Schedule change

• 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)

Galaxy server going down for maintenance on Thursday

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

4

ReverseTranscription

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

# 14

Illuminareads

Remove artifacts

AAA..., ...N...

Clip adapters(small RNA)

Pre-filter: low complexity

synthetic

Countand

discard

mappedAlign to the

genome

un-mapped

un-mapped

Re-align with different number of mismatchesetc

"Collapse" identical

reads

Assemble:contigs (exons)+ connectivity

mapped

Annotate

Filter out low confidence

contigs(singletons)

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

• 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

R

LexonRNA:

Genome:

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

C= the number of reads mapped onto the gene's exonsN= total number of reads in the experimentL= 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 statusENSG00000236743 31390 chr1 459655 461954 0 0 0 OKENSG00000248149 31391 chr1 465693 688071 787.12 731.009 843.232 OKENSG00000236679 31391 chr1 470906 471368 0 0 0 OKENSG00000231709 31391 chr1 521368 523833 0 0 0 OKENSG00000235146 31391 chr1 523008 530148 0 0 0 OKENSG00000239664 31391 chr1 529832 532878 0 0 0 OKENSG00000230021 31391 chr1 536815 659930 2.53932 0 5.72637 OKENSG00000229376 31391 chr1 657464 660287 0 0 0 OKENSG00000223659 31391 chr1 562756 564390 0 0 0 OKENSG00000225972 31391 chr1 564441 564813 96.9279 77.2375 116.618 OKENSG00000243329 31391 chr1 564878 564950 0 0 0 OKENSG00000240155 31391 chr1 564951 565019 0 0 0 OK

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 significantENSG00000000003 TSPAN6chrX:99883666-99894988 q1 q2 NOTEST 0 0 0 0 1 noENSG00000000005 TNMD chrX:99839798-99854882 q1 q2 NOTEST 0 0 0 0 1 noENSG00000000419 DPM1 chr20:49551403-49575092 q1 q2 NOTEST 15.0775 23.8627 0.459116 -1.39556 0.162848 noENSG00000000457 SCYL3 chr1:169631244-169863408 q1 q2 OK 32.5626 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