Sequence Analysis 2RNA-Seq

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1 Sequence Analysis 2RNA-Seq Lecture 10 2/21/2018 Instructor : Kritika Karri kkarri@bu.edu

2 Transcriptome Entire set of RNA transcripts in a given cell for a specific developmental stage or physiological condition to study functional elements of the genome understanding mechanisms of development and disease. Microarray used for large-scale RNA-level studies to identify DE genes between conditions. BUT hybridization- based nature limits the ability to catalog and quantify RNA molecules expressed under various conditions. 2

What is RNA-Seq Analysis Transcriptome sequencing (RNA-seq) by sequencing of cdna. RNA-seq produces millions of sequences from complex RNA samples.

3 What is RNA-Seq Analysis Transcriptome sequencing (RNA-seq) by sequencing of cdna. RNA-seq produces millions of sequences from complex RNA samples. With this powerful approach, you can: Measure gene expression. Discover and annotate complete transcripts. Characterize alternative splicing and polyadenylation. 3

4 Applications of RNA-Seq Functional studies Drug treated vs. untreated cell line or Wild type (WT) versus knock-out (KO) mice Predicting transcript sequence from genome sequence is difficult Some molecular features can only be observed at the RNA level Alternative isoforms, fusion transcripts, RNA editing, lincrna discovery. Interpreting mutations that do not have an obvious effect on protein sequence Regulatory mutations that affect what mrna isoform is expressed and how much 4

RNA-Seq vs Microarray Unbiased detection of novel transcripts: Identify novel transcripts and splicing events With microarrays, limited to the probes on the chip Low background noise Large dynamic

5 RNA-Seq vs Microarray Unbiased detection of novel transcripts: Identify novel transcripts and splicing events With microarrays, limited to the probes on the chip Low background noise Large dynamic range RNA-Seq technology offers increased specificity and sensitivity, for enhanced detection of genes, transcripts, and differential expression. Easier detection of rare or low-abundance transcripts. 5

6 RNA Seq Experimental Protocol Overview 6

7 Preparing a RNA-Seq Library 7

8 Sequencing Library - Illumina Sequencing RAW DATA: Reads in fastq.gz 8

9 Design Concepts Single vs Paired End Reads: Concept already discussed in lecture 5-2nd Gen Sequencing!! Ideal Read Length Gene Expression: short single reads (50-75bp) Novel Transcriptome Assembly and annotation project: Longer paired end reads ( 2x 75 bp) Small RNA Analysis : Usually 50 bp read covers the entire sequence. Stranded vs Unstranded Replicates At least three Biological Replicate min for DE Analysis. 9

10 Design Concepts - Stranded vs unstranded stranded RNAseq can distinguish whether the reads are derived from forward- or reverse-encoded transcripts. 10

11 Overview of RNA Seq Analysis RNA seq data can be used to address a variety of biological problems. The analysis, tools, and methods can different depending on the problem of study. Useful Review for different RNA Seq Tools : w-of-rna-seq-data-analysis-tools/ 11

12 12

13 Data Quality Assessment: Trimming Adaptor Trimming: May increase mapping rates Absolutely essential for small RNA Improves de novo assemblies Trim Galore! uses the first 13 bp of Illumina standard adapters ('AGATCGGAAGAGC') by default (suitable for both ends of paired-end libraries), but accepts other adapter sequence, too Quality Trimming: May increase the mapping rates Loss of information Lots of software doing either of these or both. E g Cutadapt, Trim Galore!, PRINSEQ, Trimmomatic, Sickle/Scythe, FASTX Toolkit, etc. 13

RNA Seq Specific QC Several intrinsic biases and limitations including nucleotide composition bias, GC bias and ribosomal contamination (fig) can be introduced to RNA-seq data of clinical samples

14 RNA Seq Specific QC Several intrinsic biases and limitations including nucleotide composition bias, GC bias and ribosomal contamination (fig) can be introduced to RNA-seq data of clinical samples with low quality or quantity. RSeQC provides metrics containing: sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity, and read distribution over the genome structure. sequencing depth determines if current RNA-seq data is suitable for expression profiling, alternative splicing analysis, novel isoform identification, and transcriptome reconstruction. Other tools : RNA-SeQC, Qualimap2,etc. 14

Design Choice: Ribosomal RNA Depletion 95% of RNA molecules in the cell are ribosomal RNA (rrna) These molecules are useless for transcriptomics Must deplete rrna species to capture other types of

15 Design Choice: Ribosomal RNA Depletion 95% of RNA molecules in the cell are ribosomal RNA (rrna) These molecules are useless for transcriptomics Must deplete rrna species to capture other types of RNA transcripts Two strategies to select for non-rrna species: poly-a selection: capture RNA molecules with 3 poly-a tail Ribo-depletion: cdna probes for specific rrna sequences captured by special streptavidin-coated beads 15

16 Ribosomal contamination - SortMeRNA SortMeRNA is a program tool for filtering ribosomal RNA from metatranscriptomic data. 16

17 RNASeq Analysis: Transcriptome Assembly 17

18 Transcript Reconstruction Transcript Reconstruction: The splices exons information generated from read alignment step is used to build transcript models. De novo Genome Guided Cufflinks: assembles the alignments into a parsimonious set of transcripts and estimates the relative abundances of these transcripts. Stringtie: Assembles transcripts from spliced read alignments produced by tools such as STAR, TopHat, or HISAT and simultaneously estimates their abundances using counts of reads assigned to each transcript. 18

19 Tasks Related to RNA-Seq Assembly: Given: RNA-Seq reads (and possibly a genome sequence) Do: Reconstruct full-length transcript sequences from the reads Quantification: Given: RNA-Seq reads and transcript sequences Do: Estimate the relative abundances of transcripts ( gene expression ) Differential expression: Given: RNA-Seq reads from two different samples and transcript sequences Do: Predict which transcripts have different abundances between two samples 19

20 de novo transcriptome reconstruction (I) When no reference genome is available Finding features which are not on the reference genome Programs: Trinity, Trans-ABySS, Velvet Oases 20

21 de novo transcriptome reconstruction (II) 21

22 Genome Guided Transcriptome Reconstruction I 22

23 Genome Guided Transcriptome Reconstruction II Reference genome is available with or without annotation Mapping programs: TopHat, STAR etc Transcriptome reconstruction: Cufflinks 23

24 RNASeq Analysis: Gene Expression Quantification 24

25 Transcript Quantification Core concept: the # of reads mapping to a gene is proportional to the transcript abundance of that gene Example: Gene A = 5, Gene B = 10, Gene C = 10 reads Gene A is about half as abundant Gene B Gene A and Gene C have about the same abundance Why? Gene A Gene B Gene C 25

Bag Of Fragments Analogy Reads are samples drawn from the distribution of all RNA fragments Metaphorical bag All RNA Fragments (billions and billions) Drawn in proportion to frequency High abundance

26 Bag Of Fragments Analogy Reads are samples drawn from the distribution of all RNA fragments Metaphorical bag All RNA Fragments (billions and billions) Drawn in proportion to frequency High abundance transcripts drawn frequently Low abundance transcripts might not be drawn at all (black read) More reads sequenced more chance to draw low abundance transcripts Absence of evidence is not evidence of absence! 26

27 Two main quantification strategies Align + count Genome Reference FASTQ Pseudo-align + estimate Transcriptome Reference FASTQ Align Pseudo-align Count Estimate Abundance Gene Annotation Raw Gene Counts Estimated Abundance (Counts) 27

28 Transcript Quantification is like fishing in the dark late!! Transcript Quantification attempts to estimate expression levels of individuals transcripts. This is performed by assigning RNAseq reads to transcripts, counting, and normalization. 28

genome + junction database Junction database needs to be tailored

29 Align + Count: Which Mapping Strategy to use? Depends on read length < 50 bp reads Use aligner like BWA and a genome + junction database Junction database needs to be tailored to read length > 50 bp reads Spliced aligner such as Bowtie/TopHat, STAR, HISAT, etc. 29

30 Align + Count: Read Mapping with Reference Genome Tophat: Analyzes the mapping results to identify splice junctions between exons. Mapped reads are separated into two categories: those that map initially unmapped (IUM). "Piles" of reads representing potential exons are extended in search of potential donor/acceptor splice sites and potential splice junctions are reconstructed Spliced Transcripts Alignment to a Reference (STAR) : maps reads using uncompressed suffix array. It operates in two stages. I stage : it performs seed search II stage: stitches maximum mappable prefix (MMPs) to generate read-level alignments. Requires at least 30 Gb RAM to align to the human or mouse genomes. HISAT aligns RNA-seq reads to a genome and discovers transcript splice sites. faster than TopHat2 Requiring less computer memory than STAR. 30

31 Align + Count: Counting Strategies Intersect aligned reads with a reference annotation (GTF) Count the number of reads per feature (e.g. gene exons) Reads mapping to multiple features (multimappers) might be skipped Packages: htseq-count subread htseq-count strategies 31

32 Pseudo-Alignment Based Quantification Some tools perform lightweight alignment of RNAseq reads against existing transcriptome sequences. quickly distribute the reads across transcripts they likely originate from without worrying too much about producing high quality alignments. Pros: entire procedure can be performed very quickly. Cons: require high quality transcriptome as input not a problem for humans or mice is a problem if we have less studied species Kallisto: uses a pseudo-alignment concept to determine the compatibility of reads with targets. Other tools: Sailfish, Salmon 32

33 Comparing Abundance Across Samples Would like to compare counts (or estimated abundance) across sets of samples Every sample will have a different # of reads, termed library size Raw counts must be normalized across samples so that they are comparable Three basic strategies: Library size normalization: divide each sample count by corresponding library size Distribution adjustment: shift count distribution for each sample based on consistent genes across samples FPKM: divide by library size and each gene s length 33

Normalisation Finite Reads In perfect world

differentially expressed can cause lowly

34 Normalisation Finite Reads In perfect world all transcripts are counted In real world, More reads taken up by highly expressed genes means less reads available for lowly expressed genes. Highly expressed genes that are differentially expressed can cause lowly expressed genes that are not actually differentially expressed to appear that way. 34

35 Raw Read Counts Counting number of reads/fragments falling with exonic regions of a gene. Example: HTseq-count. The same fragment count yet different expression levels. 35

36 Normalizing/correcting for feature length and library size 36

37 RPKM/FPKM The statistical methods calculate the expression level of each transcript. The gene expression can then be obtained by simply summing expression levels of its isoforms Question: Example: 1kb transcript with 2000 mapped reads in a sample of 10 million reads (out of which 8 million reads can be mapped) will have RPKM? 37

38 TPM: Transcripts per Million TPM is the number of transcripts you would have seen of type, given the abundances of the other transcripts in your sample 38

39 Normalization Summary with Goals R/FPKM: (Mortazavi et al. 2008) Correct for: differences in sequencing depth and transcript length Aiming to: compare a gene across samples and diff genes within sample TMM: (Robinson and Oshlack 2010) Correct for: differences in transcript pool composition; extreme outliers Aiming to: provide better across-sample comparability TPM: (Li et al 2010, Wagner et al 2012) Correct for: transcript length distribution in RNA pool Aiming to: provide better across-sample comparability Limma voom (logcpm): (Lawet al 2013) Aiming to: stabilize variance; remove dependence of variance on the mean Relative Log Expression(RLE) normalization (RLE) implemented in the DESeq2 package 39

40 RNASeq Analysis: Differential Expression 40

41 Gene Variation Experiment The goal of differential expression analysis (DE) is to find gene (DGE) or transcript (DTE) differences between conditions, developmental stages, treatments etc. In particular DE has two goals: Estimate the magnitude of expression differences; Estimate the significance of expression differences. 41

42 Sources of variation Accurately determining the variation requires many biological samples (replicates). Unfortunately in most case we only have two or three replicates. Other methods are needed to approximate/model the variation. 42

43 Differential Expression Analysis Cuffdiff Genome annotation based FPKM values Numerical method for finding the maximum likelihood optimum *Obsolete EdgeR Complex experimental designs using generalised linear model (GLM) Negative binomial distribution, similar to DESeq2 DESeq Differential gene expression analysis based on the negative binomial distribution Ballgown visualize the transcript assembly on a isoform level extract abundance estimates for exons, introns, transcripts or genes perform linear model based differential expression analyses. 43

Obtain a list of significant DE Genes Typical RNA-Seq dataset will have counts for ~20k genes Adjusted p-values account for the chance that gene counts are different across samples

44 Obtain a list of significant DE Genes Typical RNA-Seq dataset will have counts for ~20k genes Adjusted p-values account for the chance that gene counts are different across samples by chance All tested genes have: p-value Effect size (test statistic or fold change) Significant DE genes have an adjusted p-value less than a given threshold (e.g. p-adj < 0.05) 44

pathways. DAVID : https://david.ncifcrf.gov/home.

45 What can we do with DE genes? Pathway Enrichment: Utilises the enriched list of genes and see their enrichment across different functional pathways. DAVID : p CLueGO: Cytoscape plug-in integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways creates a functionally organized GO/pathway term network. can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms 45

46 Clustering Heatmaps for DEG for Different Conditions 46

47 Other Application of RNA-Seq LincRNA discovery: Long non coding RNA Using the cufflinks and cuffmerge program for transcriptome assembly. The assembled transcriptome file can be used to detect lincrna. RNA editing is a molecular process through which some cells can make discrete changes to specific nucleotide sequences within a RNA molecule after it has been generated by RNA polymerase. Many tools leverage RNA seq dataset to predict RNA editing sites 47

48 Challenges The reads are much shorter than the transcripts from which they are derived. Tasks with RNA-Seq data thus require handling hidden information: which gene/isoform gave rise to a given read. Sample : Purity, ribosomal contiminal, quality etc. 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: 10^5 10^7 orders of magnitude Since RNA sequencing works by random sampling, a small fraction of highly expressed genes may consume the majority of reads Ribosomal and mitochondrial genes 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) 48

Transcriptome analysis

Statistical Bioinformatics: Transcriptome analysis Stefan Seemann seemann@rth.dk University of Copenhagen April 11th 2018 Outline: a) How to assess the quality of sequencing reads? b) How to normalize