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Introduction to NGS
- 1. Introduction to NGS Ana Conesa Head of Genomics of Gene Expression Lab Centro de Investigaciones Prínicpe Felipe aconesa@cipf.es http://bioinfo.cipf.es/aconesa
- 2. Next Generation Sequencing NGS has brought high speed not only to genome sequencing and personal medicine, but has also change the way we do genome research: Got a question on genome organization: SEQUENCE IT!!!!
- 3. NGS technologies Cost-effective Fast Ultra throughput Cloning-free Short reads
- 6. Roche 454
- 8. Solexa
- 9. Solexa
- 10. Solexa-HiSeq 200 Gb/run in 8 days 2x100 bp fragments 2 billion reads per run
- 11. Helicos
- 12. SOLiD
- 13. SOLiD * Sequencing output in “color space” * Needs reference genome to translate to base space.
- 14. SOLiD 5500 * Fifth 3-based encoded primer * Sequencing output in base space * No reference needed
- 15. 5500 xl-u SOLiD 180 Gb/run (microbeads) 300 Gb/run (nanobeads) 35-75 bp fragments 2.8 - 4.8 billion reads/run 2x6 lanes/run 96 bar-codes 99.99% accuracy
- 16. Pacific Biosystems Real time DNA synthesis Up to 12000 nt?? 50 bases/second??
- 17. Ion Torrent •$ 50.000 •$ 500 /sample • 1 hour/run • > 200 nt lengths •Reads H+ released by DNA polymerase
- 18. Comparison Roche 454 Solexa SOLiD •Long fragments •Short fragments •Short fragments •Errors: poly nts •Errors: Hexamer bias •Color-space •Low throughput •High throughput •High throughput •Expensive •Cheap •Cheap •De novo sequencing: •Resequencing: •Resequencing: •Amplicon sequencing •ChipSeq •ChipSeq •RNASeq •RNASeq •MethylSeq •MethylSeq
- 19. Applications De novo sequencing Resequencing Exome Sequencing RNA-seq Genome annotation Chip-seq Methyl-seq …….
- 20. Applications De novo sequencing Resequencing Exome Sequencing RNA-seq Genome annotation Chip-seq Methyl-seq …….
- 21. Basic steps NGS data processing QC and read cleaning
- 22. Basic steps NGS data processing QC and Mapping read cleaning
- 23. Basic steps NGS data processing QC and Feature Mapping read cleaning identification
- 24. Basic steps NGS data processing SNVs Indels Rearrang. QC and Feature RPKM Mapping Splicing read cleaning identification DNA Binding site
- 25. RNA-seq Elucidate gene models Quantify gene expression
- 26. RNA-seq Elucidate gene models
- 27. RNA-seq protocol* total RNA purification mRNA preparation oligodT RiboZ 2nd strand synthesis 1st strand synthesis fragmentation RNA DN *Solexa Pair-End A
- 28. RNA-seq protocol (II) 100bp lad A A adenylation 3’ ends A A A A A A A A 400-200 ligate adapters amplification library 400-200 SEQUENCING!
- 31. File formats fastq: sequence data and qualities SAM/BAM: mapping data and qualities
- 32. Some Figures How much does it “cost” (computationally) to sequence a human transcriptome? One human transcriptome: 100 Million reads 1 Solexa run ==8 lanes ==25 M reads/lane==2 x 4 G fastq/lane (PE) 32 G disk space Mapping @ processor 12 cores, 48 GB RAM , 4TB disk 24 hours SAM (Ascii) / BAM (Binary) output 36 G / 9 G
- 33. Applications of RNAseq Qualitative: Quantitative: * Alternative splicing * Differential expression * Antisense expression * Dynamic range of gene expression * Extragenic expression …. * Alternative 5’ and 3’ usage * Detection of fusion transcripts …. edgeR Tophat/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 Sequencing Gene A Gene B DNA (patient) 1 Produce shotgun library 2 Determine Capture exon sequences Map against 5 variants, 4 reference Filter, compare genome 3 patients 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
- 46. Miller syndrome
- 49. Species composition of metagenomic DNA extracted from mammoth hair.
- 50. Conclusions NGS is revolutionizing how we do genome research
- 51. Conclusions NGS is revolutionizing how we do genome research But it will also revolutionize our lives….
- 52. Conclusions NGS is revolutionizing how we do genome research But it will also revolutionize our lives…. If we manage to process and analyze the data
- 53. YOUR SUCESSFUL STORY??? Have a great MDA course?