NEXT-GENERATION SEQUENCING

Transcription

1 INTRODUCTION TO NEXT-GENERATION SEQUENCING Louis Letourneau, eng. and Mathieu Bourgey, Ph.D CAGEKID Cancer Genomics Workshop March 18 th - 21 st

2 CAGEKID M12 Meeting - 6 May 2011 My work Many facets of genomic and proteomic Technology development on new instruments Cancer Analysis ATRT (Atypical Teratoid Rhabdoid Tumor), pediatric DIPG (Diffuse Intrinsic Pontine Glioma), pediatric Clear Cell Renal Cancer (CageKid) Tool, pipeline development

3 Outline 1. The technology 2. Base calling 3. Mapping vs. assembly 4. SNV calling 5. Conclusion

4 Four Major Players Roche: 454 Life technology: SOLiD / ion torrent Illumina: Genome Analyzer / Hiseq / Miseq Pacific Biosciences: PacBio

5 A Pyrosequencing method. System Workflow: One Fragment = One Bead = One Read Four main steps: Generation of a single-stranded template DNA library Emulsion-based clonal amplification of the library Data generation via sequencing-by-synthesis Data analysis using different bioinformatics tools

6 The Roche 454/GS FLX Sequencing Technology 1. Samples consisting of longer sequences are first sheared into a random library of base-pair long fragments. 2. Adaptors essential for purification, amplification and sequencing are added to both ends of the fragments

7 3. Using the adaptors, individual fragments are captured on own unique beads. 4. The empcr amplifies each fragment several million times The entire fragment collection is amplified in parallel.

8 5. The PicoTiterPlate is loaded with one fragment carrying bead per well and smaller beads with the enzymes necessary for sequencing. 6. Sequencing is accomplished by synthesizing the complementary strands of the bead attached templates. The incorporation of a new base is associated with the release of inorganic pyrophosphate starting a chemical cascade. This results in the generation of a light signal which is captured by a CCD camera

9 Signal saturation CAGEKID M12 Meeting - 6 May 2011

10 SOLiD Sequencing Sequencing by Ligation method via emulsion PCR

11 SOLiD Sequencing 1. Ligation of 3bp specific primer 2. Primer detection 3. Protection of unextended fragments 4. Cleaving extended fragments 5. Repeat N times steps 1-4 in order to complete the fragment ligation 6. Removing the primer and ligate a n-1 primer 7. Repeat steps Repeat steps 6-7 with primer n-2, n-3 and n-4

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13 Ion Torrent CAGEKID M12 Meeting - 6 May 2011

14 Ion Torrent CAGEKID M12 Meeting - 6 May 2011

15 CAGEKID M12 Meeting - 6 May 2011 Ion Torrent Homopolymers

16 Bridge Sequencing Flow cells

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20 Different type of sequencing libraries From Glenn TC, Mol Ecol Resour adatped for 2013

21 CAGEKID M12 Meeting - 6 May 2011 Pacbio RS: Build template

22 Pacbio RS: Bind bells CAGEKID M12 Meeting - 6 May 2011

23 Bases CAGEKID M12 Meeting - 6 May 2011 Pacbio RS: What you get 15% error rate ~30k-60k reads (depends on movie setup) ~2kb median C2 chemistry 60,000,000 50,000,000 40,000,000 1 cell 16,000 14,000 12,000 10,000 30,000,000 20,000,000 10,000,000 8,000 6,000 4,000 2,000 bases filtered reads Readlength

24 Sample Preparation two sequencing modes LS long sequencing reads Standard Large insert sizes (2kb-10kb) Generates one pass on each molecule sequenced CCS high quality sequencing reads Circular Consensus Small insert sizes 500bp Generates multiple passes on each molecule sequenced

25 CAGEKID M12 Meeting - 6 May 2011 Pacbio RS: Methylation/Kinetics

26 CAGEKID M12 Meeting - 6 May 2011 Pacbio RS: Methylation/Kinetics

27 CAGEKID M12 Meeting - 6 May 2011 Oxford Nanopore Talked about in AGBT 2012, silence in AGBT 2013 Mentioned Error rate of ~4% (Q14) Said they would wait for <1% 2 types, GridION, MinION

28 Technology comparison instrument Pacbio Ion Torrent 454 Illumina SOLiD Method Single-molecule in real-time Ion semiconductor Pyrosequencing synthesis Ligation Read length 3kb average 200 bp 700 bp 50 to 250 bp or bp Error type indel indel indel substitution A-T bias single-pass Error rate % 13 ~1 ~0.1 ~0.1 ~0.1 Reads per run up to 4M 1M up to 3.2G 1.2 to 1.4G Time per run Cost per 1 million bases (in US$) Advantages Disadvantages 30 minutes to 2 hours 2 hours 24 hours 1 to 10 days, 1 to 2 weeks $2 $1 $10 $0.05 to $0.15 $0.13 Longest read length. Fast. Low yield at high accuracy. Equipment can be very expensive. Less expensive equipment. Fast. Homopolymer errors. Long read size. Fast. Runs are expensive. Homopolymer errors. high sequence yield, cost, accuracy Equipment can be very expensive. Low cost per base. Slower than other methods, read length, longevity of the plateform

29 Applications Equipment 454 Genome Quebec number 3 (1) Ion Torrent 1 Illumina MiSeq 1 SOLiD 0 Current Applications Small de novo genome sequencing Amplicon sequencing Metagenomics Small de novo genome sequencing Amplicon sequencing Metagenomics Validation Small de novo genome sequencing Amplicon sequencing Metagenomics Validation Transcriptome sequencing (RNA-Seq) Whole Exome Sequencing Whole Genome Sequencing Illumina HiSeq 2000/2500 Pacific Biosciences 12 1 Transcriptome sequencing (RNA-Seq) Whole Exome Sequencing Whole Genome Sequencing Small genomes, Long haplotype sequencing, Epigenomics

30 What the NGS problem is about? Strings of 100 (to 1kb) letters Puzzle of 3,000,000,000 letters Usually have 120,000,000,000 letters you need to fit Many pieces don t fit : sequencing error/snp/structural variant Many pieces fit in many places: Low complexity region/microsatellite/repeat

31 outline 1. The technology 2. Base calling 3. Mapping vs. assembly 4. SNV calling 5. Conclusion

32 Basecalling How do we translate the machine readouts to base calls? How do we estimate and represent sequencing errors?

33 33 Base quality and Phred scores Q_sanger = -10 log_10 (p) Q_illumina = -10 log_10 (p / (1-p)) Where Q is the quality and p is the probability of the base being incorrect.

34 From MICHAEL STRÖMBERG

35 CAGEKID M12 Meeting - 6 May 2011 Illumina: Pre-phasing & Phasing

36 Base qualities usage: Trimming Will generate input sequence data of various size!! low qualtity bases can bias subsequent anlaysis (i.e, SNP and SV calling, )

37 Outline 1. The technology 2. Base calling 3. Mapping vs. Assembling 4. SNV calling 5. Conclusion

38 Assembling vs Mapping contig1 contig2 assembly all vs all reads mapping all vs reference Reference

39 Genome/deNovo assembly One of the most important tasks in genome biology is to obtain a complete genome sequence Without a good reference genome several genetic analysis are not doable With the decreasing cost of sequencing the demand for genome/transcriptome assembly is increasing

40 De Novo assembly metrics There is no perfect metrics yet: Total amount of sequence assembled: Should fit the specie estimate Contig size distribution: Useful in transcriptome assembly N50: the largest number L such that the combined length of all contigs of length L is at least 50% of the total length of all contigs Note that these most popular metrics emphasize only size and poorly capture the contig quality

41 Assembling vs Mapping Mapping: useful for interrogating the known genome SNP detection (targeted and whole-genome) SV detection (sometimes) RNA sequencing ChIP sequencing Methyl-seq Assembly: Essential if no genome sequence Whole genome analysis Transcriptome analysis unbiased ascertainment of variation in known genome SV detection

42 Mapping chalenges Find the TRUE/BEST location of a read in the reference genome: There may be several, equally likely places in the reference sequence. If we were only looking for perfect matches to the reference, we would never see any variation. Any sequencing technology produces its own type of errors. We need to do that for each of the millions of reads in our sequencing data

43 Mapping quality Algorithms use a mapping quality score: Quantify the probability that a read is misplaced and report it in a phred score (bwa example) SUM_BASE_Q(best) : sum of base quality scores at mismatched bases for the alignment SUM_BASE_Q(i) same, but for each other possible alignment Li H, Ruan J, Durbin R. (2008). Genome Research

44 Illumina AB SOLiD Roche 454 gapped all alignments multithreaded BFAST X X X X X X Bowtie X X X X BWA aln X X X X BWA bwasw X X X X X ELANDv2 X X GenomeMapper X X X X gnumap X X X X karma X X X * MAQ X X MOSAIK X X X X X X MrFAST X X X MrsFAST X X Novoalign X X X X * RMAP X X SeqMap X X X SHRiMP X X X X X Slider X X SOAP2 X X X SSAHA2 X X X X SOCS X X SXOligoSearch X X X X Zoom X X * X

45 INDEL Cleaning

46 INDEL Cleaning

47 Outline 1. The technology 2. Base calling 3. Mapping vs. assembly 4. SNV calling 5. Conclusion

48 SNPs Modified from Bionformatics.ca

49 SNP Discovery: Goal sequencing errors SNP An accurate SNP dicovery is closely linked with a good base quality and a suffisent depth of coverage Modified from Bionformatics.ca

50 Modified from Nielsen et al June 2011 SNP and genotype calling workflow Bayesian approachs MLE approachs Bayesian, threshold or t-test approachs

51 Handling Trios Take advantage of duplicate data Mendelian segregation of alleles De novo mutation rate Modified from Bionformatics.ca

52 outline 1. The technology 2. Base calling 3. Mapping vs. assembly 4. SNV calling 5. Conclusion

53 Concluding remarks NGS offers a variety of technologies and methods There is no one size fits all. NGS is still an open fields where many area are under constructs NGS analyses requires both mathematics and informatics skills The major challenge is actually link to the compute and storage capacities

54 Lincoln Stein (

55 About DNA and computers We ll hit the $1000 genome probably in 2013, then need to go for the $100 genome The doubling time of sequencing is 5-6 months. The doubling time of storage and network bandwidth is 12 months. The doubling time of CPU speed is 18 months The cost of sequencing a base pair will equal the cost of storing a base pair by 2018

56 Acknowledgments This lecture uses material from : Guillaume Bourque Mathieu Bourgey