1. RNAseq Applications in Genome Studies
Alexander Kanapin, PhD
Wellcome Trust Centre for Human Genetics,
University of Oxford

2. RNAseq Protocols Next generation sequencing protocol
cDNA, not RNA sequencing
Types of libraries available:
Total RNA sequencing
polyA+ RNA sequencing
Small RNA sequencing
Special protocols:
DSN treatment
Ribosomal depletion

3. Genome Study Applications
transcriptome analysis
identifying new transcribed regions
expression profiling
Resequencing to find genetic polymorphisms:
SNPs, micro-indels
CNVs

4. cDNA Synthesis

5. Sequencing details Standard sequencing Strand-specific sequencing
polyA/total RNA
Size slection
Primers and adapters
Single- and paired-end sequencing
Strand-specific sequencing
Beta version
Sequencing only + or – strand
Mostly paired-end

6. Arrays vs RNAseq (1)
Correlation of fold change between arrays and RNAseq is similar to correlation between array platforms (0.73)
Technical replicates are almost identical, no need to run
Extra analysis: prediction of alternative splicing, SNPs
Low- and high-expressed genes do not match

7. Array vs RNAseq (2)

8. A bit of statistics Short reads distribution
Poisson
Negative binomial
Normal
Expression values normalization
FPKM
Normalized reads number
VST (variance stabilized transformation)
Differential expression analysis
Replicates vs non-replicates

9. Expression profiles/RNA abundance
Analysis Dataflow
Illumina Pipeline
(FASTQ)
Alignment (BAM)
FASTX Toolkit
(FASTQ/FASTA)
Expression profiles/RNA abundance
Splice variants
SNP analysis

10. Software Short reads aligners
Stampy, BWA, Novoalign, Bowtie,…
Data preprocessing (reads statistics, adapter clipping, formats conversion, read counters)
Fastx toolkit
Htseq
MISO
samtools
Expression studies
Cufflinks package
RSEQtools
R packages (DESeq, edgeR, baySeq, DEGseq, Genominator)
Alternative splicing
Cufflinks
Augustus
Commercial software
Partek
CLCBio

11. FASTQ: Sequence Data “FASTA with Qualities”
@HWI-EAS225:3:1:2:854#0/1 GGGGGGAAGTCGGCAAAATAGATCCGTAACTTCGGG +HWI-EAS225:3:1:2:854#0/1 GGGAAGATCTCAAAAACAGAAGTAAAACATCGAACG +HWI-EAS225:3:1:2:1595#0/1 a`abbbababbbabbbbbbabb`aaababab\aa_`

12. SAM(BAM): Alignment Data
Read ID
Bitwise flag
Chr
Pos
MapQ
CIGAR
Mate ref
Mate pos
Insert size
Sequence
Scores
Extra tags
S35_42763_4 X
255
18M *
CACACGATTCTCAAAGGT
IIIIIIIIIIIIIIIIII
XA:i:0

13. FPKM (RPKM): Expression Values
Fragments Reads Per Kilobase of exon model per Million mapped fragments
Nat Methods. 2008, Mapping and quantifying mammalian transcriptomes by RNA-Seq. Mortazavi A et al.
C= the number of reads mapped onto the gene's exons
N= total number of reads in the experiment
L= the sum of the exons in base pairs.

14. Cufflinks package http://cufflinks.cbcb.umd.edu/ Cufflinks:
Expression values calculation
Transcripts de novo assembly
Cuffcompare:
Transcripts comparison (de novo/genome annotation)
Cuffdiff:
Differential expression analysis

15. Cufflinks (Expression analysis)
gene_id bundle_id chr left right FPKM FPKM_conf_lo FPKM_conf_hi status
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK
ENSG chr OK

16. Cuffdiff (differential expression)
Pairwise or time series comparison
Normal distribution of read counts
Fisher’s test
test_id gene locus sample_1 sample_2 status value_1 value_2 ln(fold_change) test_stat p_value significant
ENSG TSPAN6 chrX: q1 q2 NOTEST no
ENSG TNMD chrX: q1 q2 NOTEST no
ENSG DPM1 chr20: q1 q2 NOTEST no
ENSG SCYL3 chr1: q1 q2 OK yes

17. Cufflinks: Alternative splicing
trans_id bundle_id chr left right FPKM FMI frac FPKM_conf_lo FPKM_conf_hi coverage length effective_length status
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK
ENST chr OK

18. R/bioconductor Packages
Based on raw read counts per gene/transcript/genome feature (miRNA)
Differential expression analysis
DESeq
Negative binomial distribution
baySeq
views/release/bioc/html/baySeq.html
Bayesian approach
Choice of Poisson and negative binomial distribution
edgeR
DEGSeq
Genominator …

19. DESeq: Variance estimation
SCV: the ratio of the variance at base level to the square of the base mean
Solid line: biological replicates noise
Dotted line: full variance scaled by size factors
Shot noise: dotted minus solid

20. DESeq: Differential Expressionid
B cells expression
IFG expression
log2FoldChange
pValue
ENSG
e-17
ENSG
e-13
ENSG
e-33
ENSG
e-07
ENSG
e-05
ENSG
e-13
ENSG
e-133
ENSG
e-10
ENSG
e-16
ENSG
e-30
ENSG
e-08
ENSG
e-08
ENSG
e-14
ENSG
e-40
ENSG
e-10
ENSG
e-33
ENSG
e-07
ENSG
e-06
ENSG
e-11
ENSG
e-06
ENSG
e-05
ENSG
e-07
ENSG
e-12
ENSG
e-12
ENSG
e-18
ENSG
e-06

21. Visualization: Genome Viewers
Web based:
Gbrowse (
UCSC Genome Browser (
Standalone
Integrated Genome Viewer (

22. IGV: Differential Expression Visualization

23. An Introduction to ChIP-Sequencing analysis
Linda Hughes

24. What is ChIP-Seq? Chromatin-Immunoprecipitation (ChIP)- Sequencing
ChIP - A technique of precipitating a protein antigen out of solution using an antibody that specifically binds to the protein.
Sequencing – A technique to determine the order of nucleotide bases in a molecule of DNA.
Used in combination to study the interactions between protein and DNA.

25. ChIP-Seq Applications
Enables the accurate profiling of
Transcription factor binding sites
Polymerases
Histone modification sites
DNA methylation

26. ChIP-Seq: The Basics

27. ChIP-Seq Analysis Pipeline
Sequencing
Base Calling
Read quality assessment
30-50 bp
Sequences
Genome
Alignment
Peak Calling
Enriched Regions
Visualisation with genome browser
Differential peaks
Motif Discovery
Combine with gene expression

28. ChIP-Seq: Genome Alignment
Several Aligners Available
BWA
NovoAlign
Bowtie
Currently the Sequencing analysis pipeline uses the Stampy as the default aligner for all sequencing.
All aligner output containing information about the mapping location and quality of the reads are out put in SAM format

29. ChIP-Seq Peak Calling
The main function of peak finding programs is to predict protein binding sites
First the programs must identify clusters (or peaks) of sequence tags
The peak finding programs must determine the number of sequence tags (peak height) that constitutes “significant” enrichment likely to represent a protein binding site

30. ChIP-Seq: Peak Calling
Several ChIP-seq peak calling tools Available
MACS
PICS
PeakSeq
Cisgenome
F-Seq

31. ChIP-Seq: Identification of Peaks
Several methods to identify peaks but they mainly fall into 2 categories:
Tag Density
Directional scoring
In the tag density method, the program searches for large clusters of overlapping sequence tags within a fixed width sliding window across the genome.
In directional scoring methods, the bimodal pattern in the strand-specific tag densities are used to identify protein binding sites.

33. ChIP-Seq: Determination of peak significance
To account for the background signal, many methods incorporate sequence data from a control dataset.
This is usually generated from fixed chromatin or DNA immunoprecipitated with a nonspecific antibody.
Calculate false discovery rate
account the background signal in ChIP-sequence tags
Assess the significance of predicted ChIP-seq peaks

34. ChIP-Seq: Determination of peak significance
More statistically sophisticated models developed to model the distribution of control sequence tags across the genome.
Used as a parameter to assess the significance of ChIP tag peaks
t-distribution
Poisson model
Hidden Markov model
Primarily used to assign each peak a significance metric such as a P-value FDR or posterior probability.

35. ChIP-Seq: Output
chr start end length summit tags *log10(pvalue) fold_enrichment FDR(%)
chr
chr
chr
chr
chr
chr
chr
chr
chr
chr
chr
chr
chr
chr

36. ChIP-Seq: Output A list of enriched locations Can be used:
In combination with RNA-Seq, to determine the biological function of transcription factors
Identify genes co-regulated by a common transcription factor
Identify common transcription factor binding motifs

37. ChIP-Seq: Need help? http://seqanswers.com/ Good for: Publications
Answering FAQ
Troubleshooting
Contacting the programs authors