Tumor heterogeneity has been known for a while but quantifying heterogeneity is still a challenge. NGS is the method of choice in the analysis of tumor heterogeneity, however, there are some inherent challenges associated with it. These include false positives, gaps in the gene due to overrepresentation and incomplete representation of low-frequency transcripts – all contributing to an inaccurate picture. Conventional library prep strategies for NGS are based on PCR, which introduces sequence-based bias and amplification noise, leading to these inaccuracies. In this webinar, we will cover
1. Principles of UMI and the new QIAseq product porfolio
2. How UMI along with SPE (single primer extension) allows for increased uniformity across difficult-to-sequence regions, removal of library construction bias, improved data analysis and sequencing optimization
3. How data generated from using UMI and SPE is directly comparable to analysis derived from whole transcriptome and exome sequencing
4. Application of UMI and SPE in the discovery of novel gene fusions and in the analysis of gene expression and genetic variation
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QIAseq Targeted DNA, RNA and Fusion Gene Panels
1. Sample to Insight
QIAseq Targeted Sequencing
1PowerPoint Style Guide, 07.10.2015
Samuel Rulli, Ph.D.
Global Product Manager
QIAGEN
2. Sample to Insight
Legal disclaimer
2
• 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.
3. Sample to Insight
Tumor heterogeneity
3
• Tumors are notorious for being a mixed population of cancer cells and
infiltrating cells
• There is often a limited amount of sample available
• FFPE samples are a necessary part of most cancer research programs
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Tumor heterogeneity
4
• Tumor heterogeneity leads to
◦ Highly variable cancers
◦ Differential response to treatment
– Targeted
– Non-targeted
◦ A need to be monitored over time,
especially during treatment
• Low-frequency gene mutations are
as important as high-frequency
ones
• The challenge is to identify these
low frequency events
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Using targeted sequencing to understand tumor heterogeneity
5
• Targeted sequencing is uniquely positioned to address these problems
• Needs a small amount of sample input
• Robust even when using FFPE or damaged samples
• Accessible to researchers with bench-top NGS instruments
• Simplified bioinformatics
Variant?
Fusion?
GEX?
miRNA?
Variant?
Fusion?
GEX?
miRNA?
Variant?
Fusion?
GEX?
miRNA?
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Contents
6
Principles of unique molecular indexes (UMIs)1
Single primer extension (SPE) vs. PCR for library construction2
UMIs and SPE in action – gene expression analysis3
DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4
Summary/questions5
7. Sample to Insight
Contents
7
Principles of unique molecular indexes (UMIs)1
Single primer extension (SPE) vs. PCR for library construction2
UMIs and SPE in action – gene expression analysis3
DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4
Summary/questions5
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Principles of unique molecular indexes (UMIs)
8
PCR duplication and amplification bias are major issues in current RNAseq
workflows, as they result in biased and inaccurate gene expression profiles
mRNA
copies cDNA
Original gene
ratio status
mRNA ratio
based on
reads
(reads ratio)
Gene A
Sample 1
Gene A
Sample 2
Raw reads
4
1
Number
of reads
1
2
6
12
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Targeted RNAseq is a “read-based” approach to understanding
gene expression
How do we go from “reads” to counting transcripts?
Principles of unique molecular indexes (UMIs)
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Targeted RNAseq is a “read”-based approach to understanding
gene expression
How do we go from “reads” to counting transcripts?
Principles of unique molecular indexes (UMIs)
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Principles of unique molecular indexes (UMIs)
11
mRNA
copies cDNA
Original gene
ratio status
mRNA ratio
based on
reads
Gene A
Sample 1
Gene A
Sample 2
UMI reads
4
1 1
2
Molecular indexes allow the counting of original transcript levels
instead of PCR duplicates, thereby enabling digital sequencing and
resulting in unbiased and accurate gene expression profiles
Tag each transcript
with UMIs
mRNA ratio
based on
UMIs
1
4
Count UMIs,
not reads
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During mRNAseq, each capture event is archived with an UMI
12 random bases
16.7 million indexes
The strategy for measuring gene expression uses UMI-gene-specific primer
The strategy for measuring DNA variant and fusion gene is slightly different, but
the principle is the same.
Principles of unique molecular indexes (UMIs)
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Contents
13
Principles of unique molecular indexes (UMIs)1
Single primer extension (SPE) vs. PCR for library construction2
UMIs and SPE in action – gene expression analysis3
DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4
Summary/questions5
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Single primer extension
14
5’ 3’
3’ 5’
cDNA
5’ 3’
5’
Captures information
Single primer
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Advantages of SPE:
• Needs only a single region for primer design
◦ Unlocks entire transcriptome, genome and fusion genes
◦ Having to use half the number of primers lowers cost and allows for greater
content during multiplexing
• Able to adapt to G/C-rich and difficult-to-PCR regions
◦ Allows you to sequence almost everything
• Uniform reaction
◦ Uniform library construction – uniform sequencing
• Works very well on FFPE, fragmented and low quality samples
Disadvantages of SPE:
• Extra step in library construction
◦ May add 1 hour to total workflow
Single primer extension
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Contents
16
Principles of unique molecular indexes (UMIs)1
Single primer extension (SPE) vs. PCR for library construction2
UMIs and SPE in action – gene expression analysis3
DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4
Summary/questions5
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Small molecules/signal transduction application
Experiment: identify novel compounds that modulate known signal
transduction pathways
Cells
Treated
cells
RNA
UMIs and SPE in action: a gene expression example
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Small molecules/signal transduction application
• Cells are treated with different chemical inhibitors
• RNA is isolated
• Libraries are built using QIAseq Targeted RNA Panels
◦ Human Signal Transduction Panel – 421 targets/10 ng total RNA
Cells
Treated
cells
RNA
UMIs and SPE in action: a gene expression example
Experiment: identify novel compounds that modulate known signal
transduction pathways
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6hours
GSP1, GSP2 are
used at
different stages
They never
interact, which
minimizes primer
dimers
UMIs and SPE in action: a gene expression example
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UMIs and SPE in action: a gene expression example
Included in
panel kit
Library
Quant Kit
Included
in cloud
Index
Kit
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UMIs and SPE in action: a gene expression example
Included in
panel kit
Library
Quant Kit
Included
in cloud
Index
Kit
CLC Biomedical workbench with MT plugin
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Customer criteria Differential gene expression by QIAseq NGS
Species coverage
Human – catalog, extended, virtual and custom panels
Mouse and rat – custom
Biological replicates Essential for robustness of experimental design (and statistics!)
Short reads for FFPE and
exosomal RNA
Average amplicon 97 bps’; range 95-130 bases
Coverage across transcript
(i.e. cover every exon)
We are counting single common regions per gene. Same design philosophy as RT2
PCR Arrays
Depth of sequencing
High enough to infer accurate statistics determined by UMI: ~2-5 reads per UMI is
enough.
Role of sequencing depth
Capture enough unique tags of each transcript such that statistical inferences can be
made (>10 tags per gene)
Stranded library prep Not required, amplicons do not overlap lncRNA
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. Assay are
specific for lncRNA or mRNA. Currently 54,881 genes from Ensembl version 81
23
UMIs and SPE in action: a gene expression example
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Free circulating nucleic acids
RNA and DNA from dead cells shed
into the bloodstream, can contain cancer-related
mutations.
Exosomes
Tiny microvesicles found in body fluids that transport
RNA between cells.
Circulating tumor cells
Tumor cells shed from a tumor into the bloodstream
carrying genetic information.
24
Tissue samples
Fresh tissue or archived FFPE samples
QIAGEN’s comprehensive sample isolation portfolio is compatible with QIAseq RNA
Kits and allows you to use as little as 100 pg (10 cells) to 25 ng RNA
UMIs and SPE in action: a gene expression example
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Catalog panel options:
QIAseq Targeted RNA Virtual Panels (available for 12, 96 or 384 samples)
Each panel contains 84 genes + controls and house keeping genes
Choose from over 180 panels!
DiseasesPathways miRNA Targets
UMIs and SPE in action: a gene expression example
Flexible experiment design for any research interest
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Online custom builder
• Choose your own gene
content from 54,881 human
genes and lncRNAs
• Easy to use online Custom
Panel Builder to tailor panel
to your research needs
◦ Input list of genes
◦ Select proper controls
(genomic DNA contamination
control, HKGs or your own)
◦ Output: list of genomic
coordinates for primers
designed specifically for your
genes of interest
UMIs and SPE in action: a gene expression example
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Download zip file containing:
• Summary file
• Bed file
All your custom designs
are saved for easy retrieval
Have questions?
Contact us easily
Configure and order
Custom panel
number
UMIs and SPE in action: a gene expression example
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Custom builder
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Gene ID
and
symbol
Genome
strand on
which gene
is located Amplicon
coordinates
Designated
controls
• Single exon (1): 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 number 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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QIAGEN: Automating Sample to Insight
Real time
PCR + HRM
PCR
Fragment
Analysis
Pyro-
sequencing
Hybrid
capture
Bench top
assay setup
Integrated
assay setup
Low-throughput
High-throughput
Sample
disruption Purification
Assay
setup
Detection and analysis
Medium-
throughput
Quality
Control
33
GeneReader
NGS
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QIAGEN: Automating Sample to Insight
Purification
Quality
Control
34
Cells in
96 well
plates
RNA isolation
from 96 samples
RNA Integrity
(96 samples
done
automatically
while at lunch!)
RNA
quantification
(16 samples per
90 secs)
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QIAGEN: Automating Sample to Insight
Purification
Quality
Control
35
Cells in
96 well
plates
RNA isolation
from 96 samples
Sample
disruption
Assay
setup
Detection and analysis
Library
quantification
Library
integrity
RNA Integrity
(96 samples
done
automatically
while at lunch!)
RNA
quantification
(16 samples per
90 secs)
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Small molecules/signal transduction application
Cells
Treated
cells
RNA
QIAseq targeted application data
Normalized, pooled libraries
Indexed libraries
• HEK293T cells were treated with 90 different chemical inhibitors
• The 421 Signal Transduction Gene QIAseq Panel was
interrogated
• 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 sequencing run were:
◦ A single 151 bp read
◦ A custom sequencing primer (included in kit)
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Primary data analysis for QIAseq targeted RNA sequencing
37
QIAseq targeted RNA data analysis automated workflow
• Read Mapping
◦ Identify the possible position of the read within the
reference genome
◦ Align the read sequence to reference sequences
• Primer Trimming
◦ Remove primer sequences from the reads
• UMI Counting
Go get coffee!
Read
mapping
Primer
trimming
UMI count
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Small molecule application data
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Primary data analysis for QIAseq targeted RNA sequencing
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Small molecule application data
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Primary data analysis for QIAseq targeted RNA sequencing
• QIAseq RNA quantification - read details: unique captures per target gene count
Differential gene expression, inter- and intra-samples
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Controls: take the guesswork out of your analysis!
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Built-in controls
• Assays to control for any
gDNA contamination in
the RNA sample
• Mean tags per target
calculated and mRNA
counts near this number
flagged during analysis
as ‘close to noise level’
• Multiple HKG assays
normalize data to make
sample-to-sample and
run-to-run comparisons
possible
• Flexible – use none, one,
two or any other number
of genes to normalize
• HKG efficacy evaluation
built into secondary data
analysis
HKG
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QIAseq secondary data analysis setup
What kinds of things get flagged?
Low tag #, high gDNA, poor normalizer performance
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Changes in gene expression due to chemical perturbation were quantified by
QIAseq RNA NGS and characterized
Secondary data analysis for QIAseq targeted RNA sequencing
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Scatter plot and clustergram (HDAC sample compared to control)
Secondary data analysis for QIAseq targeted RNA sequencing
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HDAC mechanistic network in HEK293T cells treated with trichostatin A
44
HDAC is predicted to be inhibited by trichostatin A and drives a
mechanistic network with 18 other regulators
Ingenuity IPA analysis
Cell cycle
NHR, proliferation Transcriptional
activator
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QIAseq sample multiplexing guidelines on NGS platforms
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Where can you run these panels?
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Unparalleled efficiency and flexibility vs PCR
46
An example: 96 samples, 421 genes
Parameter QIAseq Targeted RNA Panels RT-PCR
Material required One pool of primers One hundred and five 384-well
plates
Run time 14 hours for NextSeq run 310 hours
(2 hours per plate)
Hands-on time 3 hours (for 96 samples) 105 hours
(one hour per plate)
Sample 10 ng each sample 4000 ng each sample
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Contents
47
Principles of unique molecular indexes (UMIs)1
Single primer extension (SPE) vs. PCR for library construction2
UMIs and SPE in action – gene expression analysis3
DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4
Summary/questions5
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Biomarkers
DNA
mRNA/
lncRNA
Fusion
miRNA
QIAseq Targeted DNA Panels
• Unique molecular index
• Mutation /SNP analysis
• CNV
• Insertions/deletions
• 2 ng fresh DNA
QIAseq Targeted RNAscan Panels
• Unique molecular index
• Known fusion genes (validation)
• Unknown partners (discovery)
QIAseq miRNAseq Kits
• UMIs
• Gel free library prep
• Complete miRNome
QIAseq Targeted RNA Panels
• UMIs
• Gene expression
• Start with 100 pg/10 cells
DNA variant analysis and novel gene fusion discovery
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PCR and sequencing errors (artifacts) limit variant calling accuracy
Not possible to discern whether the mutation is:
1. A PCR or sequencing error (artifact/false positive)
OR
2. A true low-frequency mutation
Traditional
targeted DNA
sequencing
EGFR exon 21
*
Variant calling based on non-unique reads does not
reflect the mutational status of original DNA molecules
Applies to a wide range of panels
DNA variant analysis and novel gene fusion discovery
A mutation is seen in 1 out of 5 reads that map to EGFR exon 21
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Digital sequencing = count and analyze each original molecule (not total reads)
Not possible to discern between:
1. Five unique DNA molecules
OR
2. Quintuplets of the same DNA molecule (PCR artifact)
Traditional
targeted DNA
sequencing
EGFR exon 21
DNA variant analysis and novel gene fusion discovery
Five reads or library fragments that look exactly the same
Five unique DNA molecules
since five UMIs are detected
Quintuplets of the same DNA molecule (PCR
duplicates) since only one UMI is detected
UMI
Digital
sequencing
with UMIs
Add UMIs
before amplification
UMIs
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Digital sequencing = count and analyze each original molecule (not total reads)
Traditional
targeted DNA
sequencing
EGFR exon 21
DNA variant analysis and novel gene fusion discovery
Add UMIs
before amplification
*
A mutation is seen in 1 out of 5 reads that map to EGFR exon 21
Not possible to discern whether the mutation is:
1. A PCR or sequencing error (artifact/false positive)
OR
2. A true low-frequency mutation
A false variant is present in only some
fragments with the same UMI
A true variant is present in all
fragments with the same UMI
UMI
* *****
Digital
sequencing
with UMIs
UMI
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QIAseq Targeted DNA Panel Workflow
DNA variant analysis and novel gene fusion discovery
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RPS6KB1-VMP1
ARFGEF2-SULF2
QIASeq Targeted RNAscan is a RNA target
enrichment method that allows verification of
known fusions and discovery of novel fusions
with next-generation sequencing (NGS).
DNA variant analysis and novel gene fusion discovery
55
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Contents
57
Principles of unique molecular indexes (UMIs)1
Single primer extension (SPE) vs. PCR for library construction2
UMIs and SPE in action – gene expression analysis3
DNA variant analysis and novel gene fusion discoveries with UMIs and SPE4
Summary/questions5
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Summary: biomarkers come in many flavors
58
Biomarkers
Gene
expression
Copy
number
variants
Indels
Mutations
miRNA
expression
Fusions
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QIAseq solutions to detect several kinds of biomarkers using NGS
59
Biomarkers
Gene
expression
Copy
number
variants
Indels
Mutations
miRNA
expression
Fusions
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NGS
Summary: NGS can be used for several kinds of biomarkers
Biomarkers
Gene
expression
Copy
number
variants
Indels
Mutations
miRNA
expression
Fusions
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Plug & play: many flavors, same Sample-to-Insight workflow
61
Different panels can be plugged into the same targeted NGS workflow
Sample Insight
DNA panels for
variant analysis
RNA panels for
differential gene
expression
RNA panels for
fusion gene
profiling
miRNome panel for
miRNA expression
Mutations, indels,
copy number variants
Gene expression levels
Fusions
miRNA levels
Sample
isolation
Library
construction
& targeted
enrichment
NGS run
Data
analysis
Interpretation
It is generally agreed that cancer stem cell model must coexists with other sources of tumor heterogeneity including clonal evolution, heterogeneity in micro-environment, and reversible changes in cancer-cell properties that can occur independently of hierarchical properties
What is not clear is what extent is metastasis, therapy resistance and disease progression reflect the intrinsic properties of the cancer stem cells as apposed to genetic evolution or other sources of heterogeneity.
What is certainly clear is that we need to develop integrated multiple experimental approach to distinguish the relative contributions of these different sources of heterogeneity to disease progression.
For easier applications in real world, these have to be easy to implement, robust and
17
18
Some features of quantitative RNAseq
Replicates (same as any proper expression experiment)
Can count using a small region
Depth has to allow statistical accuracy, but much shallower than transcriptome
Strandedness is not needed – assays target unique regions
Paired end reads not required but nice to have – must read from universal end or through it to capture barcode.
36
40
A variant identified in a sample represents one of two events: a true or false variant. False variants can be introduced at any step during the workflow, including sequencing reactions. This results in the inability to accurately and confidently call rare variants (those present at 1% of the sample). Due to PCR duplicates generated in amplification steps, all DNA fragments look exactly the same, and there is no way to tell whether a specific DNA fragment is a unique DNA molecule or a duplicate of a DNA molecule. With molecular barcodes, since each unique DNA molecule is barcoded before any amplification takes place, unique DNA molecules are identified by their unique barcodes, and PCR duplicates carrying the same barcode are removed, thereby increasing the sensitivity of the panel.
A variant identified in a sample represents one of two events: a true or false variant. False variants can be introduced at any step during the workflow, including sequencing reactions. This results in the inability to accurately and confidently call rare variants (those present at 1% of the sample). Due to PCR duplicates generated in amplification steps, all DNA fragments look exactly the same, and there is no way to tell whether a specific DNA fragment is a unique DNA molecule or a duplicate of a DNA molecule. With molecular barcodes, since each unique DNA molecule is barcoded before any amplification takes place, unique DNA molecules are identified by their unique barcodes, and PCR duplicates carrying the same barcode are removed, thereby increasing the sensitivity of the panel.
A variant identified in a sample represents one of two events: a true or false variant. False variants can be introduced at any step during the workflow, including sequencing reactions. This results in the inability to accurately and confidently call rare variants (those present at 1% of the sample). Due to PCR duplicates generated in amplification steps, all DNA fragments look exactly the same, and there is no way to tell whether a specific DNA fragment is a unique DNA molecule or a duplicate of a DNA molecule. With molecular barcodes, since each unique DNA molecule is barcoded before any amplification takes place, unique DNA molecules are identified by their unique barcodes, and PCR duplicates carrying the same barcode are removed, thereby increasing the sensitivity of the panel.