Next Generation Sequencing
An Introduction
Setia Pramana
TEIN4 WORKSHOP ON NEXT GENERATION
SEQUENCING AND BIG DATA ANALYSIS
26-28 August 2015
UNIVERSITAS INDONESIA
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Educational Background
• Universitas Brawijaya Malang, FMIPA, Statistics
department, 1995-1999.
• Hasselt Universiteit, Belgium, MSc in Applied Statistics
2005-2006.
• Hasselt Universiteit, Belgium, MSc in Biostatistics 20062007.
• Hasselt Universiteit, Belgium, PhD Statistical
Bioinformatics, 2007-2011.
• Medical Epidemiology And Biostatistics Dept. Karolinska
Institutet, Sweden, Postdoctoral, 2011-2014
Now?
• Head of Center for Methodology and Computational
Statistics Studies, Sekolah Tinggi Ilmu Statistik,
Jakarta.
• Adjunct Faculty at Medical Epidemiology and
Biostatistics Dept, Karolinska Institutet, Stockholm.
• Adjunct Faculty at Faculty of Medicine, University of
Indonesia Jakarta.
What is Bioinformatics?
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Bioinformatics
• Bioinformatics is a science straddling the domains of
biomedical, informatics, mathematics and statistics.
• Applying computational techniques to biology data
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What is Bioinformatics ?
Bioinformatics is a multifaceted discipline combining many scientific fields
including computational biology, statistics, mathematics, molecular biology and
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genetics (Fenstermacher, 2005, p. 440).
…from Bayat (2002), p 1018.
Medical Implications
• Pharmacogenomics
– Not all drugs work on all patients, some good drugs cause death in
some patients
– So by doing a gene analysis before the treatment the offensive drugs
can be avoided
– Also drugs which cause death to most can be used on a minority to
whose genes that drug is well suited
– Customized treatment
• Gene Therapy
– Replace or supply the defective or missing gene
– E.g: Insulin and Factor VIII or Haemophilia
• BioWeapons (??)
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Sequencing: from DNA to Genomes
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DNA Sequencing
• DNA sequencing is a laboratory technique used to
determine the exact sequence of bases (A, C, G, and
T) in a DNA molecule.
• The DNA base sequence carries the information a
cell needs to assemble protein and RNA molecules.
• The information is important to investigate the
functions of genes.
• The technology was made faster and less expensive
as a part of the Human Genome Project. Genome.gov
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The Birth of Sequencing
• Walter Gilbert and Allan Maxxam (Harvard Univ) in
1977 reported a technique to sequence DNA by
chemical cleavage
• The same year, Frederick Sanger’s group (Cambridge
Univ) reported DNA polymerase and dideoxynucleotides technique
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DNA Sequencing
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The Human Genome Project
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The Human Genome Project
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First draft genome of human in 2001, final 2004
Estimated costs $3 billion, time 13 years
Used Sanger Sequencing
Today:
Illumina: 1 week, 9500$
Exome: 6 weeks*, $1000
Towards 1000$ genome
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The Human Genome Project
• The draft sequence of the
HGP was imperfect because
of the incomplete coverage
of many regions – a huge
number of gaps
• The IHGSC published a
‘finished’ version of the
human genome sequence in
2004 and the HGP was then
deemed to be ‘complete’
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The Human Genome Project
• This ‘finished’ version of the
genome achieved almost
complete coverage of all the
regions and also significantly
reduced the number of gaps
to 341 from the initial
hundreds of thousands
• Initiated a new era in the
study of genetic variation and
the functional
characterization of the human
genome
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Next (second) Generation Sequencing
• New technologies allowing the massive production
of tens of millions of short sequencing fragments.
Thus, it is also called: “Massively parallel
sequencing”
• These techniques could be used to
– deal with similar problems than microarrays,
– but also with many other.•
• They raised the promise of personalized medicine
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NGS
• The advent of high-throughput sequencing
technologies has initiated the ‘personal genome
sequencing’ era for both normal and cancer
genomes
• Large-scale international projects such as the 1000
Genomes Project and the International Cancer
Genome Consortium
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NGS
• NGS technologies have been on the market only
since 2004
• Have now largely replaced Sanger sequencing
technologies (owing to the ultra-high-throughput
production/hundreds gigabases)
• Ability to simultaneously sequence millions of DNA
fragments - massively parallel sequencing
technologies
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NGS
• Reduced sequencing costs significantly, making
large-scale or WGS studies much more affordable
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NGS Technologies/Platforms
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NGS Technologies/Platforms
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Differences between platforms
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Run times vary from hours to days
Production range from Mb to Gb
Read length from <100 bp to > 1500 bp
Accuracy per base from 0.1% to 15%
Cost per base varies
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NGS, General Procedure
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NGS Process in different Platforms
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Roche
Main applications:
• Microbial genomics and metagenomics
• Targeted resequencing
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Roche 454 Sequencing
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Roche Sequencing Process
• DNA Library Preparation
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Roche Sequencing Process
• EMPCR
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Roche Sequencing Process
• Sequencing
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Roche Sequencing Process
• Fast run time 23h
• 500-700 bp long reads (75-150bp for SOLiD and
HiSeq)
• 700 Mbases from one run (180-300Gb per
flowcell using SOLiD or HiSeq)
• High run costs
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ABI SOLiD
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ABI SOLiD
Features
• High accuracy due to two-base encoding
• True paired-end chemistry - ligation from either end
• Mate-pair libraries
Main applications
• Whole genome, exome and targeted resequencing
• Transcriptome analyses
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• Methylome and ChiPSeq
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Illumina (Solexa)
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Illumina
Main applications:
• Whole genome, exome and targeted reseq (HiSeq)
• Transcriptome analyses
• Methylome and ChiPSeq
• Rapid targeted resequencing (MiSeq)
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Ion Torrent
Main applications:
• Microbial and metagenomic sequencing
• Targeted resequencing
• Clinical sequencing
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Ion Torrent Application
• Microbial sequencing
– Accurate, fast bacteria and virus de-novo & resequencing
• Mitochondrial sequencing
- Deep sequencing for research, clinical, and forensic applications
• Amplicon sequencing
- Detection of germline and somatic mutations
- Bacterial and viral typing
• Resequencing by target enrichment
• Validation of whole genome and whole exome mutations
• Whole-transcriptome human RNA-Seq
• Chip-Seq
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Third Generation Sequencing
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Pacific Bioscience
Single-Molecule, Real-Time DNA sequencing
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Pacific Bioscience
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Yield: 50-200 Mb
Read length: 2700
Error Rate 13%
Cost ~ $500-2000/Gb ($100/run)
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Pacific Bioscience
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Viral/ Microbial Genome Assembly
Full length RNA isoform identification
Eucaryotic genome assembly
Chemical Modifications
Clinical sequencing of amplicons or arrayenriched DNA
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Oxford Nanopore Technology
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Just Launched (2014)
150 bases/sec/pore
Protein nanopores on silicon chip
DNA measured as it is pulled through
125 Gb/ day
20-100.000 bases reads
4% error rate
Cost $10/Gb
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NGS Platforms
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NGS Platforms
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NGS Platforms
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NGS Application
RNA-seq
Whole Genome
Seq
Gene Regulation
NGS
Epigenetic
Exome Seq
Resequencing
Metagenomics
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NGS Application
NGS Application
• Whole genome re-sequencing
• Ancient genomes
• Metagenomics
• Cancer genomics
• Exome sequencing (targeted)
• RNA sequencing
• Chromatin immunoprecipitation (CHiP)-Seq: Protein
interaction with DNA
• Genomic Epidemiology
• Epigenomic
• Genetic human variation : SNP, CNV (diseases)
• anything with DNA
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Sequencing Factory:
Beijing Genome Institute
• Purchased 128 HiSeq2000 sequencers from
Illumina in January 2010
• each of which can produce 25 billion base pairs
of sequence a day
NGS Application: Whole Genome Seq
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NGS Application: Exome Genome Seq
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NGS Application: RNA Sequencing
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Bioinformatics Challenges of NGS
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Sequencing has gotten Cheaper and Faster
Cost of one human genome
• HGP
$ 3 billion (13 yrs)
•2004:
$ 30,000,000
•2008:
$100,000
•2010:
$ 30,000
•2011:
$10,000
•2012-13: $7,000
•2014:
$4,000 (~1 week)
•???:
$1,000
The Race for the $1,000 Genome
(Sequencing) Cost is Getting Cheaper
• Reduced sequencing costs significantly, making large-scale or
WGS studies much more affordable
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NGS Challenges
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Huge Data Storage and HPC Demand
Generalized NGS Analysis
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NGS Challenges
• Highest cost is (almost) not the sequencing but
storage and analysis.
• A standard human (30-40x) whole genome
sequencing would create 100 Gb of data
• Extreme data size causes problems
• Just transferring and storing the data
• Standard comparisons fail (N*N)
• Standard tools can not be used
• Think in fast and parallel programs
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•
Bioinformatics Challenges of NGS
Need for large amount of CPU power
- Informatics groups must manage compute clusters
-Challenges in parallelizing existing software or
redesign of algorithms to work in a parallel
environment
- Another level of software complexity and
challenges to interoperability
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Bioinformatics Challenges of NGS
• VERY large text files (~10 million lines long)
- Can’t do ‘business as usual’with familiar tools
such as Perl/Python.
- Impossible memory usage and execution time
- Impossible to browse for problems
• Need sequence Quality filtering
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Data Management Issues
• Raw data are large. How long should be kept?
• Processed data are manageable for most people
– 20 million reads (50bp) ~1Gb
• More of an issue for a facility: HiSeq recommends 32 CPU
cores, each with 4GB RAM
• Certain studies much more data intensive than other
– Whole genome sequencing
30X coverage genome pair (tumor/normal) ~500 GB
50 genome pairs ~ 25Setia
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Data Management
• Primary data usually discarded soon after run
• Secondary and tertiary data maintained on fast access disk
during analysis, then moved to slower access disk afterward
Bioinformatics Challenges of NGS
• In NGS we have to process really big mounts of data,
which is not trivial in computing terms.
• Big NGS projects require super computing
infrastructures: it's not the case that any one can
study everything.
Small facilities must carefully choose their projects to
be scaled with their computing capabilities
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Computational Infrastructure for NGS
We can start with:
- Computing cluster:
Multiple nodes(servers) with of course multiple cores
High performance storage(TB, PB level)
Fast networks(10Gb ethernet, infiniband)
- Enough space and conditions for the
equipment("servers room")
- Skilled people (sys admin, developers)
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Big Computing Infrastructure
• Distributed memory cluster
Starting at 20 computing nodes
60 to240 cores
At least 48GB RAM per node
• Fast networks
10Gbit
Infiniband
• Batch queue system (sge, condor, pbs, slurm)
• Optional MPI and GPUs environment depending on
project requirements
• Starting at 200.000€ (hardware only)
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Middle size infrastructure
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"Small” distributed file system( around 50TB).
"Small” cluster (around 10 nodes, 80 to 120 cores).
At least giga bit ethernet network.
Price range: 50.000 –100.000 € (just hard ware)
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Small Infrastructure
• Recommended at least 2 machines
– 8 or 12 cores each machine
– 48 Gb RAM minimum each machine.
– BIG local disk. At least 4 TB each machine As
much local disks as we can afford
Price range: starting at 8.000€-10.000 € (2
machines)
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Alternatives
• Cloud Computing
• Grid Computing
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Swedish National Infrastructure for Large
Scale DNA sequencing (SNISS)
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UPPNEX
• UPPmax NEXt generation sequence cluster &
storage
• Located at UPPMAX - Uppsala Multidisciplinary
Center for Advanced
• Computational Science (UPPMAX)
• Dedicated computer cluster (500 nodes)
• Uppnex is serving over 240 projects and hosting
over 800 TB of data
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Interpretation Bottleneck
Big Collaboration
• Need Collaborative expertise (human intelligence and
intuition) are required for meaning and interpretation
(Bergeron 2002)
• Including on-demand communication & sharing of protocols,
electronic resources, data, and findings among the
stakeholders
• Collaboration with other Big DATA sources: National Registers,
BPJS, Hospitals, etc.
Next Generation Projects
• 1000 Genomes Project (to provide a comprehensive resource on
human genetic variation. )
• TCGA (The Cancer Genome Atlas)
• MalariaGen: Sequencing thausands malaria isolates
• 1001 Genome Project: Arabidopsis WGS
• UK10K: Sequencing 10.000 healthy and disease affected individuals.
• Southeast Asia Mycobacterium tuberculosis complex (MTBC) DB:
Sequencing MTBC Isolates
• Many more…..
Collaboration Challenges
• Potential conflict between traditional silo researchers and those
embracing Big Collaboration
• Compatible technologies and Cloud infrastructures
• IT management of groups with different tools, requirements and
expectations
• Ownership of data
• Government regulations and policies
• Accessible data repositories and lack of transparency in findings
• Resources to support bioinformatics
• Patient privacy
Frost & Sullivan
Five Domains of Genomic Research
Green. 2011. Nature 470, 204-213
Summary
• Unraveling the Bioinformatics (Big) Data would
provide right decisions at the right time for the right
patients.
• The problem is not producing data, but more on how
to interpret them
• Bioinformatician is one of the sexiest job 
Summary
• Challenges:
– Still expensive
– Lack of Infrastructure (in developing countries)
– Lack of skilled personal on Bioinformatics
– Need (large scale) collaborations
– Integrate different technologies and system
– Making it all clinically relevant
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References
• The Evolution of High-Throughput Sequencing Technologies: From
Sanger to Single-Molecule Sequencing. Chee-Seng Ku , Yudi Pawitan ,
Mengchu Wu , Dimitrios H. Roukos , and David N. Cooper (2013)
• http://ueb.ir.vhebron.net/NGS
• http://cbs.dtu.dk/courses/27626/programme.php
• http://nestor.uppnex.se/twiki/bin/view/Courses/CM1209/Schedule
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Thank you…..
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