1. High throughput expression analysis using RNA sequencing (RNAseq)
May 19, 2016

2. Lecture objectives
Theory and practice of RNA sequencing (RNA-seq) analysis
Rationale for sequencing RNA
Challenges specific to RNA-seq
Types of libraries for RNA-seq
Sequencing coverage (depth)
Basic themes of RNA-seq analysis work flows
Terminology for RNA-seq
Steps in RNAseq analysis
Downstream interpretation of expression and differential estimates

3. Gene expression

4. RNA sequencing Isolate RNAs
Generate cDNA, fragment, size select, add linkers
Samples of interest
Condition 1
(normal colon)
Condition 2
(colon tumor)
Sequence ends
Map to genome, transcriptome, and predicted exon junctions
100s of millions of paired reads
10s of billions bases of sequence
Downstream analysis

5. Before you start: What is your experimental question?
Is a global expression experiment the best approach?
What is your experimental model?
Is global expression technically feasible?
What is your budget?
$1200 minimum for 3 replicates, 2 conditions on Ion Torrent Proton

6. Common analysis goals of RNA-Seq analysis
Gene expression and differential expression
Alternative expression analysis
Transcript discovery and annotation
Allele specific expression
Relating to SNPs or mutations
Mutation discovery
Fusion detection
RNA editing
miRNA & other non-coding RNA detection/differences

7. Why sequence RNA (versus DNA)?
Functional studies
Genome may be constant but an experimental condition has a pronounced effect on gene expression
e.g. Drug treated vs. untreated cell line
e.g. Wild type versus knock out mice
Some molecular features can only be observed at the RNA level
Alternative isoforms, fusion transcripts, RNA editing
Predicting all possible transcript sequences from genome sequence is difficult
Alternative splicing, RNA editing, etc.

8. 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, exonic/intronic splicing motifs, etc.
Prioritizing protein coding somatic mutations (often heterozygous)
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 (haploinsufficiency)
If the mutant allele itself is expressed, this might suggest a candidate drug target

9. Challenges
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
105 – 107 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 most highly expressed
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)

10. Challenges, cont. Experimental model:
Mammalian, eukaryote, bacteria or virus?
Patient derived tissue or cells?
Sufficient sample available for enough biological replicates to achieve statistical significance?
Three biological replicates for cultured cells
Three biological replicates for inbred mice, for each condition
Human samples?
As many as possible, assuming that you can identify reasonable controls

11. Biological versus technical replicates

12. Variability of RNA-seq libraries
The relative log expression should be constant across the libraries
Libraries from different conditions should cluster together
Reference: Risso D. et.al. Nature Biotech. (2014) 32:

13. RNA quality is key to success
Garbage in…garbage out
Amount of RNA influences which type of library you can construct
Quality of the RNA determines if the library construction will work well enough to give you usable data

14. Purity of RNA Pure RNA absorbs strongly at 260 nm
Ratio of absorbance 260/280 used to assess purity
Ratio = 2.1 if pure ( acceptable)
Ratio depends on pH if there are protein contaminants
A260 remains constant, A280 changes if proteins are present
Ratio 260/230 should also be close to 2
If <2, proteins, guanidinium isothiocyanate or phenol may be contaminants
Always take a scanning measurement ( nm)
Reference:

15. RNA purity, cont.
The core facility will check your RNA quality using a Agilent Bioanalyzer
Lab on a chip to perform capillary electrophoresis with a fluorescent dye that binds to RNA
Determines RNA concentration & integrity
Works with ng quantities of RNA (50 – 300 ng/ul)
QC determined by 28S/18S ratio

16. MW of 28S RNA is ~2.5x more than 18S RNA (mammalian)
So a 28S/18S ratio of 2 is considered ideal

17. Agilent example / interpretation
RNA quality assessed by Agilent Bioanalyzer
“RIN” = RNA integrity number
0 (bad) to 10 (good)
RIN = 6.0
RIN = 10

18. Enrichment of desired RNA
mRNA makes up 1-3% of total RNA
Ribosomal RNA >80%, with majority of that 18S/28S RNA
polyA selection
Not species specific
Works reasonably well for gene expression analyses
Ribosomal depletion (Ribo-Minus)
Based on oligonucleotide probes that capture rRNA and remove it via binding to streptavadin-coated magnetic beads
Species specific, or at least specific to taxonomic groups
Necessary if interested in non-coding RNA analysis

19. Other RNA-seq library construction strategies
Size selection (before and/or after cDNA synthesis)
Small RNAs (microRNAs) vs. large RNAs?
Less bias with RNA fragmentation prior to cDNA synthesis
Linear amplification
insertion sites (viral, gene therapy)
Ref: Nature Protocols (2011) 6:1026
Strand-specific sequencing
Ref: Current Genomics (2013) 14:173
Exome capture
Cancer Genomes, 1000 genomes, NHLBI genome sequencing
Review on exome vs transcriptome for variant detection
Exp. Rev. Mol. Diag. (2012) 12:

20. Sequence coverage (depth) needed for RNA-seq
Question being asked of the data
Gene expression? Alternative expression? Mutation calling?
Tissue type
RNA preparation, quality of input RNA, library construction method, etc.
Sequencing type
read length, paired vs. unpaired, etc.
Computational approach and resources
Identify publications with similar goals
Pilot experiment
Work with your Core facility

21. Sequence coverage (depth)
C = LN/G
C = coverage
L = read length
N = number of reads (depends on sequencer; Illumina v3 189 million reads /lane)
G = haploid genome length
100 bp reads of human DNA on Illumina (single lane)
C = (100 bp)*(189 x 106)/(3 x 109 bp) = 6.3
That is, each base will be sequenced between 6 & 7 times
SNP discovery requires 10-30X coverage

22. Sequence coverage, cont.
100 bp reads of human transcriptome on Illumina v3
Human transcriptome: 50 million bp
C = (100 bp)*(189 x 106)/(50 x 106 bp) = 378
Only need 30X coverage for gene expression analysis
Put 10 RNAseq libraries on a single lane and still get enough coverage for the analysis
Use barcodes that distinguish different libraries for analysis

23. Ion Torrent Proton, sequence coverage
Each chip is capable of producing million reads of ~200 bp in length
C. neoformans transcriptome is 5 Mb
Sequenced 6 libraries on 1 chip
C = (200 bp)*(70 x 106)/(5 x 106 bp)*6 = 466
Sample name
Total reads
Total alignments
Aligned
Avg. coverage depth
Avg. length
0min_1.fastq
10,839,087
10,735,147
99.04%
86.78
90.4
0min_2.fastq
8,665,789
8,570,574
98.90%
47.09
89.02
0min_3.fastq
5,949,041
5,858,474
98.48%
38.23
85.39
15min_1.fastq
12,886,290
12,729,709
98.78%
56.97
77.33
15min_2.fastq
2,828,245
2,767,603
97.86%
24.2
88.37
15min_3.fastq
5,781,444
5,718,533
98.91%
31.76
77.05

24. How many replicates?
Statistical considerations suggest that you need at least 3 biological replicates to make valid comparisons
More complex samples need more replicates to increase the signal above the biological variability
Cost of experiment which is dependent on the depth of sequencing required and thus how many chips (Ion Torrent) or lanes (HiSeq) of sequencing you need
Plan this well in advance in consultation with the core personnel
Do as many replicates as is feasible and can afford

25. Steps in RNAseq workflow
Obtain raw data from sequencer
Align/assemble reads
Process alignment to quantify reads/gene feature
Conduct differential analysis
Summarize and visualize
Create gene lists, prioritize candidates for validation, etc.
Conduct gene enrichment or pathway analysis

26. Some nomenclature
Sequencing file types:
Fasta (sequence only) Fastq (contains quality scores)
>SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

27. Alignment files SAM – Sequence Alignment/MAP coordinate file
Tab-delimited ASCII file with all of the information needed for the alignment of the reads to reference
HWI-ST155_0544:7:2:7658:34048# Chr M * 0 0 GTAGAGGTAGGACCAACAAGGACCAAGTTTCCCTGTTCCAAC ghghhhhgcghh_hhhhhhhhhhhhhhhhghhhhhghhhhhh NM:i:0 NH:i:4 CC:Z:Chr10 CP:i:
HWI-ST155_0544:7:61:11040:141129#0 0 Chr M * CTTTTCTGGCGTAACTTGGTTCCCTTTAGTTTGGAACAGATA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhghhchfhhhh NM:i:0 NH:i:3 CC:Z:Chr10 CP:i:
HWI-ST155_0544:7:8:11214:130793#0 0 Chr M * CAAATAGTGGTTTGAAACCTATCAATCAAGTCACTTTCAAGT gggggggggggggggggeggdffffggggggggggggdggfc NM:i:0 NH:i:3 CC:Z:Chr10 CP:i:
BAM – Binary version of SAM
Machine readable, smaller more compact version
More commonly used by viewers and analysis programs

28. Reference guided RNAseq alignment
You have a sequenced genome and a file with the gene model annotations
Genome reference file is usually a fasta file with each chromosome as a separate entry
GTF = gene transcript file
GFF = gene feature file (GFF3)
The GTF/GFF chromosome name must match the chromosome name in the genome reference file

29. RNAseq alignment

30. Number of reads mapped in the sample
RPKM (FPKM)
Reads (fragments) per Kilobase Per Million
RPKM =
raw number of reads
exon length X
1,000,000
Number of reads mapped in the sample
In RNA-Seq, the relative expression of a transcript is proportional to the number of cDNA fragments that originate from it. However:
The number of fragments is biased towards larger genes
Total number of fragments is related to total library depth
Use of FPKM/RPKM normalizes for gene size and library depth

31. Alternatives to FPKM Raw read counts HTSeq (htseq-count)
Instead of calculating FPKM, simply assign reads/fragments to a defined set of genes/transcripts and determine “raw counts”
HTSeq (htseq-count)
Python code that converts aligned reads to counts
You give it an alignment file and the associated transcript file and it will output a list of counts by feature
FeatureCounts (part of Subread package)
BAM file + GTF file => # reads/feature

32. How to quantify reads per feature?

33. Counts vs FPKM(RPKM)?
Count files can be analyzed by a number of different R programs
EdgeR
DESeq2
NOISeq
More robust statistical methods for differential expression
Accommodates more sophisticated experimental designs with appropriate statistical tests

34. Gene expression differences
Once you have your data in RPKM or counts format, you can use a variety of tools to compare the different conditions and determine what genes are differentially expressed
In the original study, the analysis was done by GTAC using Cufflinks/CuffDiff
In the analysis to generate the lists you are using, I used an updated version of their alignment software, Subread to convert the alignment to counts/gene and DESeq2 for the differential analysis.
Analysis was done at Gene rather than transcript level

35. How does cuffdiff work?
Model variability in fragment count for each gene across replicates
Estimate fragment count for each isoform with a measure of uncertainty from ambiguously mapped reads
transcripts with more shared exons and few uniquely assigned fragments will have greater uncertainty
Combine estimates of uncertainty and cross-replicate variability under a negative binomial model of fragment count variability to estimate count variances for each transcript
These variance estimates are used during statistical testing to report significantly differentially expressed genes and transcripts.
A whole lot of fancy statistical modeling -> a list of expression values for each gene & differences between conditions
More replicates = more confidence

36. Multiple approaches advisable

37. Multiple testing correction
As more attributes are compared, it becomes more likely that the treatment and control groups will appear to differ on at least one attribute by random chance alone.
Well known from array studies
10,000s genes/transcripts
100,000s exons
With RNA-seq, even greater problem
All the complexity of the transcriptome
Almost infinite number of potential features
Genes, transcripts, exons, juntions, retained introns, microRNAs, lncRNAs, etc, etc
Bioconductor multtest

38. Lessons learned from microarray days
Hansen et al. “Sequencing Technology Does Not Eliminate Biological Variability.” Nature Biotechnology 29, no. 7 (2011): 572–573.
Power analysis for RNA-seq experiments
RNA-seq need for biological replicates
RNA-seq study design

39. Source of gene list
Alspach E, Flanagan KC, Luo X, Ruhland MK, Huang H, Pazolli E, Donlin MJ, Marsh T, Piwnica-Worms D, Monahan J, Novack DV, McAllister SS, Stewart SA. “p38MAPK plays a crucial role in stromal-mediated tumorigenesis.” Cancer Discov Jun;4(6):716-29
Performed RNA sequencing on young, senescent and p38MAPK-inhibited senescent human fibroblasts.
6 libraries:
2 young fibroblasts
2 senescent (late) fibroblasts
2 senescent (late) fibroblasts treated with p38MAPK inhibitor SB203580

40. Gene lists Differential expression (DE) was determined for:
senescent vs young (S vs Y)
p38MAPK-inhibited senescent vs young (p38I vs Y)
DE expressed genes were identified using the DESeq2 package
Statistical cut-off was p < 0.01
Genes DE in S vs Y but NOT in p38I vs Y are considered to be p38MAPK dependent

41. Today in lab Explore the gene list using EBI-Biomart Excel tutorial
Use Excel to compare S vs Y to p38K vs Y to identify those genes specific to S vs Y
S vs Y
p38I vs Y
p38MAPK dependent