Schedule change
Galaxy server going down for maintenance on Thursday
• Day 2: AM - Introduction to RNA-Seq (and a touch of
miRNA-Seq)
• Day 2: PM - RNA-Seq practical (Tophat + Cuffdiff
pipeline on Galaxy)
• Day 3: AM – Introduction to Exome Sequencing and
Variant Discovery
• Day 3: PM - Exome sequence analysis practical (Galaxy)
Quick Recap
• NGS data production becoming commonplace
• Many applications -> research intent determines
technology platform choice
• High volume data BUT error prone
• FASTQ is accepted format standard
• Must assess quality scores before proceeding
• ‘Bad’ data can be rescued
Introduction to RNAseq
The Central Dogma of Molecular Biology
Reverse
Transcription
4
RNAseq Protocols
• cDNA, not RNA sequencing
• Types of libraries available:
– Total RNA sequencing (not advised)
– polyA+ RNA sequencing
– Small RNA sequencing (specific size range
targeted)
cDNA Synthesis
Genome-scale Applications
• Transcriptome analysis
• Identifying new transcribed regions
• Expression profiling
• Resequencing to find genetic polymorphisms:
– SNPs, micro-indels
– CNVs
– Question: Why even bother with exome sequencing
then?
Sequencing details
• Standard sequencing
–
–
–
–
polyA/total RNA
Size selection
Primers and adapters
Single- and paired-end sequencing
• Strand-specific sequencing
– still immature tech
– Sequencing only + or – strand
– Mostly paired-end
What about microarrays??!!!
• Assumes we know all
transcribed regions and
that spliceforms are not
important
• Cannot find anything
novel
• BUT may be the best
choice depending on
QUESTION
Arrays vs RNAseq (1)
• Correlation of fold change between arrays
and RNAseq is similar to correlation between
array platforms (0.73)
• Technical replicates almost identical
• Extra analysis: prediction of alternative
splicing, SNPs
• Low- and high-expressed genes do not
match
RNA-Seq promises/pitfalls
• can reveal in a single assay:
– new genes
– splice variants
– quantify genome-wide gene expression
• BUT
– Data is voluminous and complex
– Need scalable, fast and mathematically principled
analysis software and LOTS of computing resources
Experimental considerations
• Comparative conditions must make biological sense
• Biological replicates are always better than technical
ones
• Aim for at least 3 replicates per condition
• ISOLATE the target mRNA species you are after
• NOT looking for new transcripts can bias expression
estimates
Analysis strategies
• De novo assembly of transcripts:
+ re-constructs actual spliced transcripts
+ does not require genome sequence
easier to work post-transcriptional modifications
- requires huge computational resources (RAM)
- low sensitivity: hard to capture low abundance transcripts
• Alignment to the genome => Transcript assembly
+ computationally feasible
+ high sensitivity
+ easier to annotate using genomic annotations
- need to take special care of splice junctions
# 13
Basic analysis flowchart
Illumina
reads
Re-align
with different
number of
mismatches
etc
un-mapped
Remove
artifacts
AAA..., ...N...
Align
to the
genome
Clip adapters
(small RNA)
un-mapped
Pre-filter:
low complexity
synthetic
"Collapse"
identical
reads
mapped
Count
and
discard
mapped
Assemble:
contigs (exons)
+ connectivity
Filter out low
confidence
contigs
(singletons)
Annotate
# 14
Software
• Short reads aligners
•
Stampy, BWA, Novoalign, Bowtie, TOPHAT
• Data preprocessing
•
•
Fastx toolkit
samtools
• Expression studies
•
•
Cufflinks package
R packages (DESeq, edgeR, more…)
• Alternative splicing
•
•
Cufflinks
Augustus
The ‘Tuxedo’ protocol
• TOPHAT + CUFFLINKS
• TopHat aligns reads to genome and discovers
splice sites
• Cufflinks predicts transcripts present in dataset
• Cuffdiff identifies differential expression
Very widely adopted suite
‘Tuxedo’ protocol limitations
• Uses shortread data - Illumina OR SOLiD
• Requires a sequenced genome
• No GUI
• Versions implemented in GALAXY are old(ish)
Read alignment with TopHat
Splice junctions
R
RNA:
Lexon
Genome:
• In humans, terminal exons are ~1kb long, and since mRNAs
are ~2kb,
~half of the reads should originate in initial and internal
exons
• Initial and internal exons are ~200b long
=> for 75-mer reads, ~20% of reads are supposed to cross
splice junctions
Splice junctions strategies
• Create a splice junctions database joining together donors
and acceptors
• Typically, use known (annotated) splice junctions or known
splice sites
• TopHat: uses putative exons from mapped reads, database
is made of canonical splice sites around putative exons
Read alignment with TopHat (2)
• Uses BOWTIE aligner to align reads to genome
• BOWTIE cannot deal with large gaps (introns)
• Tophat segments reads that remain unaligned
• Smaller segments mostly end up aligning
Read alignment with TopHat (3)
• When there is a large gap between segments of
same read -> probable INTRON
• Tophat uses this to build an index of probable
splice sites
• Allows accurate measurement of spliceform
expression
• Possibility of detecting gene fusion events
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
Cufflinks: Transcript assembly
• Assembles individual transcripts based on aligned
reads
• Infers likely spliceforms of each gene
• Builds ‘transfrags’
• The smallest number of spliceforms that can be
explained by the data
• NOTE: assembly errors do occur -> sequencing depth
helps
Cufflinks: Transcript assembly (2)
• Quantifies expression level of each transfrag
• Filters out those likely to be premature
terminations, non-mature mRNAs, etc
Cuffmerge
• Merges transfrags into transcripts where
appropriate
• Also performs a reference based assembly of
transcripts using known transcripts
• Produces single annotation file which aids
downstream analysis
Cuffdiff: Differential expression
• Calculates expression level in two or more
samples
• Expression level relates to read abundance
• Because of bias sources, cuffdiff tries to model
the variance in its significance calculation
What else is important?
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
FPKM = 10 ´
NL
9
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.
Cufflinks (Expression analysis)
gene_id bundle_id
chr left right FPKM FPKM_conf_lo FPKM_conf_hi
ENSG00000236743 31390 chr1 459655 461954 0
0
0
OK
ENSG00000248149 31391 chr1 465693 688071 787.12 731.009 843.232 OK
ENSG00000236679 31391 chr1 470906 471368 0
0
0
OK
ENSG00000231709 31391 chr1 521368 523833 0
0
0
OK
ENSG00000235146 31391 chr1 523008 530148 0
0
0
OK
ENSG00000239664 31391 chr1 529832 532878 0
0
0
OK
ENSG00000230021 31391 chr1 536815 659930 2.53932 0
5.72637 OK
ENSG00000229376 31391 chr1 657464 660287 0
0
0
OK
ENSG00000223659 31391 chr1 562756 564390 0
0
0
OK
ENSG00000225972 31391 chr1 564441 564813 96.9279 77.2375 116.618 OK
ENSG00000243329 31391 chr1 564878 564950 0
0
0
OK
ENSG00000240155 31391 chr1 564951 565019 0
0
0
OK
status
Cuffdiff (differential expression)
• Pairwise or time series comparison
• Normal distribution of read counts
• Fisher’s test
test_id gene
locus
ENSG00000000003TSPAN6
ENSG00000000005TNMD
ENSG00000000419DPM1
ENSG00000000457SCYL3
sample_1
sample_2
chrX:99883666-99894988 q1
chrX:99839798-99854882 q1
chr20:49551403-49575092 q1
chr1:169631244-169863408 q1
status
q2
q2
q2
q2
value_1 value_2
NOTEST 0
NOTEST 0
NOTEST 15.0775
OK
32.5626
ln(fold_change) test_stat
p_value significant
0
0
0
1
no
0
0
0
1
no
23.8627 0.459116 -1.39556 0.162848 no
16.5208 -0.678541
15.8186 0
yes
Visualization: Genome Viewers
• Web based:
– UCSC Genome Browser (http://genome.ucsc.edu/)
• Standalone
– Integrated Genome Viewer
(http://www.broadinstitute.org/software/igv/)
RNAseq hands-on practical
(Galaxy)
• Data QC and trimming
• Aligning reads to reference genome
• Running CUFFLINKS and looking at some
transcripts using the UCSC genome browser
• Finding differentially expressed genes with
CUFFDIFF