Download 2012-06-14-EBI-plant-bioinf-course

Expression Analysis of RNA-seq Data
Manuel Corpas
Plant and Animal Genomes Project Leader
[email protected]
Generation
of Sequence
Mapping
Reads
Identification
of splice
junctions
Assembly of
Transcripts
Statistical Analysis
1 Summarization (by exon,
by transcript, by gene)
2 Normalization (within
sample and between
sample)
3 Differential expression
testing (poisson test,
negative binomial test)
The Tuxedo Tools
•
•
Developed by Institute of Genetic Medicine at Johns Hopkins University
/ University of California, Berkeley / Harvard University
157 pubmed citations
Tophat
Fast short read aligner (Bowtie)
Spliced read identification (Tophat)
Cufflinks package
Cufflinks – Transcript assembly
Cuffmerge – Merges multiple transcript assemblies
Cuffcompare – Compare transcript assemblies to
reference annotation
Cuffdiff – Identifies differentially expressed genes and
transcripts
CummeRbund
Visualisation of differential expression results
RNA-seq Experimental design
• Sequencing technology (Solid, Illumina)
– Hiseq 2000, 150 million read pairs per lane, 100bp
• Single end (SE) Paired end (PE), strand specific
– SE Quantification against known genes
– PE Novel transcripts, transcript level quantification
• Read length (50-100bp)
– Greater read length aids mapping accuracy, splice variant
assignment and identification of novel junctions
• Number of replicates
– often noted to have substantially less technical variability
– Biological replicates should be included (at least 3 and
preferably more)
• Sequencing depth
– Dependent on experimental aims
RNA-seq Experimental design
Toung et al. RNA-sequence analysis of human B-cells.
Genome Research (2011) .
Labaj et al. Characterization and improvement of RNA-Seq
precision in quantitative transcript expression profiling.
Bioinformatics (2011)
• Extrapolation of the sigmoid shape suggests 20 % of
transcripts not expressed
• First saturation effects set in at ~40 million read alignments
• ~240 Million reads achieve 84 % transcript recall
RNA-seq Experimental design
General guide
• Quantify expression of high-moderatly expressed known genes
• ~20 million mapped reads, PE, 2 x 50 bp
• Assess expression of alternative splice variants, novel transcripts, and
strong quantification including low copy transcripts
• in excess of 50 million reads, PE, 2 x 100 bp
Example
• Examine gene expression in 6 different conditions with 3 biological
replicates (18 samples)
• Multiplexing 6 samples per lane on 3 lanes of the HiSeq (50 bp PE)
• Generates ~25 M reads per sample
• Assuming ~80% of reads map/pass additional QC (20 M mapped
read per sample)
• Cost – 3 lanes (£978 x 3 ) 18 libraries (£ 105 x 18), total £4824
Step 1 – Preprocessing reads
• Sequence data provided as Fastq files
• QC analaysis – sequence quality, adapter contamination
(FASTQC)
• Quality trimming, adapter removal (FASTX, Prinseq,
Sickle)
Step 2 – Data sources
• Reads (Fastq, phred 33)
• Genomic reference (fasta TAIR10), or pre built Bowtie
index
• GTF/GFF file gene calls (TAIR10)
• http://tophat.cbcb.umd.edu/igenomes.html
Tuxedo Protocol
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
GTF +
Genome
CUFFLINKS (Transcript Assembly)
GTF
Alignments (BAM)
Assemblies (GTF)
CUFFMERGE (Final Transcript Assembly)
Assembly (GTF)
CUFFDIFF (Differential expression results)
Visualisation
(PDF)
CUMMERBUND (Expression Plots)
CUFFCOMPARE (Comparion to reference)
GTF
Step 3 Tuxedo Protocol - TOPHAT
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
Alignments (BAM)
•
•
Non-spliced reads mapped by bowtie
– Reads mapped directly to transcriptome sequence
Spliced reads identified by tophat
– Initial mapping used to build a database of spliced junctions
– Input reads split into smaller segments
• Coverage islands
• Paired end reads map to distinct regions
• Segments map in distinct regions
• Long reads >=75bp used to identify GT-AG, GC-AG and AT-AC
splicings)
GTF +
Genome
Step 3 Tuxedo Protocol - TOPHAT
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
Alignments (BAM)
•
•
•
•
•
-i/--min-intron-length <int> 40
-I/--max-intron-length <int> 5000
-a/--min-anchor-length <int> 10
-g/--max-multihits <int> 20
-G/--GTF <GTF/GFF3 file>
GTF +
Genome
Tuxedo Protocol
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
GTF +
Genome
CUFFLINKS (Transcript Assembly)
GTF
Alignments (BAM)
Assemblies (GTF)
CUFFMERGE (Final Transcript Assembly)
Assembly (GTF)
CUFFDIFF (Differential expression results)
Visualisation
(PDF)
CUMMERBUND (Expression Plots)
CUFFCOMPARE (Comparion to reference)
GTF
Step 4 Tuxedo Protocol - CUFFLINKS
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
GTF +
Genome
CUFFLINKS (Transcript Assembly)
GTF
Alignments (BAM)
Assemblies (GTF)
• Accurate quantification of a gene requires identifying which isoform
produced each read.
• Reference Annotation Based Transcript (RABT) assembly
• Sequence bias correction -b/--frag-bias-correct <genome.fa>
• multi-mapped read correction is enabled (-u/--multi-read-correct)
Tuxedo Protocol
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
GTF +
Genome
CUFFLINKS (Transcript Assembly)
GTF
Alignments (BAM)
Assemblies (GTF)
CUFFMERGE (Final Transcript Assembly)
Assembly (GTF)
CUFFDIFF (Differential expression results)
Visualisation
(PDF)
CUMMERBUND (Expression Plots)
CUFFCOMPARE (Comparion to reference)
GTF
Tuxedo Protocol - CUFFDIFF
Leaf (SAM1)
Read
1
Read
2
Leaf (SAM2)
Read
1
Read
2
Flower (SAM3)
Read
1
Read
2
Flower (SAM4)
Read
1
Read
2
Reads (FASTQ)
TOPHAT (Read Mapping)
Alignments (BAM)
CUFFLINKS (Transcript Assembly)
CUFFMERGE (Final Transcript Assembly)
Assembly (GTF)
GTF
mask file
CUFFDIFF (Differential expression results)
CUFFDIFF output – FPKM (fragments per kilobase of transcript per million
fragments mapped) values, fold change, test statistic, p-value, significance
statement.
GTF +
Genome
CUFFDIFF - Summarisation
1
(A)
1
(B)
(C)
2
1
1.
2.
3.
4.
3
4
2
4
2
4
A + B + C Grouped at Gene level
B + C Grouped at CDS level
A + C Grouped at Primary transcript level
A, B, C No group at the transcript level
• Cuffdiff output (11 files)
– FPKM tracking files (Transcript, Gene, CDS, Primary transcript)
– Differential expression tests (Transcript, Gene, CDS, Primary
transcript)
Look at
– Differential splicing tests – splicing.diff
difference in
distribution
– Differential coding output – cds.diff
(rather than total
– Differential promoter use – promoters.diff
level)
A test case – Ricinus Communis (Castor
bean)
• 5 tissues – Aim : identify differences in lipid-metabolic
pathways
A test case – Ricinus Communis (Castor
bean)
• Cufflinks – Cuffcompare Results
– RNA-Seq reads assembled into 75090 transcripts corresponding
to 29759 ‘genes’
– Compares to the 31221 genes in version 0.1 of the JCVI assembly
– 35587 share at least one splice junction (possible novel splice
variant).
– 2847 were located intergenic to the JCVI annotation and hence
may represent novel genes
– 218147 splice junctions were identified, 112337 supported by at
least 10 reads, >300,000 distinct to the JCVI annotation
Visualisation
• Bam files can be converted to wiggle plots
• CummeRbund for visualisation of Cuffdiff output
Bam, Wiggle and GTF files viewed in IGV
CummeRbund volcano and scatter plots
Thanks
• David Swarbreck (Genome Analysis Team Leader, TGAC)
• Mario Caccamo (Head Bioinformatics Division, TGAC)