RNA-Seq Data Analysis. I-Hsuan Lin

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1 RNA-Seq Data Analysis I-Hsuan Lin LSL Next-Generation Sequencing Workshop (Day 3) 19 Nov 2015

2 Transcriptome 2 The complete set of RNA species in a cell and their quantities Transcriptomics To catalogue all types of RNA transcripts, e.g. mrnas, noncoding RNAs and small RNAs To determine the structure of genes, such as start sites (alternative start sites), 5 and 3 ends, splicing events, and other post-transcriptional modifications To quantify the change in expression levels under different conditions

3 Frequent Applications of RNA-Seq 3 Abundance measurement of gene expression Identify differentially expression genes Identify alternatively spliced transcripts Predict novel transcripts Identify fusion genes

4 Advantages of RNA-Seq over Microarrays 4 No reference sequence needed Does not depend on genome annotation Microarrays are limited to the probes on the chip Low background noise Microarrays suffer from biases introduced during hybridization Quantify wider dynamic range of expression levels (depend on sequencing depth) mrnas abundance range from a few to >10,000 copies per cell >10 5 compared to 10 3 for microarrays High technical reproducibility RNA-Seq is more costly ($300-$1000/sample) than microarrays ($ /sample)

5 Complete View of Transcriptomics with NGS 5 A broad range of methods for transcriptomics with NGS have emerged over the past 10 years including mrna-seq, total RNA-Seq, small RNA-Seq and targeted RNA-Seq

6 Key RNA-Seq Methods 6 Methods mrna Sequencing Total RNA Sequencing Targeted RNA Sequencing Small RNA Sequencing Ribosome Profiling Ultra-Low-Input and Single-Cell RNA-Seq Description Detects known and novel transcripts and measures transcript abundance in the coding transcriptome Capture the broadest possible range of transcripts from both coding and noncoding RNA species Simultaneously measure the expression of thousands of transcripts of interest Targeted method for sequencing the complete range of small RNA and mirna species Deep sequencing of ribosome-protected mrna fragments to determine what proteins are being actively translated in a cell at a specific time point Accurate and highly sensitive characterization of gene expression from single cells or ultra-low-input samples

7 Paired-end Sequencing 7 Platform GAIIX HiSeq (v2) HiSeq (v3) SE Reads (millions) PE Reads (millions)

8 Stranded RNA-Seq 8 MCF-7 Long RNA-Seq from ENCODE/Cold Spring Harbor Lab

9 Depth of RNA-Seq (Large Genome) 9 Different transcripts are expressed at different levels Detecting rarely expressed genes often requires an increase in the depth of coverage Application Million Reads per Sample Read Type Expression profiling 10 ~ ~ 100 bp SE Alternative splicing 50 ~ bp PE Allele Specific Expression 50 ~ bp PE Rare Transcript or De Novo Assembly > bp PE

10 RNA-Seq Analysis Workflow 10 Hypothesis Raw Read 1 Raw Read Experimental Design RNA Purification QC of Raw Reads Read Alignment FASTQC Annotation-based Genome-guide assembly De novo assembly Single-end/Paired-end Un-stranded/Stranded Library Preparation Quantification Gene-level Transcript-level Differential Expression Testing Sequencing Biological Insight Visualization

11 FASTQ Format and Illumina Sequence Identifiers 11 Unique Instrument Name Flowcell ID Flowcell Lane Read # Read Filtered Index Sequence R R Run ID Tile 1:N:0:ACACAC NGGACATTTCTCTGATAATCAATGAGAACTGCATCCAACAGAGGGGGAACAAAATTTTCAGCGACCATCTGTGGATCATTGGATCGGCTCACCCAACCAGN 2:N:0:ACACAC TAATGGTGAAATGGTTACAAAGCAACCATTGATTAGAAGTATGCGAACTGTAAAAAGGGAAACTTTAAAGTTAATATCTGGTTGGGTGAGCCGATCCANNN + Line 1: Begins with a character and is followed by a sequence identifier Line 2: Raw sequence letters Line 3: A '+' character Line 4: Encodes the base quality values for the sequence in Line 2 X Y Control Number

12 12 QC with FASTQC

13 QC with FASTQC 13 Per base sequence quality Per sequence quality scores Adapter Content Overrepresented sequences Sequence Count Percentage Possible Source GTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGTTTTGA No Hit GTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGT No Hit TTTTGACCTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCA No Hit GTTTTGACCTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGC No Hit GCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGTTT No Hit BLAT Search RNA, 7SL, cytoplasmic 1 (from HGNC RN7SL1) RNA, 7SL, cytoplasmic 1 (from HGNC RN7SL1) RNA, 7SL, cytoplasmic 5, pseudogene (from HGNC RN7SL5P) RNA, 7SL, cytoplasmic 5, pseudogene (from HGNC RN7SL5P) RNA, 7SL, cytoplasmic 1 (from HGNC RN7SL1)

14 Read Alignment Strategies Model organisms with good quality genome build Alignment to a reference genome Allow quantification of known genes and transcripts Efficient computing Eliminate contaminating reads 2. Un-sequenced genomes De novo transcriptome assembly Higher number of reads are required to obtain a reliable and useful assembly

15 Read Alignment Strategies 15 Method Advantages Disadvantages Recommended Depth Genome-based Alignment to a reference genome Efficient computing Eliminate contaminating reads Very sensitive and can assemble transcripts of low abundance Can discover novel transcripts that are not annotated Requires good quality genome build De novo Assembly Not using a reference genome Genome sequence is not required. Good for unsequenced organisms Correct alignment of reads to known splice site not required Trans-spliced transcripts can be assembled More computational intense Sensitive to sequencing error ~ 10x > 30x

16 Quantification 16 No. of reads mapping to each transcript is linearly related to its abundance in the cell Infer expression levels 2 main methods union exon -based counting Such as HTSeq or featurecounts Prevents inference about the transcription of isoforms Mathematically incorrect to estimate gene abundances by adding up counts to their genomic region Transcript-based approach Such as BitSeq, express, IsoEM, RSEM, Sailfish, TIGAR2

17 Quantification 17

18 Isoform Expression Quantification 18

19 Differential Expression Testing 19 Using statistical test to evaluate if one gene is differentially expressed in one condition compare to the other(s) Many Bioconductor packages were available with different read count distribution assumptions Negative binomial distribution: DESeq, Cufflinks Bayesian methods for negative binomial distribution: edger, bayseq, BitSeq Empirical Bayesian approach: EBSeq Non-parametric: NOISeq

20 Differential Expression Testing 20 Package Input Replicates Normalization Version/Last Updated DESeq Raw counts No Library size edger Raw counts Yes bayseq Raw counts Yes NOISeq Raw or normalized counts No Library size TMM RLE Upperquartile Library size Quantile TMM Library size RPKM TMM Upperquartile v v v v TMM: trimmed mean of M RLE: relative log expression

21 Public Sources of RNA-Seq Data 21 Gene Expression Omnibus (GEO) Both microarray and sequencing data ArrayExpress Both microarray and sequencing data ENCODE: Encyclopedia of DNA Elements Sequence Read Archive (SRA) Sequencing data European Nucleotide Archive (ENA) Sequencing data DDBJ Sequence Read Archive (DRA) Sequencing data Public ENCODE Consortium data

22 Hands-on Exercises 22 Please use the following online guide

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