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Rnaseq basics ngs_application1
- 1. NGS Applica+ons 1: Introduc+on to RNA Sequencing Gene%c Epidemiology Workshop Academic Sinica Thursday, 8/20/2015 Session 2 (10:30-‐12:00PM) Yaoyu E. Wang, Ph.D
- 2. Overview • Introduc%on • Sequencing Technology • Data Quality Control • Experimental Design
- 3. Applica+ons of Next-‐Genera+on Sequencing
- 4. Central Dogma of Molecular Biology Cartegni, L., Chew, S. L., & Krainer, A. R. Listening to silence and understanding nonsense: exonic muta%ons that affect splicing. Nature Reviews Gene/cs3, 285–298 (2002) Figure 1 from:h[p://www.nature.com/scitable/topicpage/gene-‐expression-‐14121669 Copyright 2010, Nature Educa%on
- 5. The complexity of gene regula+on Image from: Nature Reviews Gene/cs 12, 283-‐293 (April 2011) Gene Expression is influenced by a variety of mechanisms: -‐polymerase binding elements -‐proximal promoter sequences -‐upstream/downstream and distal enhancers/silencers -‐microRNA/RNAi -‐natural transcript stability and recycling
- 6. What ques+ons do we want to answer? SNP and Indel Detec%on REF ATCGGTACCATCCAGCTAAGGCT S1 ATCGGAACCATCCAGCTAACGCT S2 ATCGGTACCATC-‐-‐-‐CTAAGGCT S3 ATCGGAACCATCCAGCTAAGGCT S4 ATCGGTA-‐-‐-‐-‐-‐-‐-‐-‐CTAAGGCT • Which genes are expressed? • In experiments with mul%ple samples, which genes exhibit differen%al expression? • Can we detect splicing isoforms expression? • Can we detect novel genes or isoforms? • Can we detect structural variants? SNPs, inser%ons, dele%ons, RNA-‐edi%ng. • Can we detect ncRNA that controls gene regula%on • Can we use differen%al expression to construct biomarkers for diseases?
- 7. Personalized Cancer Genomics Muta+on Transloca+on Copy Number Varia+on Epigene+c Altera+on Protein altera+on Transcriptomic altera+on T *
- 8. What is RNASeq? RNASeq means the sequencing of RNA using NGS technology, which means that….. • Any type of RNA from any sample sources, such as cell, body fluid, stool, water, etc. can be the sequenced • Sample from different sample source require different extrac%on method • Different RNA species with different sizes (i.e. miRNA, snoRNA, tRNA) require different prepara%on protocol • RNASeq very strictly refers to the sequencing of mRNA from cells in this course
- 9. What is RNASeq Analysis? • Also known as Whole Transcriptome Shotgun Sequencing • Iden%fica%on and quan%fica%on of RNA snapshot from a genome at a specific %me point • Method to study how genes are being regulated for a give cell type (i.e. tumor cells v.s. normal cells) at a given %me using Next Genera%on Sequencing (NGS)
- 10. Sequencing • Plaforms • Library prepara%on • Mul%plexing • Sequencing reads
- 11. Next Genera+on Sequencing PlaQorms Illumina Sequencing by Synthesis (SBS) • HiSeq/NextSeq/MiSeq • Shorter read length (>150bp) • Higher throughput • Domina%ng the market Life Technology (ThermoFisher) Sequencing by pH Change Monitoring • Ion Torrent/Proton • MUCH Longer read length (400bp)
- 12. Illumina Sequence by Synthesis Overview © 2014 Illumina, Inc Fix nucleo%de (cDNA) onto flow cell Amplify cDNA to generate clusters Sequence cDNA by using nucleo%de bases with color dye
- 13. Illumina SBS RNASeq Work Flow Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing
- 14. Illumina SBS RNASeq Work Flow Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing Fresh Frozen Tissues -‐ Sample %ssues freeze to -‐80C or immerse in liquid nitrogen shortly aler sample extrac%on -‐ All RNA is intact in natural form but with slow degrada%on process -‐ Produce highest quality data -‐ Expensive to keep and rare to acquire Formalin Fixed Paraffin Embedded (FFPE) Samples -‐ Fix sample %ssues in paraffin wax immediately aler extrac%on -‐ All RNA are immediately sheared into fragments -‐ All mature mRNA lost poly-‐A tail -‐ Most common sample available from clinic -‐ Used in pathology lab -‐ Very cheap to store
- 15. Illumina SBS RNASeq Work Flow RNA Extrac+on Methods Column based RNA Extrac+on -‐ Majority of the vendor RNA Extrac%on -‐ Fast and convenient -‐ Can lose small RNA (<100bp) if not careful Phenol-‐Chloroform RNA Extrac+on -‐ Cheap but labor intensive -‐ Much higher RNA yield compare to column based extrac%on -‐ Preferred method for low quan%ty RNA sample -‐ Isolate both long (>100bp) and small RNA (<100bp) simultaneously Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing
- 16. Illumina SBS RNASeq Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing 80% 15% 5% RNA Composi+on within an eukaryo+c cell rRNA tRNA Other RNA • Pre-‐mRNA and mature mRNA composed of very small por%on of total RNA • MicroRNA, ncRNA, and others composed of even smaller number
- 17. Illumina SBS RNASeq Library Prepara%on Work Flow for mature mRNA -‐ RNA Isola%on -‐ Poly-‐A Purifica%on -‐ Fragmenta%on -‐ Convert RNA to cDNA using random primers -‐ Adapter liga%on -‐ Size selec%on -‐ PCR amplifica%on Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing
- 18. Illumina SBS RNASeq Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing Pease, Nature Methods 9 (2012) 1. RNA Isola%on and Poly-‐A purifica%on 2. RNA Fragmenta%on 3. Random hexamer priming 4. Generate cDNA 5. Remove RNA 6. Adapter liga%on 7. Purify cDNA by size selec%on 8. PCR amplicia%on
- 19. Sequencing Library Structure Adaptor 1 cDNA insert Adaptor 2 Barcode Adaptor – 58 bp nucleo%de sequence to fix sequence library onto flow cell Barcode – op%onal index sequence that is typically 6 nucleo%de bases long for associa%ng sequence with a par%cular sample (can be present on both adaptor) cDNA insert – fragmented cDNA sequence generated from mRNA of interest. The insert typically range between 300-‐500bp for mRNA
- 20. Illumina SBS RNASeq Determine Sequencing Library Quality Qubit (RNA) Measures the concentra%on of only double stranded DNA, more accurate than Nanodrop Bioanalyzer Measures the RNA/library size in base pairs qPCR Measure the concentra%on of library that has adaptors ligated and will hybridize and sequence Sample Acquisi%on RNA Extrac%on Library Prepara%on Sequencing
- 21. Determining Library size distribu+on
- 22. Determining Library size distribu+on Excellent Poor BAD
- 23. DNA (0.1-1.0 ug) " Single moleculearray" Sample preparation" Cluster growth" 5’" 5’"3’" G" T" C" A" G" T" C" A" G" T" C" A" C" A" G" T" C" A" T" C" A" C" C" T" A" G" C" G" T" A" G" T" 1 2 3 7 8 94 5 6 Image acquisition" Base calling" T G C T A C G A T … Sequencing" Illumina Sequencing Technology Robust Reversible Terminator Chemistry Founda/on
- 24. Sources of Error • Reading error ccatg -‐> ccnng • Single base error ccatg -‐> ccttg • Inser%on ccatg -‐> ccatcg • Dele%on ccatg -‐> cc-‐tg • Homopolymer errors aaaaatg -‐> aaaa-‐tg aaaaaatg aaaaatag Cycle 1 Cycle n Few errors per cluster Several errors: ambiguous call Sequencing by Synthesis
- 25. The run is finished. How are sequence files created? .bcl files Data Processing • Demul%plexing • Fastq file genera%on • Sequencing filtering Raw files containing base calls and quality scores Illumina defined quality filters Split into Project and Sample Folders Jones_Lab ChIP_A ChIP-‐B Marcus_Lab RNA-‐SeqA RNA-‐SeqB RNA-‐SeqC Williams_Lab Exome1 Exome2 Fastq Files Fastq Files Fastq Files
- 26. Illumina Fastq Format Fasta format >seqID CTTCAGACGAGTCGAGGAAAGGCTTTGCTGCTTTCCTTTACAGGGTGGGG Fastq format @HWI-‐ST389:225:D18R8ACXX:5:1101:1421:2191 1:N:0:CCGTCC CTTCAGACGAGTCGAGGAAAGGCTTTGCTGCTTTCCTTTACAGGGTGGGG + @@@DDDDFHHFCFFHIJIHIJGIFGIIHIIIJGIIJHIIJIIJIHDFHJE Illumina Fastq header: @<instrument>:<run number>:<flowcell ID>:<lane>:<%le>:<xpos>:<y-‐ pos><read>:<isfiltered>:<control number>:<indexsequence>
- 27. Illumina Fastq Format Quality Scores @@@DDDDFHHFCFFHIJIHIJGIFGIIHIIIJGIIJHIIJIIJIHDFHJE Illumina Fastq header: @<instrument>:<run number>:<flowcell ID>:<lane>:<%le>:<xpos>:<y-‐ pos><read>:<isfiltered>:<control number>:<indexsequence> • Each nucleo%de in a read has an associated quality value (1-‐40). • The numerical value is encoded as an ASCII character to save space. • Each q-‐value represents a probability that the nucleo%de is incorrect at that posi%on: Q(X) =-‐10 log10(P(~X)) Quality score Q(A) Error probability P(~A) 10 0.1 20 0.01 30 0.001 40 0.0001 Typical cutoff for acceptable quality
- 28. Visualizing Quality with FASTQC FASTQC h[p://www.bioinforma%cs.babraham.ac.uk/projects/fastqc/ FASTQC: A quality control tool for high throughput sequence data. THE GOOD
- 29. Visualizing Quality with FASTQC FASTQC h[p://www.bioinforma%cs.babraham.ac.uk/projects/fastqc/ FASTQC: A quality control tool for high throughput sequence data. THE BAD
- 30. Data Quality Assessment • Evaluate read library quality – Determine if the data is proper generated • No informa%on on if the data is what you want • Iden%fy technical ar%fact • Iden%fy poor quality samples • Key features to evaluate – Uniformity of sequencing quality score (phred score) – GC content distribu%on – Level of sequencing adapter contamina%on – Level of sequence duplica%on (may caused by PCR ar%fact, rRNA contamina%on, bacterial contamina%on) • Filter or trim data as needed using FASTX
- 31. Use FASTQC on GALAXY FASTQC -‐ provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. (h[p://www.bioinforma%cs.babraham.ac.uk/projects/fastqc/) GALAXY-‐ a scien%fic workflow, data integra%on,and data and analysis persistence and publishing plaform that aims to make computa%onal biology accessible to research scien%sts that do not have computer programming experience. (h[ps://galaxyproject.org/)