Statistical Genomics and Bioinformatics Workshop. Genetic Association and RNA-Seq Studies

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Statistical Genomics and Bioinformatics Workshop: Genetic Association and RNA-Seq Studies RNA Seq and Differential Expression Analysis Brooke

1 Statistical Genomics and Bioinformatics Workshop: Genetic Association and RNA-Seq Studies RNA Seq and Differential Expression Analysis Brooke L. Fridley, PhD University of Kansas Medical Center 1 Next-generation gap 2 1

2 Why sequence RNA (versus DNA)? Functional studies Genome may be constant but an experimental condition has a pronounced effect on gene expression Some molecular features can only be observed at the RNA level Alternative isoforms, fusion transcripts, RNA editing Predicting transcript sequence from genome sequence is difficult Alternative splicing, RNA editing, etc. 3 Why sequence RNA (versus DNA)? Interpreting mutations that do not have an obvious effect on protein sequence Regulatory mutations that affect what mrna isoform is expressed and how much e.g. splice sites, promoters, TFBS Prioritizing protein coding somatic mutations If the gene is not expressed, a mutation in that gene would be less interesting If the gene is expressed but only from the wild type allele, this might suggest loss-of-function If the mutant allele itself is expressed, this might suggest a candidate drug target 4 2

Challenges to RNA Studies Sample Purity?, quantity?, quality?

$of magnitude Since RNA sequencing works by random sampling, a small fraction of highly expressed genes may consume the majority of reads RNAs come in a wide range of$ transcriptomics Hybridization-based RNA seq is still a technology under active development 1995 P. Brown, et. al.

transcriptomics Hybridization-based RNA seq is still a technology under active development 1995 P. Brown, et. al.

3 Challenges to RNA Studies Sample Purity?, quantity?, quality? RNAs consist of small exons that may be separated by large introns Mapping reads to genome is challenging The relative abundance of RNAs vary wildly orders of magnitude Since RNA sequencing works by random sampling, a small fraction of highly expressed genes may consume the majority of reads RNAs come in a wide range of sizes Small RNAs must be captured separately PolyA selection of large RNAs may result in 3 end bias RNA is fragile compared to DNA (easily degraded) 5 The evolution of transcriptomics Hybridization-based RNA seq is still a technology under active development 1995 P. Brown, et. al. Gene expression profiling using spotted cdna microarray: expression levels of known genes 2002 Affymetrix, whole genome expression profiling using tiling array: identifying and profiling novel genes and splicing variants 2008 many groups, mrna seq: direct sequencing of mrnas using next generation sequencing techniques (NGS) 6 3

RNA-Seq vs Microarrays General expression profiling Novel genes Alternative splicing Detect gene fusion Can use on any sequenced genome Better

4 RNA-Seq vs Microarrays General expression profiling Novel genes Alternative splicing Detect gene fusion Can use on any sequenced genome Better dynamic range Cleaner and more informative data Data analysis challenges 7 Advantages of RNA-Seq compared with other transcriptomics methods 8 4

Goals of RNA-seq Study Catalogue all species of transcript including: mrnas, non-coding RNAs and small RNAs Determine the transcriptional structure of genes in terms of: Start sites 5 and 3 ends

5 Goals of RNA-seq Study Catalogue all species of transcript including: mrnas, non-coding RNAs and small RNAs Determine the transcriptional structure of genes in terms of: Start sites 5 and 3 ends Splicing patterns / novel isoforms Other post-transcriptional modifications Quantification of expression levels and comparison (different conditions, tissues, etc.) Gene and exon level Determine Allelic expression Gene Fusion Transcriptome for non-model organisms 9 Computation for ChIP seq and RNA seq studies Shirley Pepke, Barbara Wold & Ali Mortazavi Nature Methods 6, S22 S32 (2009) Published online: 15 October

Coding RNAs, genes Non coding RNAs Ribosomal RNA Types of RNAs Type Size Function microrna (mirna) 21 23 nt regulation of gene expression small interfering RNA (sirna) 19 23 nt antiviral mechanisms

6 Coding RNAs, genes Non coding RNAs Ribosomal RNA Types of RNAs Type Size Function microrna (mirna) nt regulation of gene expression small interfering RNA (sirna) nt antiviral mechanisms piwi interacting RNA (pirna) nt interaction with piwi proteins/spermatogenesis small nuclear RNA (snrna) nt RNA splicing small nucleolar RNA (snorna) modification of other RNAs 11 RNA Pre mrna intron splicing exon mature mrna AAAAAAAAAAAAAAA A Poly A tail 12 6

7 Alternative Splicing 13 Next-Gen Sequencing (NGS) Long RNAs are first converted into a library of cdna fragments through either: RNA fragmentation or DNA fragmentation 14 7

In contrast to small RNAs (like mirnas) larger RNA must be fragmented RNA fragmentation or cdna fragmentation (different techniques) Types of bias: RNA: depletion for ends cdna: biased 5 end Zhong

8 In contrast to small RNAs (like mirnas) larger RNA must be fragmented RNA fragmentation or cdna fragmentation (different techniques) Types of bias: RNA: depletion for ends cdna: biased 5 end Zhong Wang, Mark Gerstein & Michael Snyder. RNA Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10,57 63 (January 2009) 15 Sequencing adaptors (blue) are added to each cdna fragment and a short sequence is obtained from each cdna using high-throughput sequencing Technology Typical read length: bp depending on technology 16 8

9 Reads are aligned with the reference genome or transcriptome and classified as three types: exonic reads, junction reads and poly(a) end-reads. de novo assembly also possible for non-model organisms 17 These three types are used to generate a baseresolution expression profile for each gene Example: A yeast ORF with one intron 18 9

10 Un-replicated Experimental Design 1 biological replicate per treatment group Pros: Cheap, can be informative, prelim data Cons: Can only make inferences about the particular biological individuals, NOT the treatment groups Applications: Pilot studies (although can not assess variation), reference transcriptome assembly 19 Biological vs Technical Replicates Biological Replicates Multiple Unique Individuals/Samples Technical Replicates One Individual/Sample with some technical steps replicated Biological Variance > Technical Variance (typically) Biological replicates more useful as allows inferences about treatment 20 10

11 Sample Pooling Combining multiple samples / individuals / tissues during preparation into a single sample for assay. Pros: Necessary when not enough material per individual for assay. Cons: Measure variability between individuals is lost. A bad sample can bias pooled sample. 21 Multiplexing Each sample is indexed and combined into one pooled sample. Indexing allows one to identify reads for each subject Pros: Removes technical variation as source of confounding; cost effective Cons: Reduces depth or coverage per sample Lane Flow cell 22 11

Comparison of two designs for testing differential expression between treatments A

Auer P L, and Doerge R W Genetics 2010;185:405-416 Copyright 2010 by the Genetics

/ G C stands for coverage L is the read length N is the number of reads G is the

12 Comparison of two designs for testing differential expression between treatments A and B. Treatment A is denoted by red tones and treatment B by blue tones. Auer P L, and Doerge R W Genetics 2010;185: Copyright 2010 by the Genetics Society of America Coverage Estimation Lander/Waterman equation for coverage C = LN / G C stands for coverage L is the read length N is the number of reads G is the haploid genome length verage_calculator.ilmn 12

What Coverage is needed? http://metalhelix.github.

More reads needed for alternative splicing / fusions Less reads needed for DE gene expression studies 15x is recommended for standard transcriptome studies Tarazone, et

13 What Coverage is needed? The amount of sequencing needed for a given sample is determined by the goals of the experiment and the nature of the RNA sample. More reads needed for alternative splicing / fusions Less reads needed for DE gene expression studies 15x is recommended for standard transcriptome studies Tarazone, et al (2011) Genome Research. Differential expression in RNA seq: A matter of depth 25 Coverage and Depth Number of detected genes (coverage) and costs increase with sequence depth (number of analyzed read) Calculation of coverage is less straightforward in transcriptome analysis (transcription activity varies) 26 13

14 ENCODE Standards DE_RNAseq_Standards_V1.0.pdf DE testing require only modest depths of sequencing: 30M pairend reads of length > 30NT, of which 20 25M are mappable to the genome or known transcriptome Experiments whose purpose is discovery of novel transcribed elements and strong quantification of known transcript isoforms requires more extensive sequencing. Experiments should be performed with two or more biological replicates, unless there is a compelling reason why this is impractical or wasteful. A biological replicate is defined as an independent growth of cells/tissue and subsequent analysis. Technical replicates not required, except to evaluate cases where biological variability is abnormally high. 27 Questions? 28 14

15 Example Workflow Brian T. Wilhelm, Josette Renée Landry. RNA Seq quantitative measurement of expression through massively parallel RNA sequencing. Methods Volume 48, Issue

Tuxedo Suite RNA-Seq Pipeline Sequencing Read alignment Transcript compilation Gene identification Differential expression RNA seq reads (2 x 100 bp) Bowtie/TopHat alignment (genome) Cufflinks

16 Tuxedo Suite RNA-Seq Pipeline Sequencing Read alignment Transcript compilation Gene identification Differential expression RNA seq reads (2 x 100 bp) Bowtie/TopHat alignment (genome) Cufflinks Cufflinks (cuffcompare) Cuffdiff (A:B comparison) Raw sequence data (.fastq files) Reference genome (.fa file) Gene annotation (.gtf file) CummRbund Inputs Visualization 31 RNA-Seq - Bioinformatics challenges (I): Storing, retrieving and processing of large amounts of data Base calling Quality analysis for bases and reads => FastQ files Mapping/aligning RNA-Seq reads (Alternative: assemble contigs and align them to genome) Multiple alignment possible for some reads Sequencing errors and polymorphisms =>SAM/BAM files 16

RNA-Seq - Bioinformatics challenges (II): Exon junctions and poly(a) ends Identification of poly(a) -> long stretches of A(T) at end of reads Splice sites: Specific sequence context: CT AG

17 RNA-Seq - Bioinformatics challenges (II): Exon junctions and poly(a) ends Identification of poly(a) -> long stretches of A(T) at end of reads Splice sites: Specific sequence context: CT AG dinucleotides Low expression for intronic regions Known or predicted splice sites Detection of new sites (e.g. via split read mapping) Overlapping genes RNA editing Secondary structure of transcripts Quantification of expression signals Mapping A multiread is a read that maps equally well to many reference sequences. read: AGTCGACTAGCTATTAGCATG 17

18 Read mapping vs. de novo assembly Options: Align and then assemble Assemble and then align Good reference No reference genome Align to: Genome transcriptome Haas and Zody, Nature Biotechnology 28, (2010) Genomic vs Transcript Mapping of Reads Exon A? Exon B???? Exon C????? Exon D Genome level mapping Transcript level mapping Exon A Exon B Exon C Exon D Mapping of reads at genome or transcript level 18

19 Advantages: Genomic Mapping Less likely to have multireads across different isoforms. One can get a sense of the coverage across exons. Disadvantages: It s a bit involved to estimate isoforms expression. Needs an (annotated) genome! (i.e. not great for non-model organisms) Transcriptome Mapping Advantages: Transcript-level expression Slightly easier to do. Disadvantages: Multiple isoforms can share an exon; can get multireads. Requires annotation to wrap to gene-level counts 19

RNA-Seq Quality Control Quality Control is Important to make sure experiment/data is valid Percentage of reads properly mapped / uniquely mapped 5 or 3 bias Per base sequence quality Per sequence GC

20 RNA-Seq Quality Control Quality Control is Important to make sure experiment/data is valid Percentage of reads properly mapped / uniquely mapped 5 or 3 bias Per base sequence quality Per sequence GC content Sequence length Duplication levels Coverage (reads per base) Alignment of Reads Alignment using bowtie algorithm: Not more than 2 mismatches per read allowed Reads with multiple alignment discarded Read longer than 35 bp truncated to 35 bp Overlapping of alignment of reads with gene footprint from middle position of read 20

21 TopHat Pipeline: Splice Junctions Reads are mapped against a reference genome, and those reads that do not map are set aside. An initial consensus of mapped regions is computed by Maq. Sequences flanking potential donor/acceptor splice sites within neighboring regions are joined to form potential splice junctions. The initially unmapped (IUM) reads are indexed and aligned to these splice junction sequences. Dataset after Aligning Reads Gene Treatment 1 Treatment Total

22 Differential Expression (DE) Analysis We would like to test whether the proportion of reads aligning to gene 1 tends to be different for experimental units that received treatment 1 than for experimental units that received treatment out of out of out of vs. 13 out of out of out of Example 1: Need for Normalization Every gene in B is expressed in A at the same level. G = number of genes expressed in B A also contains a set of G genes that are expressed but not expressed in B. Thus, A has 2*G expressed genes and its RNA production is twice the size of sample B. Each sample is sequenced at approx. the same depth Without adjustment, a gene expressed in A and B will have ½ the number of reads as B, since the # reads is spread over twice as many genes. The normalization factor would be to adjust sample A by factor of 2. 22

23 Example 2: Need for Normalization Suppose you multiplex 4 samples to lane 1 and 2 samples to lane 2 of a flowcell. The 4 samples in lane 1 will have lower number of reads (counts) as compared to 2 samples in lane 2. Need to account for the total number of reads per sample (library size) Measure for expression: FPKM and RPKM Longer transcripts, more fragments/reads FPKM/RPKM measure average pair coverage per transcript FPKM: Fragments Per Kilobase per Million RPKM: Reads Per Kilobase per Million Paired-end RNA-Seq experiments produce two reads per fragment. Both reads may not be mappable. If we were to count reads rather than fragments, we might double-count some fragments but not others, leading to a skewed expression value. Thus, FPKM is calculated by counting fragments, not reads. If single-end experiment, FPKM = RPKM 23

. L: Length of feature (in kb) N: Total number of mappable reads (in millions) Distributions used for Modeling

24 RPKM RPKM: Reads Per Kilobase per Million mapped reads RPKM = C/(L x N) C: Number of mappable reads on a feature, such as an exon or transcript.. L: Length of feature (in kb) N: Total number of mappable reads (in millions) Distributions used for Modeling Count Data count of reads for subject on treatment for gene i = 1 N and g = 1,,G ~, 0! Observed in RNA-seq data that Over-dispersed Poisson (a.k.a. Negative Binomial or Poisson-Gamma Distribution) 24

25 Empirical Assessment of Over-Dispersion of NGS Counts Poisson Dist. Mean = Var Distributions used for Modeling Count Data Technical Variation follows a Poisson Distribution Biological Variation follows a Negative Binomial Distribution Let ~, library size for sample i. e., total number of reads dispersion parameter for gene represents the coefficient of variation of biological variation between the samples (able to separate biological from technical variation) =0 reduces to Poisson( ) and V (1+ ) DE is based on parameter Model used in edger 25

26 edger Package and Methods edger (Robinson, McCarthy, Smyth; 2010): models count data using an over-dispersed Poisson model estimates the gene-wise dispersions by conditional maximum likelihood, conditioning on the total count for that gene(smyth and Verbyla, 1996). Empirical Bayes (EB) procedure is used to shrink the dispersions towards a consensus value (i.e., borrowing information) (Robinson and Smyth, 2007). DE is assessed using an exact test analogous to Fisher s exact test, but adapted for over-dispersed data (Robinson and Smyth, 2008). Similar in idea to limma (Smyth, 2004) Other Methods/Programs for DE Analysis DESeq, BaySeq, Cuffdiff, SAMseq and many others Each has pros and cons with different assumptions. No single method will be optimal under all circumstances, and hence the method of choice in a particular situation depends on the experimental conditions. Suggest running more than one method to look for sensitivity of results. 26

27 Following DE analysis Visualization of results Volcano plot (FC vs p-value) Multiple Testing Correction FDR q-values Pathway Analysis IPA Network Analysis Validation Studies Technical validation of sequence data Confirm/replicate association results 27

28 Questions? 28

RNA-Sequencing analysis

RNA-Sequencing analysis Markus Kreuz 25. 04. 2012 Institut für Medizinische Informatik, Statistik und Epidemiologie Content: Biological background Overview transcriptomics RNA-Seq RNA-Seq technology Challenges