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RNA-Seq Analysis: An Introduction to Mapping, Quantification, and Differential Expression
- 1. NGS APPLICATIONS 2: INTRODUCTION TO RNASEQ ANALYSIS
- 2. Overview • Earlier: libraries to raw reads. Now • What to do with RNA-‐seq reads? • How to design a RNA-‐Seq experiment?
- 3. Blencowe B J et al. Genes Dev. 2009;23:1379-1386 Illumina HiSeq
- 4. Reads are ready. Now What? bcl2fastq Big Fastq files (2-‐30Gb) • Reads represent real biology. • More reads corresponding to a transcript indicate higher abundance of that transcript. • Reads may represent novel transcripts or novel arrangements of exons that are not present in any known reference genome. • New exon-‐exon juncIons, RNA-‐ediIng, and nucleoIde variaIons (SNPs) may all be present in the read data. How do we translate these raw reads into biological knowledge: start with sequence alignment.
- 5. Reads are ready. Now What? Fastq Do we have a genome reference? Yes Do we a transcript/gene annotaIon reference? Yes No No Perform full de novo transcriptome construcIon Perform alignment-‐guided de novo transcriptome assembly Align to the genome. QuanIficaIon Only: accept only alignments that correspond to known transcripts Align to known exons but accept alternaIve arrangements. Align to known exons plus other regions. Like microarray
- 6. What to map to? Map to a genome with no gene annotaSon. • Assembling transcripts from exon regions is difficult and requires complex staIsIcal algorithms. • IdenIfying alternaIve transcript isoforms is unreliable. • Usually this is best for a novel or unannotated genomes. Exons ? Genome ref
- 7. What to map to? Map to the genome, with knowledge of transcript annotaSons • Well annotated genome reference is required. • To effecively map to exon juncIons, you need a mapping algorithm that can divide the sequencing reads and map porIons independently. • IdenIfying alternaIve transcript isoforms involves complex algorithms.
- 8. Which sequence mappers to use? • RNASeq Alignment algorithm must be – Fast – Able to handle SNPs, indels, and sequencing errors – Maintain accurate quanIficaIon – Allow for introns for reference genome alignment(spliced alignment detecIon) • Burrows Wheeler Transform(BWT) mappers – Fast – Limited mismatches allowed (<3) – Limited indel detecIon ability – Examples: BowIe2, BWA, Tophat – Use cases: large and conserved genome and transcriptomes • Hash Table mappers – Require large amount of RAM for indexing – More mismatches allowed – Indel detecIon – Examples: GSNAP, SHRiMP, STAR – Use case: highly variable or smaller genomes, transcriptomes
- 9. RNA-‐Seq reads Alignment Assemble Transcripts fastq file SAM/BAM file Transcript isoforms Gene or transcript quanSficaSon Count reads HTseq -‐ h_p://www-‐huber.embl.de/users/ anders/HTSeq/doc/overview.html Cufflinks -‐ h_p://cufflinks.cbcb.umd.edu/ Bioconductor -‐ h_p://www.bioconductor.org/ Trinity -‐ h_p://trinityrnaseq.sourceforge.net/ Cufflinks -‐ h_p://cufflinks.cbcb.umd.edu/ Generalized Analysis Workflow BowIe2, BWA, Tophat, GSNAP, SHRiMP, STAR
- 10. RNA-‐Seq reads Align to the genome using BowIe/Tophat. Tophat Cufflinks Spliced Fragments align to known exon-‐exon juncIons. Genomic mapped reads may idenIfy novel isoforms. fastq file SAM/BAM file Genome reference .fasta Gene annotaSons .g^ Genome reference .fasta Gene annotaSons .g^ Transcript isoforms Gene/transcript quanSficaSon Cufflinks idenIfies mutually exclusive exons. Graph-‐based analysis uses a shortest-‐path algorithm to determine Tophat/Cufflinks Workflow
- 11. Sequence Alignment Files BAM/SAM alignment files • SAM file is the standard alignment file format generated from all mappers • All alignments files are stored in a BAM file, an industry standard. • BAM is a compressed (binary) version of the SAM file. BAM is not readable. It can be indexed so that huge alignment files can be read and searched rapidly by other tools and genome browsers. • A suite of tools (called “samtools”) is used to convert between SAM and BAM. • Samtools can also be used to index bam file for faster visualizaIon, on IGV or UCSC Genome Browser
- 12. SAM format h_p://samtools.sourceforge.net/SAM1.pdf Format version Ref seq name Ref seq length Sort order Cigar String
- 13. h_p://samtools.sourceforge.net/SAM1.pdf CIGAR Strings Compact IdiosyncraIc Gapped Alignment Report
- 14. DifferenSal Gene Expression Analysis • Given samples from different experimental condiIons, find changes in transcriptome profiles • Allows for hypothesis genera0on on molecular abnormaliIes and mechanisms that may contribute to the tumor phenotype • Provides insights to potenIal biological mechanisms associated with experimental/ diseased condiIons
- 15. Sample annotations STAR aligner featureCounts DESeq, GSEA, QC HTML report Standard Transcriptome Sequencing Pipeline
- 16. This is really a simple sequence counSng problem Data: NGS randomly sample and sequence all gene transcripts from samples (so the number of reads correlate with the number of transcripts) ObjecSve: Does gene X has more copies in condiIon Z than in B (Z>B)? X Y Z X Y Z CondiSon Z CondiSon B
- 17. CounSng Rules for RNASeq • Count mapped reads, not base-‐pairs • Count each read at most once • Discard a read if – It cannot be uniquely mapped – Its alignment overlaps with several genes – The alignment quality score is bad – (for paired-‐end reads) the mates do not map to the same genes (poten0al fusion genes) • Do not discard if there is read duplicates (same reads appear mulIple Imes) • Keep track of alignment method and parameters
- 18. What kind of quesSons can be answered from sequence count data? Gene Healthy1 Health 2 Health 3 PaSent 1 PaSent 2 PaSent 3 CCT2 50 60 45 75 5 69 TP53 30 72 30 127 40 80 CXCR5 3 10 60 20 5 40 Gene Sequence Count Data Is gene TP53 upregulated in paSent samples? -‐ Hint: If healthy samples were sequenced at 20 million reads and paIent samples were sequenced at 80 million reads, does it change the answer? Is there more TP53 transcript copies compare to CCT2? -‐ Hint: TP53 transcript is a lot longer than CCT2
- 19. Direct comparison of read counts per gene is problemaSc More sequence reads mapped to a transcript if it is a) Long b) At higher depth of Coverage Read Counts = 12, Depth = 3X, Read Counts = 5, Depth = 3X Read Counts = 11, Depth = 5X Read Counts = 5, Depth = 3X Cannot claim blue transcript is transcribed at a higher level than green transcript based on read counts
- 20. NormalizaSon RNASeq Count Data • Data NormalizaIon is ALWAYS required to compare one sequencing result to another • Bring count data from different experiments to the same scale for comparison • RNASeq count data normalizaIon wants to adjust data such that: – gene with different lengths can be compared – Total sequence counts are considered
- 21. RPKM: Reads per Kilobase per Million Mapped Reads C = # of mappable reads in a feature (exon or transcript) N = # of mappable reads in the experiment L = length of the feature in base pairs The easiest way to normalize is take the number of the mapped reads on a transcript and divide by the length of the transcript and the number of total read Nature Methods -‐ 5, 621 -‐ 628 (2008) • Generally correct for biases • Vulnerable to bias by a few highly expressed genes driving N to be large • Used to be the standard, but not anymore
- 22. Other NormalizaSon Methods Upper QuarSle Method Aim: Correct for the bias that total read count is strongly dependent on a few highly expressed transcripts Method: Use the top 25% (upper quarIle)most expressed transcripts as scaling factor and report back Normalized Count Geometric Mean Method (the DESeq method) Aim: to minimize the effect of majority of sequences and concentrate on variaIon between condiIons AssumpSon: A majority of transcripts is not differenIally expressed Method: Take geometric means of read counts as reference value sj to normalize transcript count Bullard et al. BMC Bioinforma0cs 2010, 11:94 kij=number of reads in sample j assigned to gene i v = sample 1 to m
- 23. Inferring DifferenSal Expression (DE) Method NormalizaS on Needs replicas Input StaSsScs for DE Availability edgeR Library size Yes Raw counts Empirical Bayesian esImaIon based on NegaIve binomial distribuIon R/Bioconductor DESeq Library size No Raw counts NegaIve binomial distribuIon R/Bioconductor baySeq Library size Yes Raw counts Empirical Bayesian esImaIon based on NegaIve binomial distribuIon R/Bioconductor LIMMA Library size Yes Raw counts Empirical Bayesian esImaIon R/Bioconductor CuffDiff RPKM No RPKM Log raIo Standalone
- 24. Typical DE Result Table Gene or transcript name Mean expression levels Fold Change: measurement of changing magnitude, calculated as FC=baseMeanB/baseMeanA Typically Log2(FC) is reported Significance: use adjusted P value (padj) instead of raw P value (pval) unless you know what you are doing
- 25. Why use adjusted P-‐value instead of raw P-‐value? MulSple Comparison Problem – When large number of staIsIcal tests were performed simultaneously (as in genomic analysis), some tests will have P values less than 0.05 purely by chance, even if all your null hypotheses are really true. Benne@-‐Salmon-‐2009 The Dead Thinking Salmon Experiment -‐ Buy a whole salmon -‐ Take fMRI image of the salmon, which similar to genomic analysis asks the quesIon if a small region (voxels) of the brain is acIve -‐ Some region WILL BE significantly acIve if enough of picture and enough of voxel are taken -‐ SuggesIng the dead salmon is thinking… -‐ Nothing is significant if p-‐val is adjusted Methods for Adjustment: Bonferroni correcIon, FDR controlling procedures
- 26. Heatmap and Hierarchical Clustering • Most common representaIon for differenIal expression analysis • Hierarchical clustering on both samples are genes are oven performed to idenIfy similar samples/genes • Can be generated using many tools, such as R/Bioconductor heatmap and gplots package
- 27. FuncSonal Enrichment Analysis • Use gene expression to idenIfy pathways or gene funcIons that are over-‐represented • Address the quesIon: “What biological funcIons are different between sample groups?” • Many open-‐source and proprietary tools – GSEA (h_p://www.broadinsItute.org/gsea/index.jsp) – DAVID (h_ps://david.ncifcrf.gov) – TopGO/GOSEQ (R/Bioconductor) – Ingenuity Pathway Analysis (QIAGEN, proprietary) • Detailed discussion is out of scope for this course
- 29. Design RNASeq Experiment • Biological Comparison(s) • Replicates • Read length • Paired End/Single Read • Read depth • Pooling
- 30. Biological System in QuesIons Simple QuesSon Complex QuesSon Examples: • Cell line groups treated with different condiIons • PaIent groups with the same disease treated with different treatment Examples: • Matched paIent samples from both normal and diseased Issues • Normal and cancer samples obtained from genotypically diverse populaIon
- 31. Experimental QuesSons • What are my goals? – DifferenIal expression analysis of genes? – DifferenIal expression analysis of transcripts? – IdenIfy rare transcript isoforms? – IdenIfy transcript polymorphism? – IdenIfy non-‐coding RNA populaIons such as miRNA, lincRNA? • What are the characterisScs of systems? – Large, complex genome ? (ie. Human) – Highly heterogeneous sample populaIon ? (i.e. breast tumor) – No reference genome or transcriptome ? – High degree of alternaIve splicing?
- 32. Experimental QuesSons What are the sequencing opIons? How much money to spend?
- 33. What are Single Read (SR) and Paired End (PE) sequencing cDNA Single Read (SR) : only one end from each cDNA fragment is sequenced to generate one read per fragment Paired End (PE) : the cDNA fragment is sequenced from both ends to generate two reads per fragment from two direcIons
- 34. What are Single Read (SR) and Paired End (PE) sequencing Single Read (SR) -‐ Sample the same number of cDNA fragment as PE -‐ Generate half of the reads (half of the depth) than PE -‐ Suitable for gene expression level detecIon -‐ SubstanIally cheaper than PE Paired End (PE) -‐ Sample the same number of cDNA fragment as SR -‐ Allow for more accurate detecIon of structural variant, novel isoform idenIficaIon and quanIficaIon Reference Sequence
- 35. Impacts of Read Length on RNASeq Longer read length provides (ie. 75bp vs 50bp): -‐ be_er ability to assemble unknown transcripts -‐ Higher accuracy to map reads to complex regions (i.e. repeats, high polymorphic regions) -‐ Splice juncIon detecIon is most affected by read length Is long read length (ie. 100bp vs 50 bp) always give bejer? -‐ Not necessarily -‐ Long reads convey minimal to no advantage for differenIal gene expression analysis 50 bp 75bp 50 bp 75bp
- 36. Impacts of Sequencing Depth • Quick means to detect more genes and transcript variants with low expression (the more reads you sequence, the more genes you find) • Require logarithmic increase in depth for linear increase in gene detected X Y Z X Y Z RNASeq 1, 30 million reads RNASeq 2, 10 million reads
- 37. Number of reads needed for an experiment • Different RNA sequencing require different number of reads • More genes are detected with higher sequencing depth • However, the increase of detected genes reduces substanIally • Understand your sequencing system before deciding on depth • Can always increase depth by addiIonal sequencing on the same library – Unlike microarray there is very limited batch effect for RNASeq Differen0al expression in RNA-‐seq: A ma@er of depth. Genome Res. 2011.
- 38. Experimental Design • Technical replicates – Not needed: RNASeq have low technical variaIon • Minimize batch effects • Biological replicates – Not needed for novel transcript idenIficaIon and transcriptome assembly – EssenIal for differenIal expression analysis – Difficult to esImate the minimum number • 3+ for cell lines • 5+ for inbred lines (i.e. mouse, model organsims) • 20+ for human samples (usually unachievable) – Must have 3+ to perform staIsIcal analysis
- 39. Experimental Design • Pooling samples – Limited RNA obtainable • Tumor samples from hard to reach Issue type (i.e. brain) – Novel transcriptome assembly – Don’t do it unless you know what you are doing
- 40. QuesSons to ask when gekng raw RNASeq data back • How was the RNA extracted? • How was RNASeq library constructed? • Which playorm was the library sequenced on? • How long was the read length? • Was sequencing done with single read or paired end? • How many reads were sequenced per sample? • Where is the QC report?
- 41. Check list for gekng RNASeq DE analysis results back q Fastq files q FastQC Report q BAM files q RNASeq QC Report (Not discussed) q Table of DifferenIally Expressed Genes/ Transcripts q Heatmaps q FuncIonal Enrichment Analysis Table
- 42. Recognize Yourself as a Genomic Data Consumer BioinformaScists/Data ScienSsts -‐ Let data drive scienIfic hypothesis generaIon -‐ Start with raw data (i.e. fastq) -‐ Process raw data by privately tuned pipelines KNOW YOUR DATA SOURCE TranslaSonal ScienSsts -‐ Start with a specific hypothesis derived from observaIon -‐ Find processed to perform secondary analysis -‐ Use readily available tools -‐ Interpret results in the context of iniIal hypothesis KNOW YOUR TOOLS
- 43. THE END