Gene expression profiling is the key to understanding biological pathways and complex cellular systems. In this webinar we will discuss the challenges of targeted RNA-seq data analysis and present the solutions provided by the QIAGEN automated online data analysis tools. Using raw sequencing data from targeted sequencing, the output of the QIAseq primary data analysis tool and the options in QIAseq secondary analysis, such as normalization strategies, will be described. The use of Ingenuity Pathway Analysis (IPA) to unlock the molecular insights buried in experimental data by quickly identifying relationships, mechanisms, functions, and pathways of relevance will be shown with an example.
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Molecular insight into Gene Expression Using Digital RNAseq: Digital RNAseq Webinar Part 3
1. Sample to Insight
Molecular Insight into Gene Expression using Digital RNAseq
Data Analysis Tutorial
Melanie Hussong Ph.D., Scientist, NGS
Jean-Noel Billaud, Ph.D., Principal Scientist, Bioinformatics
1
Webinar on
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
2. Sample to Insight
Welcome to 3 part webinar series on Digital RNAsequencing
Digital RNA sequencing for accurate gene expression profiling
Part 1: What is digital RNAseq?
Speaker: Eric Lader, Ph.D.
Senior Director, Research & Development, QIAGEN
Date: Feb 17th, 1 pm EST, 10 am PST, 6 pm GMT
Part 2: Digital RNAseq for gene expression profiling
Speaker: Raed Samara, Ph.D.
Global Product Manager, NGS, QIAGEN
Date: Feb 24th, 1 pm EST, 10 am PST, 6 pm GMT
Part 3: Molecular Insight into gene expression profiling using digital
RNAseq: data analysis tutorial
Speaker: Melanie Hussong, Ph.D. Scientist, NGS
Jean-Noel Billaud, Ph.D. Principal Scientist. Bioinformatics
Date: March 1st, 1 pm EST, 10 am PST, 6 pm GMT
3. Sample to Insight
Molecular Insight into Gene Expression using Digital RNAseq:
Data Analysis Tutorial
Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 3
4. Sample to Insight
Today’s Agenda
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Why use Targeted Next Generation Sequencing (NGS) to study gene expression?
Targeted Enrichment
• QIAseq Targeted RNA Panels
QIAseq Primary Data Analysis
• Read Mapping
• Molecular Barcode Counting
QIAseq Expression Analysis
• Workflow
• Interface
• Data Interpretation: Ingenuity Pathway Analysis (IPA)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
5. Sample to Insight
Outline of a RNAseq Experiment
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Want to Study Gene Expression?
For example:
• Have 96 samples
• Investigate 500 genes
• Only have a limited amount of total RNA
• Budget per sample is $100
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
6. Sample to Insight
Gene Expression Profiling
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PCR-based
Accuracy
Limited sample & assay throughput
Requires a lot of RNA
Whole transcriptome sequencing (WTS)
Throughput power
Expensive
Complex data
Microarrays
Easy data analysis
High background noise
Requires a lot of RNA
Limited dynamic range
Traditional targeted RNA sequencing
Manageable data
Low per-sample cost
Amplification bias
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
7. Sample to Insight
QIAseq Targeted RNA: High Throughput Digital NGS
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Simple to use, complex behind the scenes
• Complete, Integrated System: Sample to Insight
o Sensitive and highly specific
o Extremely flexible in experimental design (n samples x n assays)
o Simple for end user to address bioinformatically
o Requires no rRNA depletion or blocking or dT selection
o Makes best use of limited NGS read budget
o Flexible content
– Leverages QIAGEN’s content know-how
– Disease and pathway focused panels
– Ready to use, easy to modify, and fully custom panel content all in one kit
• When? Who? Why?
• Scientists with known gene list or pathway
• Follow up on broader experiments, such as WTS or microarray
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
8. Sample to Insight
RNA
Extraction
Synthesize
cDNA
Library
Construction
Library
Quantification
Sequencing
Run
Data
Analysis
Data
Interpretation
Ingenuity Pathway
Analysis (IPA)
Molecular Barcode Assignment
Purification
Gene Specific PCR
Purification
Target Enrichment and Index Assignment
Purification
MiSeq
NextSeq
Ion Torrent
Platform Agnostic
Primary Analysis for MT count
QIAseq RNA Quantification Portal
Secondary Analysis for
Normalization
Sample Insight
QIAseq Targeted RNA Workflow: from Sample to Insight
8QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
9. Sample to Insight
GSP1
GSP2
MT
FS2
RS2
RS2
Adaptor and Index
Reverse gene-specific primer
QIAseq Targeted RNA Library Construction Schematic
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MT: 12-base unique barcode
GSP1, GSP2: Gene-specific primers
RS2, FS2: Universal PCR primers
RNA Sample
cDNA Synthesis
Primer Extension
and Molecular Barcoding
Double QIAseq Bead Cleanup
1st Stage PCR
2nd Stage PCR
library construction
and sample indexing
Library Quantification
QIAseq Bead Cleanup
QIAseq Bead Cleanup
6 Hours
Adaptor and Index
GSP1 and GSP2
never see each other,
thereby minimizing primer dimers
Molecular Barcoded
forward gene-specific primer
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
10. Sample to Insight
QIAseq Targeted RNA Panels
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• Cancer Transcriptome (411)
• Inflammation & Immunity Transcriptome (491)
• Signal Transduction PathwayFinder (421)
• Stem Cell & Differentiation Markers (309)
• Molecular Toxicology Transcriptome (386)
• Angiogenesis & Endothelial Cell Biology (356)
• Apoptosis & Cell Death (280)
• Extracellular Matrix & Cell Adhesion Molecules
(437)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
11. Sample to Insight
QIAseq Targeted RNA Data Primary Analysis Web Portal
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 11
• FREE Complete and Easy to use Data Analysis with Web-based Software can be found at:
ngsdataanalysis.sabiosciences.com/NGSRNA/
12. Sample to Insight
QIAseq Targeted RNA Data Analysis Upload Files Interface
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• .fastq or fastq.gz files (Illumina Platforms)
• .bam files (Ion Torrent)
• File Upload Tab
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
13. Sample to Insight
QIAseq Targeted RNA Data Analysis File Management Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 13
• File Management Tab
• Delete/Manage Files
14. Sample to Insight
QIAseq Targeted RNA Data Analysis RNA Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 14
■ RNA Quantification Tab
• Submit Runs for Analysis
• View and download Output Files
15. Sample to Insight
QIAseq Targeted RNA Data Analysis Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 15
• RNA Quantification Jobs Tab
16. Sample to Insight
QIAseq Targeted RNA Data Analysis Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 16
• RNA Quantification Jobs Tab
• Select the appropriate Catalog
Or Custom Catalog Number
17. Sample to Insight
QIAseq Targeted RNA Data Analysis Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 17
• RNA Quantification Jobs Tab
• Select Sample Files to be analyzed
18. Sample to Insight
QIAseq Targeted RNA Data Analysis Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 18
• RNA Quantification Jobs Tab
• File Lanes 1 for MiSeq/HiSeq and Ion Torrent
• File Lanes 4 for NextSeq
19. Sample to Insight
QIAseq Targeted RNA Data Analysis Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 19
• RNA Quantification Jobs Tab
20. Sample to Insight
Data Analysis for QIAseq Targeted RNA Sequencing
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 20
QIAseq Targeted RNA Data Analysis workflow
• Adaptor Trimming
o Remove the adaptor sequences from the reads
• Read Mapping
o Identify the possible position of the read within the reference
o Align the read sequence to reference sequences
• Molecular Barcode Counting
o Merges unique barcodes with mapped reads
Adaptor
Trimming
Read
Mapping
Molecular
Barcode
Count
(Primary
Analysis)
21. Sample to Insight
Read Mapping
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Adaptor Trimming and Alignment of Reads to the Reference Genome
• Trim universal PCR adaptor sequences off the 3’ end of reads.
• Reads that are less than 55 bp in length after adapter trimming are likely oligo dimers and
are dropped.
• The first 12 bp are removed from each read as they are the Molecular Tag (MT) sequence.
o Append the MT sequence to the read identifier.
• Using STAR RNA read mapper, reads are aligned to GRCh38 reference genome.
o STAR uses Gencode transcript models to aid in aligning reads that cross introns.
o Reads that do not map well are dropped.
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
22. Sample to Insight
Process Read Alignments
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Process Read Alignments
• At least 60 bp of the reads must align otherwise the reads are dropped.
o As most oligos are less than 30 bp, this ensures that at least 30 bp of the endogenous
RNA sequence is present in the read.
• Drop reads that are generated by off-target or unintended priming by identifying the 5’ primer
at the start of the read.
• If the genome alignment at the primer start site is not very good then these reads are
dropped as the boundary between the MT region and the primer region of the read is
ambiguous. Hence dropping these reads improves MT counting accuracy.
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
23. Sample to Insight
Count MolecularTags
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Count Molecular Tags
• For each amplicon, cluster together the 12 bp read sequences from the MT region that have
low edit distance from each other. Reads in a cluster putatively originated from the same
MT priming event.
• All of the above reads have the same MT ( ) although there are five replicates of one
Transcript they have a MT Count of one.
• All of the above reads have different MTs and are five unique MTs for one transcript they
have a MT Count of five.
Five replicates of one transcript MT = 1
Five unique transcripts of a gene MT = 5
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
24. Sample to Insight
QIAseq Targeted RNA Data Analysis Quantification Interface
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 24
• RNA Quantification Jobs Tab
Job Status
25. Sample to Insight
QIAseq Primary Data Analysis Output
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 25
• Features of the Primary Analysis Molecular Tag Report
Summary Tab
26. Sample to Insight
QIAseq Primary Data Analysis Output
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 26
• Features of the Primary Analysis Molecular Tag Report
Summary Tab
27. Sample to Insight
QIAseq Primary Data Analysis Output
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 27
• Features of the Primary Analysis Molecular Tag Report
Summary Tab
28. Sample to Insight
QIAseq Primary Data Analysis Output
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 28
• Features of the Primary Analysis Molecular Tag Report
Summary Tab
29. Sample to Insight
QIAseq Primary Data Analysis Output
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 29
• Features of the Primary Analysis Molecular Tag Report
Summary Tab
30. Sample to Insight
Read Details: Unique Captures per Target Gene Count
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Sample by Sample, Gene by Gene Unique Barcode Counts (and Total)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
31. Sample to Insight
Read Details: Unique Captures per Target Gene Count
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Sample by Sample, Gene by Gene Unique Barcode Counts (and Total)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
Differential gene expression can be seen inter- and intra- samples
32. Sample to Insight
Read Details: Unique Captures per Target Gene Count
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gDNA Control Amplicons
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
33. Sample to Insight
Read Details: Unique Captures per Target Gene Count
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Housekeeping Reference Genes Amplicons
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
34. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Setup
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 34
• Found in the GeneGlobe Data Analysis Center
• Upload Data
35. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Setup
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 35
• Analysis Setup
Uploaded Data
36. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Setup
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• Analysis Setup
Sample Manager
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
37. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Setup
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 37
• Analysis Setup
Data QC
38. Sample to Insight
Secondary Data Analysis: Normalization Against
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• Unique barcodes/total unique barcodes per sample
This method simply uses the sum of the molecular tag counts in each sample as the
normalization factor. However, it assumes that equal amounts of RNA were used in
the analysis and that a majority of the genes are expressed at a relatively stable level
across the Samples.
• Housekeeping genes (one, some, all)
Housekeeping genes are ranked as most stable to least stably expressed.
Housekeeping genes stability ranking is based on GeNorm calculations.
Genes that have a stability factor of less than 1.5 are considered to be stably
expressed.
• Genes of your choice
Calculate - fold change, p-values, generate heat maps, volcano plots
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
39. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Setup
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 39
• Analysis Setup
Select Normalization Method – Total Molecular Tags
40. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Setup
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 40
• Analysis Setup
Select Normalization Method – Housekeeping Genes
41. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 41
• Analysis
42. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Plots and Charts
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 42
• Plots and Charts
43. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Scatter Plot
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 43
44. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Volcano Plot
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 44
45. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Clustergram
45QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
46. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Export Data
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 46
• Export Data
47. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Export Data
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 47
• Gene Table Tab
48. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Export Data
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 48
• Fold Changes and p-value Tab
49. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Export Data
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D. 49
• Fold Regulation and p-value Tab
50. Sample to Insight
QIAseq Targeted RNA Secondary Data Analysis Export Data
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• IPA Upload Tab
■ Biological Interpretation Using IPA
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
51. Sample to Insight
QIASeq Targeted RNA Panel: Sample to Insight workflow
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Integrated library preparation
Works with any RNA sample type
Compatible with most sequencers
Complementary data analysis tool for fold change analysis
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
52. Sample to Insight
The QIAGEN Knowledge Base: Content & Content-aware Analytics
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• Cancer Scoring
• Hereditary Disease Scoring
• Causal Network Analysis
• Druggable Pathways
• Disease Model-based Analysis
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
53. Sample to Insight
Introduction to Ingenuity Pathway Analysis (IPA), Interpretation
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Gene View, Chem View, and
Disease/Function View
Human and Mouse
Isoform Views
Canonical Pathways/Molecule
Activity Predictor
Upstream Analysis
Upstream Regulators/Mechanistic
Network/Causal Networks
Diseases & Functions
Downstream Effects Analysis
Regulator Effects
microRNA Target Filter
BioProfiler
Interaction Networks,
Build and Overlay tools
IsoProfiler
My Findings
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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Biological Interpretation of Breast Cancer (using 2 breast cancer cell lines)
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BT-549 and MDA-MD-231 Breast cancer cell lines
• Understanding the transcriptome in these cell lines when TOB1 is knockdown by siRNA
o What are the signaling or metabolic pathways involved, are they activated/inhibited? (Canonical
Pathways)
o What are the underlying transcriptional programs? (UpstreamAnalysis)
o What biological processes are involved and in what way? (Diseases & Functions)
o Identify Isoforms differentially expressedconnectedto cancer (IsoProfiler)
o What hypotheses can be drawn further? (Mechanistic Network, Causal Network, Regulator Effects)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
55. Sample to Insight
QIAseq RNA Panels: 2 Breast cancer cell lines
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Upload the 254 QIAseq RNA panel dataset
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
56. Sample to Insight
IPA: Analysis of QIAseq RNA panel of 2 Breast Cancer cell lines
56QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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Canonical Pathways (CP) in BT-549 cell line
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Understanding which Canonical Pathways (overlap) are involved and determining the predicted
activity of this CP (Pathway Activity Analysis)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
58. Sample to Insight
PathwayActivity Analysis in BT-549 cell line
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Cell Cycle G1/S Checkpoint Regulation is predicted to be inhibited (Z-score negative)
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
59. Sample to Insight
Upstream Analysis in MDA-MB-231 cell line
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Determining which transcriptional program is involved and in what way
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
60. Sample to Insight
TP53-driven network in MDA-MD-231 cell line
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TP53 is predicted to be activated when TOB1 siRNA is added
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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TP53-driven network in MDA-MD-231 cell line
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Overlay statistically significant diseases and functions
Apoptosis
Proliferation
of cells
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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CTNNB1-driven Mechanistic Network in MDA-MB-231
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CTNNB1 is predicted to be activated and drives a Mechanistic Network along with 18 other
regulators. This Network may explain the pattern of expression of 38 targets downstream (not
shown here).
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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Downstream Effect Analysis of MDA-MD-231 cell line
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Apoptosis of tumor cell lines is
predicted to be activated
Apoptosis
of tumor
cell lines
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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TCEB3 driven Causal Network in MDA-MB-231
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TCEB3 Causal Network is predicted to be activated and to increase “Invasion of Cells”
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
65. Sample to Insight
Regulator Effects in MDA-MB-231
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Connecting the transcriptional program and the biological processes involved together via the
targets in the dataset
Upstream
Regulators
Targets
Downstream
Diseases and
Functions
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
66. Sample to Insight
IsoProfiler in IPA to discover isoforms that may drive tumor progression
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Some VEGF isoforms are identified in the datasets that are specifically connected to EEC
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
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Using QIAGEN BioinformaticsApproach
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• Using IPA, we have been able to:
o Visualize the differentially expressed genes in the 2 breast cancer cell lines with TOB1 siRNA
o Understand which signaling pathways are involved in tumor progression in these cell lines
o Discover potential transcriptional program(s) that are induced or repressed in these BrCa cell lines treated with TOB1
siRNA
o Discover specific biological processes that participate in the tumor progression
o Identify Isoforms differentially expressed connected to EEC
o Highlight new hypotheses (ready to be tested and validated) that could explain cell invasion
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
68. Sample to Insight
68
Jean-Noel Billaud, Ph.D.
Principal Scientist.
Bioinformatics
Melanie Hussong, Ph.D.
Scientist, NGS
QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
69. Sample to Insight
69QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
70. Sample to Insight
70QIAseq RNA Part 3: Molecular Insight into Gene Expression using Digital RNAseq: Data Analy sis Tutorial, 03/01/2016, Melanie Hussong, Ph.D. and Jean-Noel Billaud, Ph.D.
Notas do Editor
Dr. so and so did a …. Found a … follow up with NGS on….
Gene Expression profiling is central to many biological process such as: Immune profiling, Cell Cycle Research, Cancer Research, Biomarker development.
It is an open the box and use integrated system that counts reads and how many original molecules / original RNAs you have. It is engineered to produce results that are both accurate and precise.
We can guide you if need be in experimental design and number of samples etc…
QIAGEN has been developing panels for years that are diseased and pathway focused that have leveraged External databases and papers and been curated by PhDs.
Scientist know what genes they are interested in. For example genes in cancer, differentiation, development, immunity or signal transduction you as a research already have these in mind.
The only sample-to-insight digital RNAseq solution for unbiased gene expression profiling using NGS.
Integrated library preparation
Works with any RNA sample type
Amplicons are short so it is compatible with FFPE (on average about 97 bp)
Compatible with most sequencers
Single read
Complementary data analysis tool for fold change analysis
Easy enough for first time NGS users
1st Strand cDNA Synthesis Random hexamers and oligo-dT primed.
Everything needed to go from RNA to a Sequence Ready Library in one kit in one day.
MT consisting of 12 random bases, therefore providing 412 = 16777216 unique molecular tags for each BC primer
The gene specific primers are never in the same reaction which minimizes primer dimers.
Schematic –
Notice we never do highly multiplex PCR with both 5’ and 3’ primers.
Limited number of gene specific extensions, then universal PCR and sample indexing.
Amplicons rigorously restricted in size and Tm to make this extremely uniform.
170+ pathway and disease focused panels for humans (84 gene panels)
Make a Custom Panel by selecting from over 500 pathway maps. Customize the content to address your research questions.
Shows the total reads per sample
Shows the primer dimers. So if this is high then it suggests that there may have been a problem in the clean-up steps.
Reads used for MT counting. Counting the number of unique molecular tags delivers an accurate representation of the starting amounts of RNA when compared to counting reads.
Row 11: MTs: Number of unique tags per sample
Row 12: reads per MT mean: Indicates the unique capture read times so aim to have between 2 – 3. This seems to plateau at about 10 and hence you see diminished returns.
We see the Gene ID and Gene Symbol,
Gene Strand
Chromosome location and coordinates
Control Type
Single exon indicates if the amplicon that is designed is across an exon (indicated by 0) or within an exon (indicated by a 1)
The gDNA control amplicons are spread out on the genome in transcriptionally dead regions. These will get flagged if the MT counts are high suggesting gDNA background.
The reference housekeeping genes were chosen because they are expressed at a moderate level.
The inclusion of the Housekeeping Reference Genes in a QIAseq RNA Panel means that they can be used to normalize the data and to make sample-to-sample and run-to run comparisons conceivable.
The QIASeq Targeted RNA Panel Secondary Data Analysis Webportal analyzes molecular tag counts to normalize the data and calculate the changes in gene expression.
The “Uploaded Data” page displays the molecular tag counts, Sample name, and defined Groups. Confirm that the data was uploaded and labeled correctly.
On the “Sample Manager” page, assign “Samples” to their different “Groups”, with at least one “Control Group” and at least one “Group 1”, or choose to “Exclude Sample” using the dropdown menu for each Sample. Click the “Update” button when finished. For Custom and Extended Panels, use the “Select Housekeeping Genes” to identify the desired housekeeping/reference genes.
Review the “Data QC” page to ensure that each Sample has passed the Genomic DNA Contamination quality control.
Unique MTs:
Normalization to Total MTs is used in a panel where a majority of genes are stably expressed or majority of MTs originate from stable genes.
In general, Total MTs used with samples originating from the same cell type or tissue type.
Equal loading of RNA sample into cDNA reaction and BC reaction.
P-values and volcano plots are only generated if replicates are performed in the experiment.
This method simply uses the sum of the molecular tag counts in each sample as the normalization factor. However, it assumes that equal amounts of RNA were used in the analysis and that a majority of the genes are expressed at a relatively stable level across the Samples. Therefore, the software performs a quality control test on the percentage of relatively expressed genes in each Sample before recommending whether or not this method should be used. It also assumes that the samples originate from the same cells or tissue type.
Upon selecting “Total Molecular Tags” from the “Choose your preferred method of analysis” dropdown menu, the software provides a table listing for each Samples “Stably Expressed Genes” molecular tag count, “Total” molecular tag count, “% Stably Expressed Genes”, and “QC Check”. If more than half (> 50 %) of the molecular tag counts are derived from stably expressed genes in a Sample, then the “QC Check” column reports “OK” for that Sample; otherwise, it reports “Caution”.
GeNorm ranks expression from low to high.
Genes that have a stability factor of less than 1.5 are more stably expressed.
If you use all of the housekeeping genes you will get a fold change that is slightly an under estimated value when compared to using only the top three ranked genes which gives you a more accurate fold change.
Upon selecting “Average Reference Genes' Molecular Tags” from the “Choose your preferred method of analysis” dropdown menu, a table is provided of the reference genes that demonstrate a GeNorm Stability Factor less than 1.5. Checkboxes are provided to de-select or re-select genes as desired based on their stability factor ranking. The normalization factor, calculated as the average molecular tag count for the selected reference genes in each Sample and in each Group, is provided for evaluation to insure its stability across Samples and Groups.
Review the “Fold Regulation” and “Fold Change” pages for the final results processed by the software from the inputted data.
Define “Groups” to be compared. Choose the fold-change boundary (threshold) of interest.
Mouse over the right-hand table entries to highlight symbols on the plot.
Click check boxes to remove genes from or add genes to plot.
Click the “Export Data” button to download the results as an Excel file.
To save the figure, mouse over and click on the options available from the top right icon.
Define “Groups” to be compared. Choose the fold-change boundary (threshold) of interest.
Mouse over the right-hand table entries to highlight symbols on the plot.
Click check boxes to remove genes from or add genes to plot.
Click the “Export Data” button to download the results as an Excel file.
To save the figure, mouse over and click on the options available from the top right icon.
Define “Groups” to be compared. Choose the fold-change boundary and p-value thresholds.
Mouse over the right-hand table entries to highlight symbols on the plot.
Click check boxes to remove genes from or add genes to plot.
Click the “Export Data” button to download the results as an Excel file.
To save the figure, mouse over and click the options available from the top right icon.
The clustergram performs non-supervised hierarchical clustering of the entire dataset to display a heat map with dendrograms indicating co-regulated genes across Groups or individual Samples. With Targeted RNA Panel data, the clustergram provides an overview of what genes may be co-regulated by a common factor, e.g. a transcription factor, miRNA, methylation status, etc.
Fold change is calculated as the average ratio of normalized molecular tag counts (or relative gene expression) between the Control Group and each Test Group. Numbers greater than 1 indicate upregulated or increased gene expression, numbers between 0 and 1 indicate down regulated or decreased gene expression, and a fold change of 1 indicates no change. Fold-change results greater than 2 or less the 0.5 are highlighted in red.
For all fold-change values greater than 1, the fold-regulation and fold-change values are the same. For all fold-change values (X) less than 1, the fold regulation is the negative inverse of the fold change (–1/X). For example, a fold-change value of 0.25 corresponds with a fold regulation of –4.0.
Fold-regulation values greater than 1 indicate upregulated or increased gene expression; fold-regulation values less than zero indicate down regulated or decreased gene expression. Fold-regulation results greater than 2 or less the –2 are highlighted in red.
Content powers advanced data analysis and interpretation
Quality of manual curation
Proprietary databases plus best in class public domain databases offer as comprehensive as possible view of characterized variants
Workflow using Claudin Low
To define:
Bioprofiler -> to Pathway
RNA seq -> Claudin Low