Next Generation Sequencing Technology

Transcription

1 Next Generation Sequencing Technology Henk Buermans, PhD FOS: Bio-informatics; Computational Biology of Complex disease and Ageing 2013 BW 4/5

2 What is Next Gen Sequencing Analysing complex mixtures of nucleic acid strands Relatively novel sequencing technology development Short sequencing reads MILLIONS of reads in parallel a single run Gigabase scale 2

3 Why Next generation Sequencing Cost Per base cheaper than Sanger sequencing Throughput >575 Gb of data per run (HiSeq). Enables multiplexing Accuracy Generates high accuracy raw data with reliable and informative quality metrics Ease of use Simplified & automated library preparation accessible technology Completeness Whole genomes, regions, genes RNA populations, Interactome Utility Multiple applications 3

4 Sequencing technology: Output Revolution Stratton, nature 458, ,

5 Sequencing by synthesis basics DNA sequence primer DNA Polymerase dntps ssdna template Unique localization Eavesdropping on the DNA polymerization reaction 5

6 Sequencing LGTC First NGS LGTC : dec 2006 Solexa December 2009 December 2010 December 2009 March November 2011

7 Other sequencing platforms Illumina HiSeq 2500 LifeTech Proton Illumina MiSeq LifeTech Solid 7

8 Sequencing platforms development!!! Source: Seqbench 8

9 Sequencing platforms Output Sequence by Template dntps (A,G,C,T) Run time read length #Reads per unit #Gb output Illumina HiSeq2000 LifeTech Iontorrent PacBio SMRT Seq synthesis synthesis synthesis polony polony Single molecule simultaneous sequential simultaneous 12 days 4h 2h 2x , *106 4* *

10 Illumina Hiseq : Solexa Bought by Illumina Upgrades to improve output and quality GAII Early 2011: HiSeq million reads per lane Paired-end 2x100 bp reads 36 Gbase per lane 288 Gbase per flowcell 576 Gbase per run Run folder per flowcell: ~3 Tb Fastq datafiles: 20Gb per lane (.gz) 10

11 Over-simplified library preparation DNA fragment of interest Adapter oligo ligation Library pre-amplification Sequencer Different experiment types different library characteristics for QC 11

12 Hiseq2000 cluster generation DNA ( µg) 12

13 Hiseq2000 cluster generation DNA ( µg) 13

14 Hiseq2000 cluster generation DNA ( µg) 14

15 Hiseq2000 cluster generation DNA ( µg) Cluster growth 15

16 Hiseq2000 cluster generation 100 µm DNA ( µg) Cluster growth 16

17 Hiseq2000 cluster generation 100 µm DNA ( µg) Cluster growth 17

18 Hiseq2000 Sequencing by synthesis

19 Hiseq2000 Sequencing by synthesis 3 Cycle 1: Add sequencing reagents A C G T C A T G A T G C 5 19

20 Hiseq2000 Sequencing by synthesis 3 Cycle 1: Add sequencing reagents First base incorporated A T C G T C A T G A T G C 5 20

21 Hiseq2000 Sequencing by synthesis 3 Cycle 1: Add sequencing reagents First base incorporated Remove unincorporated bases T 5 21

22 Hiseq2000 Sequencing by synthesis 3 Cycle 1: Add sequencing reagents First base incorporated Remove unincorporated bases T Detect signal 5 22

23 Hiseq2000 Sequencing by synthesis 3 Cycle 1: Add sequencing reagents First base incorporated Remove unincorporated bases A T G C Cycle 2-n: Add sequencing reagents and repeat G T Detect signal C A T G A T G C 5 23

24 Hiseq2000 Sequencing by synthesis 3 Cycle 1: Add sequencing reagents First base incorporated Remove unincorporated bases T G C T A Detect signal Cycle 2-n: Add sequencing reagents and repeat C G A T A All four labelled nucleotides C in one reaction C C G A High accuracy Base-by-base sequencing T C G A 5 T 24

25 Hiseq2000 Sequencing by synthesis T G T A C G A T sequencing cycles T T T T T T G T 25

26 Hiseq2000 Sequencing by synthesis 26

27 Hiseq2000 Paired end sequencing Paired-end: read both ends of the insert Read1 Read2 the read 1 sequence the read 2 sequence distance between R1&R2 on the genome index 27

28 Individual Genomes: Kriek Why? show it is possible [technical, computational, analytical] to learn [technology, data floods, analysis] attractive project to tackle Results technically - no problem computationally - at our limits analytically - not possible Next: to be applied in patients Resolve Genetic Disease 30

29 Individual Genomes: Kriek Female / Male: no Y-chromosome sequences F origin mtdna > haplogroup H4a > Europe phenotype red hair blue eyes other height, disease risks 31

30 Individual Genomes: Kriek Europe World = Marjolein Kriek

31 Individual Genomes: Kriek

32 Individual Genomes: Kriek

33 Individual Genomes: Kriek

34 Individual LGTC 2008: Kriek Genome 2011: 1x 2012: Centre for Genome Diagnostics: 9x +1x Prof Johan den Dunnen + 1x DJ Daniël Lippens (SlamFM) Sequencing data generated in 2 months! analysis ongoing.. Genome of the Netherlands project: 250 couples and their offspring (trio s) will map genetic variation in the Netherlands

35 Ion Torrent IonTorrent (LifeTech) Personal Genome Machine Installed april 2011 No light, H+ detection! 37

36 Ion Torrent: no clusters spheres DNA Library Emulsion PCR (water-oil emulsion) Single reaction chambers containing Ion-spheres library template primers epcr 38

37 Ion Torrent: no flowcell CMOS chip One sphere per well CTGAAATCTTCCCGAACATGCAATTCGAAGCCA GAAGGGCTTGTACGTTAAGCTTCGGT 39 Sequence primer

38 Ion Torrent: sequencing by synthesis Native nucleotides One nucleotide at a time (flow) Successive cycling of the 4 nucleotides 40

39 Ion Torrent: sequencing by synthesis Native nucleotides One nucleotide at a time (flow) Successive cycling of the 4 nucleotides 41

40 Different plaforms different output Human genome: 3 Gbase 30x coverage 90 Gb needed HiSeq: one run: 576 Gb output 5-6 genomes per run one genome per 2 days IonTorrent: one run: 0.8 Gb 113 runs per genome (2 runs/day) one genome per 56 days 42

41 G. Moore Whole genome on PGM Average 10.6x coverage 40 Mb per chip (old output specs) 800 Chips!? 2,598,983 SNPs in the G. Moore genome (~9/10 Kb) 3.08% were found to be novel Rothberg, J. Nature 475, (21 July 2011) 43

42 Illumina -vs- LifeTechnologies Both companies are upscaling their systems HiSeq 2500 Proton sequencer AND CHEAPER RUN COSTS? 44

43 Illumina -vs- LifeTechnologies Both systems have a limited max read length Drop in quality during run Limited reagent life span Out of phase molecules Physical limitation for handling long template molecules Polony based sequencing epcr and bridge amplification are inefficient Artefacts: GC bias; length limitations PCR slippage Some DNA molecules cannot be PCR'd Avoid the PCR amplification single molecule sequencing 45

44 SMRT-Seq: Pacific Biosciences PGM PacBio 46

45 SMRT-Seq: basic technology 47

46 SMRT-Seq: basic technology 48

47 SMRT-Seq: basic technology Movie Per SMRT cel : 2x75,000 ZMWs 75 fps Filming polymerase activity at 2-3 extension events per second 49

48 SMRT-Seq: Library prep gdna Fragmentation covaris g-tubes End Repair Adapter ligation 50

49 SMRT-Seq: Library prep Exonuclease treatment 51

50 SMRT-Seq: Library prep add primer add polymerase 52

51 > 22Kb Aligned Read 53

52 SMRT-Seq: LGTC projects Hybrid de novo assembly Bacterial re-sequencing Transcriptome analysis Plasmid re-sequencing Repeat mapping Targetted re-sequencing Base modifications Variant confirmation 54

53 SMRT-Seq: Base Modifications Base modification plays a role in a variety of biological processes such as: Gene expression Host-pathogen interaction DNA damage DNA repair 55

54 SMRT-Seq: Base Modifications Potential Epigenetic applications Elongation of a ma template takes longer than a A 56

55 SMRT-Seq: Base Modifications Proof of Principle Un-methylated Lambda, control Methylated Lambda, EcoRI Methyltransferase Methylated Lambda, Normal (dam+) Lambda genome ~50 kb 5 EcoRI sites EcoRI Methyltransferase 57

56 SMRT-Seq: Base Modifications 58

57 Future developments. Longer sequences Higher quality data More sequences Getting the data in less time Lower costs Less work to generate the data 59

58 Future developments. Nanopores 1 nm Solid state Biological (e.g., alpha-hemolysin) translocationdna.mpg movie 60

59 Future developments. Expected from 20 GridIONs Human genome 15x in 15 min Less then $10 per Gb Read lengths upto 100 kbp Raw error rate 1% Source: 61

60 Future developments. BUT, no system available yet Source: 62

61 General considerations Think before you start Do I really need NGS to solve my biological question Which sequence platform should I use Is a suitable sample preparation available Biological replicates, experiment design Can I handle the bioinformatics Statistics? Rubbish in = rubbish out Contaminations Sample degradated Sample identity 63

62 Acknowledgements: LGTC team 64