VM origin. Okeanos: Image Trinity_U16 (upgrade to Ubuntu16.04, thanks to Alexandros Dimopoulos) X2go: LXDE
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1 VM origin Okeanos: Image Trinity_U16 (upgrade to Ubuntu16.04, thanks to Alexandros Dimopoulos) X2go: LXDE
2 NGS intro + Genome-Based Transcript Reconstruction and Analysis Using RNA-Seq Data Based on material from: Brian Haas Broad Institute Martin Reczko Source:
3 Overview Next generation sequencing (NGS) introduction Quality control Genome-based and genome-free (de-novo) transcript reconstruction from RNA-Seq Running the Tuxedo and Trinity software and visualizing the results. Principles of transcript abundance estimation Principles of differential expression analysis Analysis frameworks included in Tuxedo and Trinity
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23 Overview of RNA-Seq Quality control From:
24 Common Data Formats for RNA-Seq FASTA format: >61DFRAAXX100204:1:100:10494:3070/1 AAACAACAGGGCACATTGTCACTCTTGTATTTGAAAAACACTTTCCGGCCAT FASTQ AAACAACAGGGCACATTGTCACTCTTGTATTTGAAAAACACTTTCCGGCCAT + ACCCCCCCCCCCCCCCCCCCCCCCCCCCCCBC?CCCCCCCCC@@CACCCCCA Read Quality values AsciiEncodedQual(x) = -10 * log10(pwrong(x)) + 33 AsciiEncodedQual ( C ) = 64 So, Pwrong( C ) = 10^( (64-33/ (-10) ) = 10^-3.4 =
25 Paired-end Sequences Two FastQ files, read name indicates left (/1) or right (/2) read of AAACAACAGGGCACATTGTCACTCTTGTATTTGAAAAACACTTTCCGGCCAT + CTCAAATGGTTAATTCTCAGGCTGCAAATATTCGTTCAGGATGGAAGAACA + C<CCCCCCCACCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCBCCCC
26 Good QC Summarise, Visualise and Flag
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28 Technical Technical Failures C T A G C T A G C T A G Signal Level Signal Level Signal Level Call = T Confidence = High Call = T Confidence = Low Call = T Confidence = Low
29 Technical Phred Scores Phred = -10 log 10 p p = Probability call is incorrect 10% error Phred10 1% error Phred20 0.1% error Phred30
30 Technical Incorrect Encoding Phred64! #$%& Phred Phred33 (Sanger) Phred64 (Illumina)
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40 Sequencing adapter identification
41 Sequencing adapter removal
42 Transcript Reconstruction from RNA-Seq Reads Nature Biotech, 2010
43 Transcript Reconstruction from RNA-Seq Reads TopHat
44 Transcript Reconstruction from RNA-Seq Reads TopHat Cufflinks
45 Transcript Reconstruction from RNA-Seq Reads TopHat Trinity The Tuxedo Suite: End-to-end Genome-based RNA-Seq Analysis Software Package Cufflinks (successor: StringTie ) GMAP
46 Transcript Reconstruction from RNA-Seq Reads TopHat Trinity Cufflinks
47 Transcript Reconstruction from RNA-Seq Reads TopHat Trinity Cufflinks GMAP
48 Transcript Reconstruction from RNA-Seq Reads End-to-end Transcriptome-based RNA-Seq Analysis Software Package Trinity GMAP
49 Overview of the Tuxedo Software Suite Bowtie (fast short-read alignment) TopHat (spliced short-read alignment) Cufflinks (transcript reconstruction from alignments) (now: StringTie) Cuffdiff (differential expression analysis) (now: BallGroom) CummeRbund (visualization & analysis) (now: BallGroom)
50 Slide courtesy of Cole Trapnell
51 The TopHat Pipeline From Trapnell, Pachter, & Salzberg. Bioinformatics. 2009
52 seed and extend
53 Alignments are reported in a compact representation: SAM format 0 61G9EAAXX100520:5:100:10095: chr M 6 = CCCAAACAAGCCGAACTAGCTGATTTGGCTCGTAAAGACCCGGAAA 10 ###CB?=ADDBCBCDEEFFDEFFFDEFFGDBEFGEDGCFGFGGGGG 11 MD:Z:67 12 NH:i:1 13 HI:i:1 14 NM:i:0 15 SM:i:38 16 XQ:i:40 17 X2:i:0 SAM format specification:
54 Alignments are reported in a compact representation: SAM format 0 61G9EAAXX100520:5:100:10095:16477 (read name) 1 83 (FLAGS stored as bit fields; 83 = ) 2 chr1 (alignment target) (position alignment starts) M (Compact description of the alignment in CIGAR format) 6 = (read sequence, oriented according to the forward alignment) 9 CCCAAACAAGCCGAACTAGCTGATTTGGCTCGTAAAGACCCGGAAA 10 ###CB?=ADDBCBCDEEFFDEFFFDEFFGDBEFGEDGCFGFGGGGG 11 MD:Z:67 (base quality values) 12 NH:i:1 13 HI:i:1 14 NM:i:0 15 SM:i:38 (Metadata) 16 XQ:i:40 17 X2:i:0 SAM format specification:
55 Alignments are reported in a compact representation: SAM format 0 61G9EAAXX100520:5:100:10095:16477 (read name) 1 83 (FLAGS stored as bit fields; 83 = ) 2 chr1 (alignment target) (position alignment starts) M (Compact description of the alignment in CIGAR format) 6 = Still not compact enough Millions -264 to billions (read sequence, of reads oriented takes according up a lot to the of forward space!! alignment) 9 CCCAAACAAGCCGAACTAGCTGATTTGGCTCGTAAAGACCCGGAAA 10 ###CB?=ADDBCBCDEEFFDEFFFDEFFGDBEFGEDGCFGFGGGGG 11 Convert MD:Z:67 SAM to binary BAM format. (base quality values) 12 NH:i:1 13 HI:i:1 14 NM:i:0 15 SM:i:38 (Metadata) 16 XQ:i:40 17 X2:i:0 SAM format specification:
56 Tools for Samtools converting SAM <-> BAM Viewing BAM files (eg. samtools view file.bam less ) Sorting BAM files, and lots more:
57 Cufflinks
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61 Reads vs. transcripts
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63 Compatible reads/fragments
64 Wang et al., Alternative isoform regulation in human tissue transcriptomes, Nature
65 Transcript Reconstruction Using Cufflinks From Martin & Wang. Nature Reviews in Genetics. 2011
66 Transcript Reconstruction Using Cufflinks From Martin & Wang. Nature Reviews in Genetics. 2011
67 Transcript Reconstruction Using Cufflinks From Martin & Wang. Nature Reviews in Genetics. 2011
68 More details at:
69 Min. vertex cover is equiv. to max. matching
70 'Marrying' reads with transcripts
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83 Novel isoform discovery
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85 FPKM Tracking Files Column number Column name Example Description 1 tracking_id TCONS_ A unique identifier describing the object (gene, transcript, CDS, primary transcript) 2 class_code = The class_code attribute for the object, or "-" if not a transcript, or if class_code isn't present 3 nearest_ref_id NM_ The reference transcript to which the class code refers, if any 4 gene_id NM_ The gene_id(s) associated with the object 5 gene_short_nam e Lypla1 The gene_short_name(s) associated with the object 6 tss_id TSS1 The tss_id associated with the object, or "-" if not a transcript/primary transcript, or if tss_id isn't present 7 locus chr1: Genomic coordinates for easy browsing to the object 8 length 2447 The number of base pairs in the transcript, or '-' if not a transcript/primary transcript 9 coverage Estimate for the absolute depth of read coverage across the object 10 q0_fpkm FPKM of the object in sample 0 11 q0_fpkm_lo the lower bound of the 95% confidence interval on the FPKM of the object in sample 0 12 q0_fpkm_hi the upper bound of the 95% confidence interval on the FPKM of the object in sample 0 13 q0_status OK Quantification status for the object in sample 0. Can be one of OK (deconvolution successful), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents deconvolution.
86 Priority Code Description Class Codes 1 = Complete match of intron chain 2 c Contained 3 j Potentially novel isoform (fragment): at least one splice junction is shared with a reference transcript 4 e Single exon transfrag overlapping a reference exon and at least 10 bp of a reference intron, indicating a possible pre-mrna fragment. 5 i A transfrag falling entirely within a reference intron 6 o Generic exonic overlap with a reference transcript 7 p Possible polymerase run-on fragment (within 2Kbases of a reference transcript) 8 r Repeat. Currently determined by looking at the soft-masked reference sequence and applied to transcripts where at least 50% of the bases are lower case 9 u Unknown, intergenic transcript 10 x Exonic overlap with reference on the opposite strand 11 s An intron of the transfrag overlaps a reference intron on the opposite strand (likely due to read mapping errors) 12. (.tracking file only, indicates multiple classifications)
87 Cufflinks pipelines Discovering novel genes and transcripts 1 Map the reads for each tissue to the reference genome 2 Run Cufflinks on each mapping file 3 Merge the resulting assemblies 4 (optional) Compare the merged assembly with known or annotated genes Differential expression 5 Run cuffdiff
88 Visualizing Alignments of RNA-Seq reads
89 Text-based Alignment Viewer % samtools tview alignments.bam target.fasta
90 IGV
91 IGV: Viewing Tophat Alignments
92 Transcript Structures in GTF Format (tab-delimited fields per line shown transposed to a column format here) (genomic contig identifier) 1 Cufflinks 2 transcript (left coordinate) (right coordinate) (strand) 7. 8 gene_id "CUFF.1"; transcript_id "CUFF.1.1"; FPKM " (annotations) Cufflinks 2 exon gene_id "CUFF.1"; transcript_id "CUFF.1.1"; exon_number "1"; FPKM "
93 Demo: Tuxedo and IGV Run Tophat to align reads to the genome Reconstruct transcripts using cufflinks View genome-aligned reads and reconstructed transcripts using IGV
94 Full Tuxedo Framework Demo See: Tuxedo_workshop_activities.pdf
95 Tuxedo Framework for Transcriptome Analysis Derived from: Nat Protoc Mar 1;7(3): doi: /nprot
96 Differential Expression Analysis Using RNA-Seq
97 Diff. Expression Analysis Involves Counting reads Statistical significance testing Sample_A Sample_B Fold_Change Significant? Gene A fold No Gene B fold Yes
98 Observed RNA-Seq Counts Result from Random Sampling of the Population of Reads Technical variation in RNA-Seq counts per feature is well modeled by the Poisson distribution Mean # fragments (observed read counts) See:
99 Beware of concluding fold change from small numbers of counts Poisson distributions for counts based on 2-fold expression differences No confidence in 2-fold difference. Likely observed by chance. High confidence in 2-fold difference. Unlikely observed by chance. From:
100 More Counts = More Statistical Power Example: 5000 total reads per sample. Observed 2-fold differences in read counts. SampleA Sample B Fisher s Exact Test (P-value) genea geneb genec < 0.001
101 Tools for DE analysis with RNA-Seq See: ShrinkSeq NoiSeq bayseq Vsf Voom SAMseq TSPM DESeq EBSeq NBPSeq edger (metaseqr) + other (not-r) including CuffDiff
102 Visualization of DE results and Expression Profiling
103 Plotting Pairwise Differential Expression Data Volcano plot ( fold change vs. significance) MA plot (abundance vs. fold change) Log 2 (fold change) (A of MA) Log 10 (Pvalue) Log 2 (fold change) Log 2 Average Expression level (M of MA) Significantly differently expressed transcripts have FDR <= (shown in red)
104 Comparing Multiple Samples Heatmaps provide an effective tool for navigating differential expression across multiple samples. Clustering can be performed across both axes: -cluster transcripts with similar expression patters. -cluster samples according to similar expression values among transcripts.
105 Examining Patterns of Expression Across Samples Can extract clusters of transcripts and examine them separately.
106 Summary of Key Points RNA-Seq is a versatile method for transcriptome analysis enabling quantification and novel transcript discovery. Genome-based and genome-free methods exist for transcript reconstruction Expression quantification is based on sampling and counting reads derived from transcripts Fold changes based on few read counts lack statistical significance. Multiple analysis frameworks are available alternative and often complementary approaches to support biological investigations.
107 Software Links Tuxedo Bowtie: Tophat: Cufflinks: Trinity IGV for Visualization GMAP Samtools
108 Papers of Interest Next generation transcriptome assembly Tuxedo protocol Trinity
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