Whole Transcriptome Analysis of Illumina RNA- Seq Data. Ryan Peters Field Application Specialist
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1 Whole Transcriptome Analysis of Illumina RNA- Seq Data Ryan Peters Field Application Specialist
2 Partek GS in your NGS Pipeline Your Start-to-Finish Solution for Analysis of Next Generation Sequencing Data Data RNA-Seq / SmallRNA- Seq ChIP-Seq DNA-Seq MeDIP-Seq coming soon 2 Copyright Partek
3 Partek Web Tools (coming soon) 3 Copyright Partek
4 Data Import Sequence Reads Flexible import Many supported formats Align Reads to Reference Genome 4 Copyright Partek
5 RNA-Seq workflow for Whole Transcriptome Analysis Import & Quality Control Assign reads to known RNAs in a transcriptome db Differential expression of mrnas Identification of alternative splicing events Differential expression of non-coding RNAs Coding SNP discovery Biological Interpretation Detect Unexplained Regions 5 Copyright Partek
6 Biological Replicates? 2 Illumina RNA-Seq Datasets Dataset #1 Breast Cancer Biological Replicates - YES Expression of genes between ER+, ER-, Normal Dataset #2 Brain vs. UHR Biological Replicates - NO Expression profile of genes between Brain sample and UHR sample Goal: Understand different types of statistical tests used for each scenario 6 Copyright Partek
7 Assign reads to known isoforms modified E/M algorithm Junction reads Paired end reads Multiple aligned reads Strand-specific reads Strand-specific reads can distinguish genes transcribed from forward/reverse strand. 7 Copyright Partek
8 Dataset #1 Breast Cancer Data w/ Replicates
9 Breast Cancer Dataset Illumina s idea Data Set Illumina idea challenge (Illumina Data in Excellence Award) 8 Paired-End RNA-Seq samples 4 ER +, 3 ER -, 1 Normal Control Breast Cancer Cell Lines Replicates Aligned using TopHat, junction alignment available BAM format 9 Copyright Partek
10 Summary Report Know where your reads are mapping: 10 Copyright Partek
11 Principal Components Analysis(PCA) 11 Copyright Partek
12 Biological Replicates - ANOVA Most powerful ANOVA implementation in Partek GS 1. Balanced, Unbalanced & Incomplete 2. Random & Fixed Effects (mixed model) 3. Nested Hierarchical designs 4. Numeric & Categorical Variables 5. Any number of factors 6. Linear Contrasts 12 Copyright Partek
13 Statistical Report 13 Copyright Partek
14 Create Gene List 14 Copyright Partek
15 Hierarchical Clustering of Significant Genes 15 Copyright Partek
16 GO Enrichment 16 Copyright Partek
17 Visualize Diff. Expression and Alternative Splicing 17 Copyright Partek
18 Allele Specific Expression Use Analysis of Variance to study allele specific expression based on the interaction of allele (A, T, G, C) counts and sample groups. 18 Copyright Partek
19 A Table of Distant Paired-end Reads Translocations? Fusion genes? 19 Copyright Partek
20 Biological Interpretation Pathway Analysis (coming soon) 20 Copyright Partek
21 Dataset #2 Brain & UHR w/ No Replicates
22 Dataset #2 Brain vs. UHR Whole Transcriptome data for universal human reference RNA (UHR) & human brain RNA (Brain) samples Sequenced with strand specific reads, using Illumina Genome Analyzer Aligned using Eland million reads / sample Sorted.txt format No replicates Hypothesis: Expect Differential expression of genes specific to neuronal function in Brain sample compared to the UHR sample? 22 Copyright Partek
23 Transcript Level Mapping No Replicates Each row is NCBI mrna (e.g., NM_ ) Probability of differential transcript expression across groups Probability of alternative splicing within a gene Log Likelihood (Diff Exp) / Alt Splice (χ2) Transcript Both Raw & Normalized read counts Gene per sample level level 23 Copyright Partek
24 Gene Level Analysis ACTL6B: Encodes Actin-like 6B protein, a subunit that may be involved in the regulation of genes by structural modulation of their chromatin, specifically in the brain. 24 Copyright Partek
25 Transcript Level Analysis 25 Copyright Partek
26 Exon Level Expression Analysis 26 Copyright Partek
27 Discover Novel Exons & Transcripts 27 Copyright Partek
28 Coding SNP Discovery and Visualization 28 Copyright Partek
29 GO Enrichment Brain vs. UHR 29 Copyright Partek
30 Up-/Down-regulation of Functional Group Forest plot of Brain VS Uhr 30 Copyright Partek
31 Biological Interpretation Brain vs. UHR Pathway Analysis (coming soon) 31 Copyright Partek
32 Integrated Genomics A few examples..
33 Integration of ChIP-seq & RNA-Seq data ChIP-seq: neuron-restrictive silencer factor (NRSF). Repress the expression of neuronspecific genes in non-neuronal cells PLCH2: phospholipase activity. May be important for formation and maintenance of the neuronal network in the brainf 33 Copyright Partek
34 Integration of RNA-seq & Exon Array Data 34 Copyright Partek
35 More Next Gen Analysis.
36 Differential Expression of Non-coding RNAs SnoRNA, sirna, mirna, long non-coding RNA 36 Copyright Partek
37 MeDIP-seq Workflow for Methylation Study MeDIP-2 MeDIP-1 37 Copyright Partek
38 ChIP-Seq Flow Chart Sequence Reads Import Align Reads to Reference Genome Detect peaks Detect motifs 38 Copyright Partek
39 *Upcoming Statistics Webinars October 26 th, Copyright Partek
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