The independent study on how Cloud Computing can be used to introduce a new Next Generation Sequencing method in terms of better understanding of the limitations of existing Next Generation Sequencing Methods.

1. The Effect of Cloud
Computing in Next
Generation Sequencing
By : Supeshala Madushani
144116F

2. Introduction
 At present, DNA sequencing methods have been widely emerged
and they are collectively known as next generation sequencing
methods.
 Next generation sequencing has the ability to dramatically accelerate
biological researches by analyzing genomes cheaply and quickly
rather than requiring significant production-scale efforts.
 This results in the production of huge sequence datasets.
 Meanwhile, cloud computing has become a prominent technology in
many more areas.

3. Problem Addressed
 The production of large data sets in next generation sequencing
leads to an additional computational challenges in data mining and
sequence analysis.
 This represents a significant overburden so that high quality
sequencing techniques must be considered.
Focus: Analyzed the existing literature and find next generation
sequencing methods, its applications, cloud computing and its
applications in biological systems and the effect of cloud computing in
next generation sequencing.

4. Background
Fact 1: In 2003, successfully completed the sequencing of human genome
project.
Fact 2: Sanger sequencing technology used to sequence the human
genome.
Fact 2: It required over a decade to deliver the final draft.
Fact 3: In contrast, using next generation sequencing an entire genome can
be sequenced within a single day.

5. Focus of Research
Next Generation
Sequencing Methods
Cloud Computing
Effects of cloud
computing in next
generation
sequencing
Understand the
features of cloud
computing and its
applications in
biological systems
Understand existing
next generation
sequencing methods
and applications

6. Next Generation Sequencing Methods
1. First Generation Sequencing
Sanger Sequencing
2. Second Generation Sequencing
Roche 454 System
AB SOLiD System
Illumina Genome Analyzer
3. Third Generation Sequencing
Heliscope TM Single Molecule Sequencer
Single Molecule Real Time Sequencer
Progress So Far:

7. First Generation Sequencing
• Fact 1: In 1975, Sanger introduced the concept of DNA sequencing.
• Fact 2: Later on, published a rapid method for determining sequences in DNA
by prime synthesis with DNA polymerase enzyme.
• Fact 3: In 1977, two landmark articles for DNA sequencing were published.
1. The Frederick Sanger’s enzymatic dydeoxy DNA sequencing technique based on the
chain terminating method.
2. Allam Maxam and Walter Gilbert’s chemical degradation DNA sequencing
technique.

8. Next Generation Sequencing
Fact 1: In 2000, Jonathan founded 454 Life Sciences, which further developed
the first commercially available NGS platform, the GS 20.
Fact 2: The developed technique was successfully validated by combining
single-molecule emulsion PCR with pyrosequencing.
• In general, the principle of pyrosequencing technique is based on the
“sequencing by synthesis”.
• It differs from Sanger sequencing because, it depends on the detection of
pyrophosphate release on nucleotide incorporation, rather than chain
termination with dideoxynucleotides.

9. Second Generation Sequencing
• The second generation HT-NGS platforms can generate about five hundred
million bases of raw sequence (Roche) to billions of bases in a single run.
• The second generation NGS platforms are based on template preparation,
massively parallel clonal amplification and sequencing and alignment of
short reads.
• There are three widely used NGS platforms.
1. Roche/ 454 system
2. ABI SOLiD system
3. Illumina genome analyzer

10. Second Generation Sequencing
Roche/ 454 System
• Emulsion PCR
• One DNA fragment per
bead
• Pyro sequencing
• Read length 400-500
ABI SOLiD System
• Emulsion PCR
• One DNA fragment per
bead
• Sequencing by ligation
• Read length ~50
Illumina System
• Solid amplification
• One DNA fragment per
cluster
• Sequencing synthesis
• Read length ~100

11. Comparison of second generation techniques

12. Using second generation techniques for DNA
Sequencing
Progress So Far:
 The principle was based on the emulsion PCR amplification of the DNA
fragments, to make the light signal strong enough for reliable base detection
by the cameras.
 Problem: It may introduce base sequence errors or favor of certain
sequences over others, thus changing the relative frequency and abundance
of various DNA fragments that existed before amplification.
SOLUTION: Determine the sequence directly from a single DNA
molecule, without the need for PCR amplification.

13. Third generation NGS Platforms
• The sequencing from a single DNA molecule is known as “third generation
NGS sequencing”.
• Progress So Far:
• Heliscope TM Single Molecule Sequencer
• Single Molecule Real Time Sequencer
• Nanopore DNA Sequencing

14. Comparison of Second and Third Generation
Techniques

15. Applications of Next Generation Sequencing
1. Full-genome resequencing or more targeted discovery of mutations or
polymorphisms.
2. Mapping of structural rearrangements, which may include copy number
variation, balanced translocation breakpoints and chromosomal inversions.
3. RNA-sequencing, analogous to expressed sequence tags or serial analysis of
gene expression.
4. Large- scale analysis of DNA methylation, by deep sequencing of bisulfite-
treated DNA.
5. Genome wide mapping of DNA-protein interactions, by deep sequencing of
DNA fragments.

16. Cloud Computing
Fact 1: Clouds can be categorized into three types based on the availability of
the data center.
• Public clouds- clouds owned and operated by third parties aiming at
individual client satisfaction by providing services at lower cost.
• Private clouds- clouds that are owned and operated by enterprises for their
own use and benefits.
• Hybrid clouds- the combination of both public and private clouds.

17. Cloud Computing
Fact 2: The services provided under cloud computing can be categorized into
three groups.
Software as a service (SaaS): software is served on demand to the clients.
Multiple users are serviced using single software without investing in
licenses.
Platform as a service(PaaS): A working platform is provided as a service by
encapsulating the required software and working environment to the
provider.
Infrastructure as a service(IaaS): provides computing capabilities and basic
storage over the network.

18. Applications of Cloud Computing in Biological
Systems
1. Genome Analysis and SNP detection
2. Comparative genomics
3. Genome informatics
4. Metagenomics

19. Cloud Computing in Next Generation Sequencing
Literature:
There are cloud computing frameworks which can be used in next generation
sequencing methods.
 Hadoop- This is a framework developed to process high volume of data by
running a huge number of machines which run in simultaneously in a
cluster and various bigdata cloud computing platforms are evolved to
reserve and examine tremendous amount of data cost effectively.
 MapReduce- This is a software framework implemented by Google with
the intention of processing big data.
 Hbase- This framework is modeled on Google’s BigTable database and it is
considered as a dominant Hadoop associated project.

20. Cloud Computing in Next Generation
Sequencing
 Crossbow uses Hadoop in order to calculate and to analyze the entire
genome re-sequencing data and for SNP genotyping from short reads.
 Contrail uses Hadoop for the purposes of de novo assembly from short
sequencing reads without the requirement of using a reference genome.
 Myrna uses Bowtie, another ultrafast short read alignment for the purposes
of evaluating dissimilar gene expressions from large RNA sequence
datasets. While operating on cluster, Myrna uses Hadoop and also Myrna is
employed in the cloud utilizing Amazon Elastic MapReduce.

21. Cloud Computing in Next Generation
Sequencing
 Use of virtual machines:
• A recent cloud computing invention is virtual machines (VMs).
• They are programs that perform parallel processing to overcome
differences between several platforms and VM technology has been used
in bioinformatics.
• CLoVR has been developed for analyzing bacterial NGS data.

22. The Effect of Cloud Computing in Next
Generation Sequencing
Conclusion:
• Handling huge datasets that generated in next generation sequencing is
crucial.
• Cloud computing can be used in next generation techniques.
Suggestion:
• Cloud enable frameworks such as Hadoop, MapReduce and HBase could be
used in next generation sequencing methods to facilitate NGS analytics that
allow users to rapidly interrogate vast data sets.
• Develop CLoVR platform for analyzing DNA sequences.

23. Thank you!