高通量测序技术简介ppt课件

1、12Next Generation SequencingSample fragmentationLibrary preparationSequencing reactionData analysis3Roche 454 焦磷酸测序Pyrophosphate Sequencing基本原理4454 sequencing： Emulsion PCR (emPCR)Mix DNA Library & capture beads(limited dilution)“Break micro-reactors”Isolate DNA containing beads Generation of mi

2、llions of clonally amplified templates on each bead No cloning and colony pickingCreate “Water-in-oil” emulsion + PCR Reagents + Emulsion Oil Perform emulsion PCR Adapter carrying library DNAABMicro-reactors Adapter complementEnrichAnneal Seqprimer5Centrifuge StepLoad Enzyme Beads454 sequencing： Dep

3、osition of DNA beads into the PicoTiterPlateLoad beads into PicoTiterPlate 6Illumina Solexa 合成测序Sequence by Synthesize基本原理7Clonal Single Molecule Arrays单分子克隆1000 molecules per 1 m cluster 1000 clusters per 100 m square40 million clusters per experimentPrepare DNA fragmentsLigate adaptersAttach singl

4、e molecules to surfaceAmplify to form clusters20 micronsSequence8Reversible Terminator Chemistry可逆终止反应O PPPHNNOOcleavagesitefluor3blockNext cycleIncorporationDetectionDeblock; fluor removalODNAHNNOO3O5free 3 endXOHAll 4 labelled nucleotides in 1 reaction9Sequencing-by-Synthesis (SBS)5GTCAGTCAGTCAGT3

5、5CAGTCATCACCTAGCGTAFirst base incorporatedCycle 1: Add sequencing reagentsRemove unincorporated basesDetect signalCycle 2-n: Add sequencing reagents and repeat1、每轮测序反应加入四种带有荧光标记的dNTP，末端带有可以被去除的阻断基团2、每轮反应只能整合一个核苷酸，仪器读取相应的荧光信号3、信号读取结束，用化学方法去除阻断基团，进行下一轮测序反应10123789456T T T T T T T G T T G C T A C G A T

6、 The identity of each base of a cluster is read off from sequential images根据每个点每轮反应读取的荧光信号序列，转换成相根据每个点每轮反应读取的荧光信号序列，转换成相应的应的DNA序列序列Base calling from the raw data11Solexa 测序测序 Workflow12ABI SOLiD 连接法测序Sequence by Ligation基本原理13文库制备：微珠单分子克隆141024种8碱基探针4色荧光，4种双核苷酸，每色荧光有256个探针(46)SOLiD 利用探针的连接反应读取模板的利用探

7、针的连接反应读取模板的DNA序列序列15连接法测序（一）每个探针进行检测的两个碱基后面有三个匹配碱基，因此一条测序引物读取的序列是不完整的每个探针进行检测的两个碱基后面有三个匹配碱基，因此一条测序引物读取的序列是不完整的测序引物与测序引物与adapteradapter退火退火探针连接，检测荧光探针连接，检测荧光切除荧光基团切除荧光基团第二轮探针连接，检测荧光第二轮探针连接，检测荧光切除荧光基团切除荧光基团16连接法测序（二）测序引物沿着测序引物沿着Adapter移动移动5次，确保每个位点都被检测次，确保每个位点都被检测17连接法测序（三）0位置是位置是Adapter的最后一个碱基，因此只检测一

8、次，的最后一个碱基，因此只检测一次，该碱基是进行解码所必须的。该碱基是进行解码所必须的。18Advantage & disadvantage454 sequencing读取长度大，400bp可以对未知基因组进行从头测序de novo sequencing当遇到polymer时，如AAAAAA等，荧光强度和碱基个数不成线性关系，判定重复碱基个数有困难Solexa sequencing高度自动化的系统读取片段多，适合进行大量小片段的测序，如microRNA profiling基于可逆反应，随反应轮数增加，效率降低，信号衰减，读取序列较短，给de novo sequencing 拼接带来困难

9、SOLiD sequencing每个碱基读取两次非常高的准确性，特别是对于SNP的检测灵活的系统，完善的磁珠编码系统，可以进行样品的pooling，分割测序区域读取长度受连接反应的轮数限制，给de novo sequencing 拼接带来困难19高通量测序的应用De novo 测序基因深度测序（genome re-sequencing）转录组深度测序（transcriptome re-sequencing）Digital expression profilingChIP-seqMethy-seq20Transcriptome resequencing：malignant pleural mes

10、otheliomas (MPMs) ：恶性胸膜间皮瘤pulmonary adenocarcinoma (ADCA)：肺腺癌21Transcriptome characteristicsSolid line： at least one readDashed line：at least 20 readsExpression difference between MPM and ADCA sample compare to a lung tissue controlAnalysis of percent-age of reads containing known coding region SNVs

11、 in the six tissue samples.SNV： Single Nucleotide Substitution Variant22 Tag SequenceBrainUHRSymbol Gene DescriptionGATCAAACCAAGGCCCAGGC118861RPL29Ribosomal protein L29GATCACTGTTAATGATTTGC162163PRDX2Peroxiredoxin 2GATCAGTGTCTTTTCAGCAC8535PFN2Profilin 2GATCATCATGACCAATGAAA730STMN4Stathmin-like 4GATCA

12、TGCTGGCTGCAAAGA9196COX5BCytochrome c oxidase subunit VbGATCCAAACCCAAGTCTTGA480GABRA1Gamma-aminobutyric acid (GABA) A receptor, alpha 1GATCCAAGATAAAGAAGGCA266271UBBUbiquitin BGATCCCAGACTGGTTCTTGA2171538RING1Ring finger protein 1GATCCCCAATTGACTCAGAG910DIRAS2DIRAS family, GTP-binding RAS-like 2GATCCGGG

13、GCTGCAGGCTTG1010PHF20PHD finger protein 20GATCCTACAGAAGTGGAGCT0113IGJImmunoglobulin J polypeptideGATCCTAGTAATTGCCTAGA46799FN1Fibronectin 1GATCCTGCGGGAGTCTCCCG4112NFIXNuclear factor I/X (CCAAT-binding transcription factor)GATCCTGTGAAGGCCTGGAA042TOP2ATopoisomerase (DNA) II alpha 170kDaGATCGAGACACGTGAT

14、GGGA8346KRT8Keratin 8GATCGAGGACAGTGCAACCA059ITGB7Integrin, beta 7GATCTCAATGCCAATCCTCC24081BASP1Brain abundant, membrane attached signal protein 1GATCTGCACGCCGCTGACCC510DRD1IPDopamine receptor D1 interacting proteinGATCTGTGCCCAGAGATGGG7160GFAPGlial fibrillary acidic proteinGATCTGTGGAGAATGTACAC13269AP

15、LP2Amyloid beta (A4) precursor-like protein 2Digital expression profiling（1）：人大脑组织与UHR（Universal Human Reference）的表达差异23Digital expression profiling &microRNA re-sequencing：hESC： human embryonic stem cellsEB： embryoid bodies24ChIP-seq（1）：人一号染色体DNA-蛋白相互作用25ChIP-seq（2）： Sequenced short reads (typi

16、cally 2550 bp) from ChIP-Seq experiments are first mapped onto the reference genome. The mapped reads are then used to estimate statistical parameters, which include the estimation of the average length Fof sequenced DNA fragments.26Methy-seq（1）：肿瘤和MCF7细胞系中 BRCA!启动子区域的甲基化差异27Some highlights:Correlat

17、ion between ChIP-Seq and his prior SAGE-like method (called GMAT) has r=0.906However the resolution with ChIP-Seq was dramatically higher. Furthermore, ChIP-Seq was more sensitive and generated less false-negative regions12,726 genes whose transcription levels are known in CD4+ T-cells were correlat

18、ed with the histone modifications and 35,961 Pol II binding site islands were identifiedThis cost-effective method produces digital-quality data and should find broad applications in our efforts to understand the contribution of the human epigenomes in gene expression and epigenetic inheritanceMethy

19、-seq（2）：28部分参考文献阅读Genome re-sequencing van Orsouw N J, Hogers R C, Janssen A, et al. Complexity reduction of polymorphic sequences (CRoPS): a novel approach for large-scale polymorphism discovery in complex genomes. PLoS ONE, 2007, 2(11): e1172Hillier L W, Marth G T, Quinlan A R, et al. Whole-genome

20、 sequencing and variant discovery in C. elegans. Nat Methods, 2008, 5(2): 183188Transcriptome re-sequencingMortazavi A, Williams B A, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods, 2008, 5(7): 621628Sugarbaker D J, Richards W G, Gordon G J, et al. Transcriptome sequencing of malignant pleural mesothelioma tumors. Proc Natl Acad Sci USA, 2008, 105(9): 35213526 Digital expression profilingRuby J