Introduction to NGS

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Introduction to NGS - Ana Conesa -Massive sequencing data analysis workshop -Granada 2011

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  • 1. Introduction to NGSAna ConesaHead of Genomics of Gene Expression LabCentro de Investigaciones Prnicpe Felipeaconesa@cipf.es http://bioinfo.cipf.es/aconesa

2. Next Generation SequencingNGS has brought high speed not only to genome sequencingand personal medicine, but has also change the way we dogenome research: Got a question on genome organization: SEQUENCE IT!!!! 3. NGS technologiesCost-effectiveFast Ultra throughput Cloning-freeShort reads 4. Roche 454 pyrosequencing 5. Roche 454 pyrosequencing 6. Roche 454 7. GS Junior, benchtop 8. Solexa 9. Solexa 10. Solexa-HiSeq200 Gb/run in 8 days 2x100 bp fragments2 billion reads per run 11. Helicos 12. SOLiD 13. SOLiD* Sequencing output in color space* Needs reference genome totranslate to base space. 14. SOLiD 5500* Fifth 3-based encoded primer* Sequencing output in base space * No reference needed 15. 5500 xl-u SOLiD180 Gb/run (microbeads)300 Gb/run (nanobeads) 35-75 bp fragments2.8 - 4.8 billion reads/run2x6 lanes/run96 bar-codes99.99% accuracy 16. Pacific BiosystemsReal time DNA synthesis Up to 12000 nt??50 bases/second?? 17. Ion Torrent $ 50.000$ 500 /sample 1 hour/run > 200 nt lengthsReads H+ released by DNApolymerase 18. Comparison Roche 454Solexa SOLiDLong fragmentsShort fragmentsShort fragmentsErrors: poly ntsErrors: Hexamer bias Color-spaceLow throughputHigh throughputHigh throughputExpensive CheapCheapDe novo sequencing: Resequencing:Resequencing:Amplicon sequencing ChipSeqChipSeq RNASeq RNASeq MethylSeqMethylSeq 19. ApplicationsDe novo sequencingResequencingExome SequencingRNA-seqGenome annotationChip-seqMethyl-seq. 20. ApplicationsDe novo sequencingResequencingExome SequencingRNA-seqGenome annotationChip-seqMethyl-seq. 21. Basic steps NGS data processingQC and read cleaning 22. Basic steps NGS data processingQC and Mapping read cleaning 23. Basic steps NGS data processingQC andFeature Mapping read cleaning identification 24. Basic steps NGS data processingSNVs IndelsRearrang.QC andFeature RPKM Mapping Splicing read cleaning identification DNABinding site 25. RNA-seq Elucidate gene modelsQuantify gene expression 26. RNA-seqElucidate gene models 27. RNA-seq protocol*total RNA purificationmRNA preparation oligodTRiboZ2nd strand synthesis 1st strand synthesisfragmentation RNA DN *Solexa Pair-End A 28. RNA-seq protocol (II)100bp ladA Aadenylation 3 ends AAAAAAAA400-200ligate adaptersamplificationlibrary 400-200SEQUENCING! 29. Strand-specific RNAseq 30. Strand-specific RNA-seq 31. File formatsfastq: sequence data and qualities SAM/BAM: mapping data and qualities 32. Some FiguresHow much does it cost (computationally) to sequence a human transcriptome?One human transcriptome: 100 Million reads1 Solexa run ==8 lanes ==25 M reads/lane==2 x 4 G fastq/lane (PE) 32 G disk spaceMapping @ processor 12 cores, 48 GB RAM , 4TB disk 24 hoursSAM (Ascii) / BAM (Binary) output 36 G / 9 G 33. Applications of RNAseqQualitative: Quantitative: * Alternative splicing* Differential expression * Antisense expression* Dynamic range of gene expression * Extragenic expression. * Alternative 5 and 3 usage * Detection of fusion transcripts . edgeRTophat/Cufflinks DESeq Scripture baySeq Alexa NOISeq 34. Advantages of RNAseq? RNAseq microarrays* Non targeted transcript detection * Restricted to probes on array* No need of reference genome * Needs genome knowledge* Strand specificity* Normally, not strand specific* Find novels splicing sites* Exon arrays difficult to use* Larger dynamic range* Smaller dynamic range* Detects expression and SNVs * Does not provide sequence info* Detects rare transcripts* Rare transcripts difficult.. and. are there any disadvantages????? 35. Resequencing 36. Exome SequencingGene A Gene BDNA (patient)1Produce shotgun library2 Determine Capture exon sequences Map against 5variants, 4 referenceFilter,compare genome 3patients candidate genes Wash & Sequence 37. Exome capture 38. The principle: comparison of patients Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 candidate gene (shares mutation for all patients) mutation 39. ChipSeq 40. MethylSeq 41. MIDseq 42. Census NGS methods 43. Sucessful Stories 44. Miller syndrome 45. Species composition of metagenomic DNAextracted from mammoth hair. 46. ConclusionsNGS is revolutionizing how we do genome research 47. ConclusionsNGS is revolutionizing how we do genome researchBut it will also revolutionize our lives. 48. ConclusionsNGS is revolutionizing how we do genome researchBut it will also revolutionize our lives. If we manage to process and analyze the data 49. YOUR SUCESSFUL STORY???Have a great MDA course?