Targeted RNA and DNA sequencing in disease prediction ...• Down to 2 ng fresh DNA QIAseq Targeted...

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Targeted RNA and DNA sequencing in disease prediction using QIAseq tools

Michael Hansen, Ph.D., Technical Specialist

1Targeted RNA and DNA sequencing in disease prediction using QIAseq tools

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Legal disclaimer

QIAGEN products shown here are intended for molecular biology

applications. These products are not intended for the diagnosis,

prevention or treatment of a disease.

For up-to-date licensing information and product-specific

disclaimers, see the respective QIAGEN kit handbook or user

manual. QIAGEN kit handbooks and user manuals are available at

www.qiagen.com or can be requested from QIAGEN Technical

Services or your local distributor.


Sample to Insight

Sample to Insight Workflow

4

Ingenuity Variant Analysis

QIAGEN® Clinical Insight

Interpret

Ingenuity Pathway Analysis

HGMD®

Inova Genomes

Biomedical

Genomics

Workbench

&

Biomedical

Genomics Server

Solution

QIAGEN® Clinical

Insight Analyze

(Early Access)

QIAseq FX

Library Kits

QIAseq 1-step

Amplicon KIt

QIAseq Ultralow

Input Kit

QIAseq miRNA

Library kit

QIAseq stranded

RNA kit

QIAseq RNA

Panels

QIAseq DNA

Panels

QIAseq RNAscan

Panels

QIAamp DNA kits

PAXgene RNA/DNA kits

GeneRead DNA FFPE Kit

QIAamp cfDNA/RNA Kit

exoRNeasy Kits

RNeasy Kits

Thermo

Illumina


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What is NGS?

5

Region of interest

+ Whole Genome Sequencing (WGS)

+ Whole Transcriptome Analysis (WTA)

Exome sequencing

Targeted sequencing

Sample preparation

Less info

rmation

Hig

her

covera

ge

Less c

ost

/ effort


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NGS can be used to detect all biomarkers

7

Biomarkers

Gene

expression

Copy

number

variants

Indels

Mutations

miRNA

expression

Fusions


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QIAseq solutions to detect all biomarkers using NGS

8

Biomarkers

Gene

expression

Copy

number

variants

Indels

Mutations

miRNA

expression

Fusions

QIAseq targeted RNA panels

QIAseq miRNA Library

kit

QIAseq targeted

RNAscan panels

QIAseq targeted DNA panels

QIAseq targeted

DNA panels

QIAseq targeted

DNA panels


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Targeted sequencing with UMIs

9

Biomarker

Discovery

DNA

mRNA/lncRNA

Fusion

miRNA

QIAseq Targeted DNA Panels

• UMIs

• Mutation /SNP analysis

• CNV

• Insertions / deletions

• Down to 2 ng fresh DNA

QIAseq Targeted RNAscan Panels

• UMIs

• Known fusion genes (validation)

• Unknown partners (discovery)

QIAseq miRNAseq Kit

• UMIs

• Gel free library prep

• Down to 25 ng RNA

QIAseq Targeted RNA Panel

• UMIs

• Gene expression

• Down to100 pg or 10 cells

All kits utilize SPE (single primer extension)


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Challenges of current DNA targeted sequencing approaches

15

Inability to

detect low-

frequency

mutations

Inefficient

enrichment and

sequencing of

GC-rich

regions

PCR and sequencing errors

• Limits sensitivity and accuracy of calling low-frequency variants

o Doesn’t let you confidently call variants down to 1% variant allele frequency

(VAF)

Suboptimal

uniformity of

enrichment and

sequencing

Suboptimal, GC-rich region-incompatible PCR chemistry

• Limits comprehensiveness of panel coverage

o Doesn’t let you efficiently sequence clinically-relevant genes such as CEBPA or

CCND1 – or clinically-relevant regions such as TERT promoter

Conventional PCR protocols and two-primer amplicon design

• Increases variability in coverage across targeted genomic regions

o Causes you to over-sequence to accommodate the under-sequenced

o Doesn’t let you call variants in low-depth regions

Mainly due to inferior PCR amplification approaches.


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Challenges of conventional targeted DNA sequencing

PCR duplicates limit accurate quantification

Five reads OR library fragments that look exactly the same.

Cannot tell whether they represent:

1. Five unique DNA molecules, or

2. Quintuplets of the same DNA molecule (PCR duplicates)

Conventional targeted

DNA sequencing

EGFR exon 21

Quantification based on non-unique reads does not reflect

quantities of original DNA molecules


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Challenges of conventional targeted DNA sequencing

PCR and sequencing errors (artifacts) limit variant calling accuracy

A mutation is seen in 1 out of 5 reads that map to EGFR exon 21.

Cannot accurately tell whether the mutation is:

1. A PCR or sequencing error (artifact)/false positives, or

2. A true low-frequency mutations

Conventional targeted

DNA sequencing

EGFR exon 21

Variant calling based on non-unique reads does not reflect

the mutational status of original DNA molecules

*


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What is a UMI (molecular barcode)?

A tag (barcode) to identify unique DNA molecules

TATCGTACAGAT(12 nucleotides long)

Incorporate this random barcode (signature)

into the original DNA or RNA molecules before amplification

to preserve their uniqueness


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5′ AATGTACAGTATTGCGTTTTC NNNNNNNNNNNN CGGCAGGAGACGAAGAG 3′

UMI

What is a UMI (molecular barcode)?

QIAGEN uses UIMIs with 12 random bases.

This corresponds to 16.7 million indices per sample.


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How are UMIs incorporated?

Ligate molecularly-barcoded adapters to unique DNA molecules before amplification

DNA

dsDNA

PCR amplification and sequencing

Correct for PCR duplicates and errors

TATCGTACAGAT

Molecularly-barcoded adapter

Incorporate this random barcode (signature)

into the original DNA molecule (before

amplification) to preserve its uniqueness

MB

MB


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Accurate quantification with UMIs

23

Five reads that look exactly the same

Cannot tell whether they represent:

1. Five unique DNA/RNA molecules, or

2. Quintuplets of the same DNA/RNA molecule (PCR duplicates)

Five unique DNA/RNA molecules

detected from 5 molecular bar codes

Quintuplets of the same DNA/RNA molecule (PCR duplicates)

Detected from 1 molecular bar code

UMI

Conventional

targeted DNA/RNA

sequencing

Digital sequencing

with UMIs

UMIs before any

amplification

EGFR exon 21

UMI


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Accurate variant calling with UMIs

24

A mutation is seen in 1 out of 5 reads that map to EGFR exon 21

Cannot accurately tell whether the mutation is:

• A PCR or sequencing error (e.g., artifact or false positive)

• A true low-frequency mutation

False variant is present in some fragments

carrying the same molecular barcode

True variant is present in all fragments

carrying the same molecular barcode

UMI

UMIs before any

amplification

EGFR exon 21

*

* *****

Conventional

targeted DNA/RNA

sequencing

Digital sequencing

with UMIs UMI


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Reduction of false positives

27

Enabled by the unique combination of UMIs and UMI-aware bioinformatics

Variant calling performance on 1% NA12878 variants in panel N0030. X-axis is the number of false positives per megabase and y-axis

is sensitivity. Solid lines, dashed lines, and dotted lines represent smCounter, MuTect, and VarDict respectively. Each point on the

ROC curve represents a threshold value. (a) ROC curves of smCounter, MuTect, and VarDict base on 223 SNVs. (b) ROC curves of

smCounter and VarDict on 49 indels. Note that MuTect does not call indels

smCounter =

UMI-aware variant caller

UMI-based analysis:

90% sensitivity at 10

false positives per Mb

Non-UMI-based analysis:

90% sensitivity at 85

false positives per Mb

Xu et al. BMC Genomics (2017) 18:5


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Actionable DNA variants for precision medicine

Only a handful of mutations are actionable

Actionable DNA variant BRAF V600EEGFR E746-750

+ Kinase domain mutationHER2

Disease Melanoma Lung adenocarcinomas IDC-breast cancer

Therapy Vemurafenib (PLX4032) Erlotinib/Gefitinib Trastuzumab

Reference genome

AGCTCGTTGCTCAGCTC

Insertion

AGCTCGTTGCTCAGCGTTC

Deletion

AGCTC---GCTCAGCTC

Mutations Indels Copy number variations

T

G

CA

TG

A

C

C

G

CA

TG G

C

G

Point mutation


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29

QIAseq Targeted DNA Panel Workflow

DNA variant analysis workflow (Illumina®)

UMI

1.5 Days

Enzyme-based random DNA fragmentation

End repair and A tailing

Library quantification


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Comprehensive coverage of GC-rich regions

CCND1

GC content

Coverage

GC content

Coverage

CEBPA

The proprietary PCR chemistry used in the QIAseq Targeted DNA Panels enables

efficient coverage of regions high in GC content.


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Unmatched uniformity

• A 830 kb region was enriched from 20 ng of NA12878 DNA with the

Comprehensive Cancer Panel

• A library was constructed for sequencing on a MiSeq, with 2600x read depth

• The panel achieved a uniformity of 99.5% at 0.2x of mean coverage, and 98% at

0.3x of mean coverage


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Specifications of QIAseq Targeted DNA Panels

32

DNA input 1–40 ng DNA

Primer multiplexing level 11,500 / 9.600 primers (catalog/custom DNA)

Number of primer pools 1

Enrichment technology SPE-based with molecularly-barcoded adapters

Amplicon size Average 150 bp

Sample multiplexing level 384 (Illumina), 96 (Ion Torrent)

Total workflow time 8–9 hours

Number of libraries per sample 1

Sequencer compatibility Illumina and Ion Torrent platforms

Types of variants detected SNP, indel, CNV

Variant allele frequency called 1% with 40 ng DNA


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QIAseq Targeted DNA panel with different DNA input amounts

• QIAseq Targeted DNA Panel

◦ Actionable Solid Tumor Panel

◦ Comprehensive Cancer panel

33

QIAseq Targeted DNA Panels delivered consistently high sequencing specificity and

coverage uniformity with different DNA inputs.

94,0 94,790,5

97,7 97,8 97,499,5 99,6 99,9 99,2 99,4 99,8

0

20

40

60

80

100

120

101Z-1 ng 101Z-10 ng 101Z-20 ng 3501Z-1 ng 3501Z-10 ng 3501Z-20 ng

Percentage

on target% with primer 0.2x mean% baseMT


Sample to Insight

QIAseq DNA panels on various types of DNA samples

• QIAseq Targeted DNA Panel

◦ Myeloid Neoplasms panel

◦ BRCA1 and BRCA2 Panel

• Blood, bone marrow or FFPE DNA

sample

• QIAseq Targeted DNA Panels delivered

consistently high sequencing

performance across different DNA

sample types

34

50

60

70

80

90

100

110

blood BM BM blood FFPE FFPE FFPE BM

Percentage

on target with primers% 0.2x mean baseMT%

50

60

70

80

90

100

110

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Percentage

FFPE DNA samples

on target% with primer 0.2x mean baseMT%

Myeloid Neoplasms Panel

BRCA1 and BRCA2 Panel


Sample to Insight

RS

QIAseq DNA Panel on buccal swap DNA samples

• QIAseq Targeted DNA Inherited Diseases Panel

◦ 298 genes, 11,579 primers in a single tube, ~838 kb region

• Buccal swab DNA sample (20 ng)

◦ Usually contaminated with bacterial DNA that could pose a challenge for target enrichment

97,93 98,2099,10 99,21

50

60

70

80

90

100

110

1 2

Percentage

Buccal swab DNA

on target% with primer

0.2x mean baseMT%

35

QIAseq Targeted DNA Panels achieved high specificity and coverage uniformity on

buccal swab DNA samples indicating high specificity of primers.

.


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List of panels

Panel

Variant

(Cat) number Number of genes Number of primers Type of coverage

Breast Cancer Panel DHS-001Z 93 4831 1

Colorectal Cancer Panel DHS-002Z 71 2929 1

Myeloid Neoplasms Panel DHS-003Z 141 5887 1

Lung Cancer Panel DHS-005Z 72 4149 1

Actionable Solid Tumor Panel DHS-101Z 23 651 2

BRCA1 And BRCA2 Panel DHS-102Z 2 223 1

BRCA1 And BRCA2 Plus Panel DHS-103Z 6 348 1

Pharmacogenomics Panel DHS-104Z 39 146 3

Mitochondria Panel DHS-105Z Chromosome M 222 4

Inherited Diseases Panel DHS-3011Z 298 11,579 1

Comprehensive Cancer Panel DHS-3501Z 275 11,311 1

1. Exonic regions of genes plus 10 bases to cover intron/exon junctions

2. Type 1 coverage for tumor suppressor genes and hotspots for oncogenes

3. SNPs

4. Full chromosome

Types of coverage:


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QIAseq Targeted DNA panels

List of panels

Panel

Variant

(Cat) number

Panel size

(bases)

Specificity

(reads with primers, %)

Uniformity

(0.2x mean baseMT, %)

Breast Cancer Panel DHS-001Z 370,942 96.47 99.84

Colorectal Cancer Panel DHS-002Z 215,328 90.39 99.79

Myeloid Neoplasms Panel DHS-003Z 436,672 95.31 99.71

Lung Cancer Panel DHS-005Z 318,059 97.3 99.91

Actionable Solid Tumor Panel DHS-101Z 15,160 90.48 99.85

BRCA1 And BRCA2 Panel DHS-102Z 16,405 99.59 100

BRCA1 And BRCA2 Plus Panel DHS-103Z 25,590 99.46 99.92

Pharmacogenomics Panel DHS-104Z 3313 93.43 99.34

Mitochondria Panel DHS-105Z 16,570 99.72 99.08

Inherited Diseases Panel DHS-3011Z 838,627 97.29 99.21

Comprehensive Cancer Panel DHS-3501Z 836,670 97.42 99.76

Uniformity and specificity are defined based on NA12878 tests


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Customized panels

40

Extended and custom

What is the list of your targets?

Extended

panels

Custom panels

• Extend the contents of an existing cataloged panel

• Turnaround time = 14 days

• Bioinformatically target any gene(s) or genomic region(s) within the

human genome

• Turnaround time = 14 days

Targeted DNA sequencing in disease prediction – detecting sequence variants with digital sequencing

Sample to Insight

QIAseq Targeted DNA Custom Panel performance

41

QIAseq Targeted DNA Custom Panels achieved consistent superior performance

without any optimization.

50

60

70

80

90

100

110

1 2 3 4 5 6 7 8 9 10

Percentage

Custom Panel

on target with primer% 0.2x mean baseMT%

Targeted DNA sequencing in disease prediction – detecting sequence variants with digital sequencing

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Overcoming current challenges

42

For optimal variant detection

With the QIAseq Targeted DNA Panels, variant detection is done by analyzing

unique DNA molecules instead of total reads.

Current approach Challenges

How QIAseq Targeted DNA Panels

overcome challenges of current

approaches

• Conventional targeted DNA

sequencing for variant detection

• PCR and sequencing errors • UMIs that enable digital sequencing to

correct for PCR and sequencing errors

• Inefficient sequencing of GC-rich

regions

• Proprietary chemistry to efficiently

sequence GC-rich regions

• Suboptimal uniformity of enrichment

and sequencing

• SPE-based primer design to increase

uniformity


Sample to Insight

QIAGEN’s solutions to overcome challenges of targeted NGS

43


• Proprietary PCR chemistry to enrich even

GC-rich regions

• Primers based on single primer extension

(SPE) approach for enhanced uniformity

• Detects SNP, indel and CNV

Panel box (kit)

• Molecularly-barcoded library adapters to

incorporate unique molecular indices

(UMIs).

Index box (kit)


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QIAGEN’s DNA sequencing solutions

44

Targeted DNA panels for actionable variant identification

Whole genome library preparation using FX technology

Whole genome sequencing from ultra-low input samples

Whole genome sequencing from cell-free DNA

Amplicon sequencing with fast library preparation

Single-cell sequencing

Whole methylome analysisNEW !


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RNA targeted panels for disease prediction

45

Why use targeted RNA panels for gene expression analysis ?


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Central dogma revisited


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Unparalleled efficiency and flexibility vs RT-qPCR

48

An example: 96 samples, 421 genes

Benefits QIAseq targeted RNA panels RT-qPCR

Material required One pool of primers 105 x 384-well plates

Run time 14 hours for NextSeq500 run 310 hours

(2 hours per plate)

Hands-on time 3 hours (for 96 samples) 105 hours

(one hour per plate)

Cost per sample $65 $239

Sample 10 ng each sample 4 ug each sample


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QIAseq Targeted RNAseq - performance

49

Extremely sensitive profiling, avg. 2-5 copy per cell

Highly flexible, from 12 to 1000 or more targets, 1 to 96 samples

High specificity, ~ 97-99% accuracy maintained through all panels

UMIs for absolute quantification

Requires no rRNA depletion or blocking or dT selection

Only 10 ng total RNA

Makes best use of limited NGS budget

System optimized for best possible performance with FFPE samples

Curated knowledge base for gene expression specific panels


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Title, Location, Date 50

Value of QIAseq molecular barcode for RNA targeted panels

50

In a well designed, well optimized, and well executed experiment, data from barcodes and reads ~ same. But with

sample variation, FFPE, oversampling, etc. barcodes give a distinct advantage in precision and reproducibility .

Problem:

sequence-dependent amplification bias and noise

Solution:

Molecular barcode, count unique barcodes instead of number of raw reads

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‘Simple’ procedure, integrated library preparation

51

6 h

ou

rs

GSP1, GSP2

clean up per

primer, thereby

minimizing primer

dimers


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QIAseq sample multiplexing guidelines on NGS platforms

52

Where can you run this?


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QIAseq Targeted Panels

54

Comprehensive Panels (available for 12 or 96 samples)

Cancer Transcriptome (395)

Inflammation & Immunity Transcriptome (475)

Signal Transduction PathwayFinder (406)

Stem Cell & Differentiation Markers (293)

Molecular Toxicology Transcriptome (370)

Angiogenesis & Endothelial Cell Biology (340)

Apoptosis & Cell Death (264)

ECM & Adhesion Molecules (421)


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QIAseq Targeted Panels – content, controls and customization

55

Flexible design, extend your panel

Add 25 of your favorite targets (mRNAs or lncRNAs)

to QIAGEN’s comprehensive panel

lnc13

ADAMT

S9

CAHM

DLEU2

GAS5

GAS6-

AS1

GNAS

LINC00

261

MEG3

MIR31

HG

MIR7-

3HG

NAMAPTCSC1

PTCSC3

TERC

ZFAS1

LINC0

0312DLX6

NEAT

GACA

T1

What is the role of tumor suppressor lncRNAs?


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Custom panels

56

Online custom builder

• Choose your own gene content from 54,881

human mRNA and lncRNA

• Easy to use online Custom Panel Builder to

tailor panel to your research needs

o Input list of genes

o Select proper controls (genomic DNA

contamination control, HKGs, or your

own)

o Output: list of genomic coordinates for

primers designed specifically for your

genes of interest


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Custom builder

57

Download zip file containing:

• Summary file

• Bed file

All your custom designs

are saved for easy retrieval

Have questions?

Easily contact us

Configure and order

Custom panel

number


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Custom builder

58

Gene ID

and

symbol

Strand of the

genome the

gene is onAmplicon

coordinates

Designated

controls

are shown

here

• Single exon (1) means both primers are within one exon

• # Gencode basic RNAs: total number of RNA transcripts found for the gene in Gencode

• # Gencode basic RNAs matched: # of RNA transcripts targeted by the designed amplicon

• # off target genes: rough prediction of # of off target genes that will also get enriched by the

primer pair for the target gene

• Amplicon not genome unique: reads that will not be able to be uniquely mapped to the

genome, so some MT counts might come from another loci


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Custom builder

59

Bed fileLocation of designed amplicon


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Specifications : Differential Gene Expression :

Species coverage

Biological replicates

Human

Mouse + Rat – Custom

Please consider, n=3 important for statistics

Short reads for FFPE, and

exosomal RNA

Average amplicon size 97 nt, range [95-130 nt]

Coverage across the transcript

We count single common exon per gene. Same design

philosophy as RT2 PCR Arrays

Depth of sequencing Capture enough unique tags of each transcript such that

statistical inferences can be made (>10 tags per gene)

Stranded library prep Not required, assays target unique regions

Type of reads (paired or

unpaired?)

Not necessary, 150 base single reads more than enough for

accurate data

mRNA and lncRNAs

QiaSeq was designed against database containing lncRNA and

mRNA. Assays are specific for lncRNA or mRNA. Currently

54,881 genes from Ensembl version 81

63

Experimental Setup & things to consider


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QIAseq workflow: from sample to insight

64

Included in

Panel Kit Included in cloud+ Index Kit+ Library Quant

Kit

Extra : CLC Biomedical workbench with MT plugin


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Small Molecules – Signal Transduction

• HEK293T Cells were treated with 90 different chemical inhibitors.

• The 421 Signal Transduction Gene Qiaseq Panel was investigated.

• In one day we went from total RNA to sequence ready libraries for

96 samples. The final libraries were quantified, normalized, and

pooled.

• Prior to loading onto a NextSeq,the denatured libraries were diluted

to the appropriate input concentration to generate suitable clusters

on the NextSeq.

• The parameters of the NextSeq run were; single 151 bp read, with

a Custom Sequencing Primer (included in kit).

QIAseq Targeted RNA Panel : Application data from customer

cells

treated cells

RNA

Indexed libraries

Normalized, pooled libraries


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HDAC Mechanistic Network in HEK293T Cells Treated with Trichostatin A

86

HDAC is predicted to be inhibited by Trichostatin A and drives a

mechanistic network with 18 other regulators.

Ingenuity Pathway Analysis

Cell cycle

NHR, proliferation Transcriptional

activator


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Our RNA-seq vision to our customers

The importance of miRNA regulation is shown here by targeting genes involved in invasion of cells necessary

for tumor progression and metastasis

87

We tie everything

together with our

different RNA-seq

options & data

analysis


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miRNA

88

… with miRNA

sequencing


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89

miRNA

mRNA

… with mRNA

sequencing


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miRNA

90

mRNA

Molecular

mechanism

… and data

analysis &

biological

interpretation

with IPA


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QIAGEN’s RNA-Seq solutions

92

3’ RNA-Seq transcriptome with sample ultra-plexing incl. (LNA®)

miRNA/piRNA sequencing, gel-free and locked nucleic acids (LNA®) enhancement

Stranded mRNA sequencing, unique workflow and CleanStart PCR

Single-cell RNA-seq, gene expression or RNA isoform analysis

Targeted RNA panels for gene expression

T-cell receptor CDR3 quantification and identification

Fusion gene analysis

NEW !

NEW !


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93

Questions ?


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Extra...

QIAGEN 082017 94

Sample to Insight

QIAseq miRNA Library Kit

The “next-generation” in miRNA sequencing products


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96

QIAseq miRNA overview

What is the kit? miRNA-focused next-generation sequencing library prep kit and integrated

bioinformatics/data analysis solution

What is the product used for? Preparation of mature miRNome libraries from any species

What sequencers are the libraries compatible with? Illumina sequencers

What can be done with the sequencing data?

• Differential expression calculations of miRNA from highly multiplexed samples

• Novel miRNA discovery

• Identification of IsomiRs

What are distinguishing features of the prep kit?

• “Gel-free” prep kit for miRNA sequencing

• Broad RNA input: 500 ng to 1 ng

• Library prep from cells and tissues of any species

• Library prep from serum, plasma and other biofluids

• Integrated Unique Molecular Index (UMI) technology

• Rapid workflow

• Highly optimized chemistry

QIAseq miRNA Library Kit: Unparalleled miRNA-focused

sequencing for accurate digital quantification


Sample to Insight

QIAGEN 082017 97

QIAseq miRNA mapping rates and specs

miRNA mapping rates routinely observed

• Cell lines: 50-60% or greater

• Tissues: 75% or greater

• Serum/plasma: 15-30% or greater

Specs

• Sample type: Cells, fresh/frozen tissue, FFPE tissue, serum/plasma, biofluids

• Animal and plant samples

• Any species

• Total RNA input range (cells/tissues): 500 ng to 1 ng

• Total RNA input recommendation (serum/plasma): 5 µl when RNA has been isolated from

200 µl of sample

• What RNAs are included in library prep? Highly optimized for miRNA

• piRNAs will also be efficiently sequenced

• Multiplex capability: 48 samples

• Sequencer compatibility: Illumina

• Total library construction time: 8 hours

Sample to Insight

Amplify

& Sequence

98

Unique Molecular Index (UMI) principle

Original Sample

miRNA 1

miRNA 2

RT

with UMI

Raw Reads

• Original sample (3:2 ratio of “miRNA 1” to “miRNA 2”)

• miRNA 1: 3 molecules

• miRNA 2: 2 molecules

• Interpretation of “raw reads” (2:1 of “miRNA 1” to “miRNA 2”)

• miRNA 1: 12 reads

• miRNA 2: 6 reads

• Interpretation of “UMIs” (3:2 ratio of “miRNA 1” to “miRNA 2”)

• Reads are collapsed based on “molecule counts”

• miRNA 1: 12 reads BUT 3 molecules are identified due to UMIs

• miRNA 2: 6 reads BUT 2 molecules are identified due to UMIs

Instead of number of reads, the number of unique UMIs are counted, which

accurately reflects the original status of the transcript


Sample to Insight

99

5’ PO4 3’ miRNA

Step 1: 3’ ligation3’Pre-adenylated

adapter

Step 3: Reverse-transcription

with Unique Molecular Index

(UMI) assignment

5’3’

Step 4: QMN Bead prep

Step 5: cDNA cleanup

RT primer with UMI

5’ Step 2: 5’ ligation3’

5’ 3’

5’5’ 3’3’

Step 6: Library amplification

and Sample Index assignment5’ 3’3’ 5’

Universal For

Rev with Index

5’ PO4

QIAseq miRNA: Save a Day of Workflow-8 Hours

Step 7: Library cleanup

Steps 8-11: Library Pre-Seq QC, Determining Library Conc, Prep for Seq, Data Analysis

Elimination of adapter dimer

from sequencing library


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QIAGEN 082017 100

Data Analysis

• Primary Analysis: http://ngsdataanalysis.sabiosciences.com/QIAseqmiRNA/

• Well-characterized species:

• UMI (Molecular Tag: MT) counting and mapping (species-specific miRBase, genome)

• Poorly-characterized species:

• UMI (MT) counting and mapping (“all of miRBase”)

• Secondary Analysis: http://qiagen.com/GeneGlobe

• Differential expression analysis

• Multiple normalization methods offered that are routinely used for miRNA analysis

• geNorm

• Total Molecular Tag Count

• DESeq2

• Trimmed Mean of M (edgeR)

read set Sample 1

total_reads 36,373,962

no_adapter_reads 528,950

too_short_reads 3,217,741

MT_defective_reads 355,202

miRNA_Reads 29,847,956

hairpin_Reads 6,598

piRNA_Reads 320,882

rRNA_Reads 432,801

tRNA_Reads 328,813

mRNA_Reads 181,949

otherRNA_Reads 506,069

notCharacterized_Mappable 386,804

notCharacterized_notMappable 260,197

Targeted RNA and DNA sequencing in disease prediction ...• Down to 2 ng fresh DNA QIAseq Targeted...

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Transcript of Targeted RNA and DNA sequencing in disease prediction ...• Down to 2 ng fresh DNA QIAseq Targeted...