This document provides an overview of next generation sequencing (NGS), including its history, key methods, applications and challenges. It discusses that NGS allows for massively parallel sequencing of thousands of DNA molecules simultaneously, providing exact sequences of genes. Some main NGS platforms and their sequencing methods are described, such as Illumina sequencing by synthesis. Applications of NGS discussed include cancer research, pharmacogenetics and preimplantation genetic diagnosis. Challenges associated with NGS like poor quality FFPE samples and large data analysis workflows are also outlined.

1. Next Generation
Sequencing
Joshua Lee Voon Kai (0333356)
Tee Zhong Ee (0327975)
Sing (0328048)
Cheong Pui Yi (0326329)
Hsing Lu (0331441)
Julian Tay (0328917)

2. Second
G.S.
Sanger
Sequencing
First G.S.
The Discovery of Next Generation Sequencing (NGS)
“to determine the sequence of a DNA molecule(s) with total size significantly larger than 1 million base
pairs in a single experiment ” (Ploski, 2016).
Third
G.S.
Discovered
- 1977
- Frederick Sanger
- Walter Gilbert
Based on dideoxy
technique
Developed into chain-
termination method, also
known as Sanger
sequencing.
Derived from Sanger
Sequencing
- 1986
dNTP and ddNTP are used
for electrophoresis
technology.
Created by implementing
several new methods into
FQS
Eg.
- Roche/454 platform in
2005
- Solexa/Illumina system in
2006,
Launched
- in 2000
- by Lynx
Therapeutics (USA)
Company.
Launched by Helicos
BioSciences
Achieved due to advances
in automated single
molecule imaging and
fluidics technologies
Lower cost than Sanger
sequencing.
Highly time efficient.
(Barba, Czosnek and Hadidi, 2014)
(Heather and Chain, 2016)
(Ambardar et al., 2016)(Totomoch-Serra, Marquez and Cervantes-Barragán, 2017)
(Ozsolak, 2012)(Kamps et al., 2017)

3. Type of protocols !
Types of Sequencing Method
Pyrosequencing
Sequencing by synthesis
Sequencing by ligation
Ion semiconductor
sequencing
Template Preparation (Amplify)
Emulsion PCR
Bridge PCR
Common steps
1. Template Preparation
2. Sequencing
3. Data Analysis
Goal: Sequence thousands of DNA molecules simultaneously and get the exact sequence of
genes
Example: Roche/454 FLX, Illumina/ Solexa Genome
Analyzer, Applied Biosystems (ABI) SOLiD Analyzer,
Polonator G.007 and Helicos HeliScope
(ABM, n.d.)

4. Illumina Genome Analyzer
● Most widely used system
● Fast approach
● Run multiple sample simultaneously
● Incorporate 1 single nucleotide at a time (1 by 1)
All nucleotide
added. Only 1 will
bind because of the
terminator group
Fluorescence
molecule and
terminator
group cleaved
and washed
away
Repeat until the
sequencing
reaction is
complete
Terminator
group
Fluorescence
dye
Fluorescence
signal is read
at each cluster
and recorded
ACGA…………..
Sequencing by synthesis
(ABM, n.d.)
Model: Illumina Hi Seq 4000

5. Massive parallel
sequencing
Detection of somatic
cells’ genetic changes
High sensitivity &
detection rate
- Allowing million of
sequencing
reactions to happen
at the same time
- Broad spectrum of
mutation detection
(Arsenic et al. 2015)
- The heterogenous of
tumour makes somatic
cell mutation hard to be
detected
- NGS is able to
overcome this
conundrum
(Arsenic et al. 2015)
- Some tumours are
attributed to
mutations at low
variant frequency
alleles
- NGS is able to detect
mutation at low
frequency alleles
(Serrati et al.
Key features of NGS in cancer research

6. Sanger Sequencing (First
Generation Sequencing)
Next Generation Sequencing
Efficacy was limited due to the
inabilities of performing parallel
investigation of multiple genes (Arsenic et al.
2015)
Fast and efficient by performing massive
parallel sequencing (Arsenic et al. 2015)
Somatic cancer mutation can only be
recognized with performing
microdissection (Arsenic et al. 2015)
Both somatic and germline mutation can
be detected (Arsenic et al. 2015)
Low sensitivity to mutation occuring at
an allele frequency lower than 20%
(Arsenic et al. 2015)
High sensitivity and detection rate in
spite of the low frequency allele
(Arsenic et al. 2015)
NGS vs Sanger in cancer research

7. Assist in therapeutic
decision making
Panitumumab treatment
prolonged progression
free survival in KRAS-
WT patients compared to
KRAS-mutant patient
Chemotherapy
applications
Colorectal cancer (CRC)
Affects colon and rectum
Third most common type
of cancer
Great quantity of
activating mutations
CRC
Fast high throughput
and cost effective
technology
Can accurately identify
mutation in known
genes
9 genes from 320
samples
NGS
Detected mutations in KRAS, NRAS , BRAF, PI3KCA, PTEN, TP53, EGFR, AKT, CTNNB1
(Vecchio et al, 2017)

8. ctDNA as a non-invasive
method cancer
biomarker
real time cancer
detection, screen for
diseases, monitoring
therapeutic responses
NGS has high enough
specificity and
sensitivity
Liquid biopsies
Current development
Prevent inherited cancer
syndrome
PGD and IVF used in
combination to detect
aberrations in
blastomere
NGS to detect
chromosomal
aberrations across
entire genome
Preimplantation
genetic diagnosis
Rare variants exerts
detrimental effects drug
metabolising enzymes
NGS is capable of
analysing high number
of genes and identifying
variants
More useful than
analyses single gene
Pharmacogenetics
(Kamps et al, 2017)
(Giannopoulou et al, 2019)
(Kamps et al, 2017)
(Illumina, n.d.)

9. Challenges: Process
● Poor FFPE sample quality:
Preservation Method
● FFPE: Popular in cancer research¹
● ↓ DNA yield & Fragment size -
difficulty in constructing high quality
libraries (important for analysis)
● False positives & artefacts
● Quality variability¹ → ↑ QA,
streamline/automate/optimize²,³→
Improve DV200
(Sample isolation --> library construction)
● Variance of Uncertain Significance
(VUS)
● Normal variation or expected to
cause disease symptoms?
● Correlation of disease to VUS unclear
● Example: BRCA1 has other
associations⁴ → take time to identify
combinations
● Testing healthy patients will be
uninformative, widely-publicized
case of Elisha Cooke-Moore⁵
(Interpretation stage)
¹(Gaffney et al. 2018) ²(Einaga et al. 2017) ³(Fisher et al. 2011) ⁴(Rebbeck et al. 2015) ⁵(Jamie Ducharme 2017)

10. Challenges: Sequence Data Analysis Workflows
(Kulkarni and Frommolt, 2017)
Large Generated Data
Use Centralized
Processing Methods
Scattered Human
Genomics Sequences
International
collaboration efforts
Complex genomic
variation, dynamics and
pathology, VUS
Machine learning
deep neural
networks

11. - NGS helps to conquer the
challenges of low tumour
quality, heterogeneity of
tumour. (Meyerson, Gabriel & Getz 2010)
- In future, smaller samples are
likely to be diagnosed by using
NGS (Meyerson, Gabriel & Getz 2010)
Render remarkable impact in
cancer treatment
Conclusion - NGS
- Able to run multiple
samples at the same time
- High sensitivity and
detection rate
- Cost-effective
- Provide comprehensive
genomic information
(Arsenic et al. 2015)
Effective method to
detect mutation

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