Introduction to next generation sequencing

Introduction to
Next Generation Sequencing
Alex Sánchez

Statistics and Bioinformatics Research Group
Statistics department, Universitat de Barelona

Statistics and Bioinformatics Unit
Vall d’Hebron Institut de Recerca

Introduction to NGS http://ueb.ir.vhebron.net/NGS

Outline

Introduction, Presentation, Goals.
Next generation sequencing technologies.
Evolution, Description, Comparison.
Bioinformatics challenges.
Some aspects of NGS data analysis.
NGS data, and data preprocessing (QC)
Types of analyses, workflows, tools
Conclusions and perspectives


Who, where, what?


Introduction


Why is NGS revolutionary?
• NGS has brought high speed not only to genome
sequencing and personal medicine,
• it has also changed the way we do genome research

Got a question on genome organization?

SEQUENCE IT !!!

Ana Conesa, bioinformatics researcher at
Principe Felipe Research Center


Sequencing: the Sanger Method (1977)

Click here to see an animation


History of DNA sequencing is related to the combination of new technologies.


The human genome project


Next generation sequencing

The future is here, now


Next generation Sequencing
• Improvements in enzymes, chemistry and image
analysis, mature by the middle of last decade
dramatically increased sequencing capabilities.
• The newest type of technology, called “next-generation
sequencing“, appeared with the potential to dramatically
accelerate biological and biomedical research
– by enabling the comprehensive analysis of genomes,
transcriptomes and interactomes,
– by tending to become inexpensive, routine and
widespread, rather than requiring very costly
production-scale efforts.


NGS technologies


Next-generation DNA sequencing
Sanger sequencing Cyclic-array sequencing


Sanger sequencing Next-generation sequencing

Advantages of NGS
- Construction of a sequencing
library clonal amplification to
generate sequencing features



Advantages:

No in vivo cloning,
transformation, colony picking...



Advantages:

No in vivo cloning,

- Array-based sequencing



Advantages:

No in vivo cloning,

- Array-based sequencing

Higher degree of parallelism
than capillary-based sequencing


NGS means high sequencing capacity

GS FLX 454 HiSeq 2000 5500xl SOLiD
(ROCHE) (ILLUMINA) (ABI)

GS Junior

Ion TORRENT


NGS Platforms Performance

454 GS Junior
35MB


454 Sequencing


ABI SOLID Sequencing


Solexa sequencing


Comparison of 2nd NGS


Some numbers


The sequencing process, in detail
1 Library preparation 1 DNA
fragmentation
and in vitro
adaptor ligation


2 Clonal amplification fragmentation
and in vitro
adaptor ligation

emulsion PCR
2


and in vitro
adaptor ligation

emulsion PCR bridge PCR
2


and in vitro
3 Cyclic array sequencing adaptor ligation

2

3 Pyrosequencing

454 sequencingIntroduction to NGS http://ueb.ir.vhebron.net/NGS

and in vitro

2

3 Pyrosequencing Sequencing-by-ligation

454 sequencingIntroduction to NGSplatform
SOLiD http://ueb.ir.vhebron.net/NGS

and in vitro

2

3 Pyrosequencing Sequencing-by-ligation Sequencing-by-synthesis

454 sequencingIntroduction to NGSplatform
SOLiD Solexa technology
http://ueb.ir.vhebron.net/NGS

Next next generation sequencing
• Pacific Biosystems
– Real time DNA
synthesis
– Up to 12000nt (?)
– 50 bases/second (?)

• Promises delivery of
human genome in
minutes?
– Company on track for
2013


Bioinformatics challenges of NGS


I have my sequences/images. Now what?


NGS pushes (bio)informatics needs up
• 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
• 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


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
• A 30X coverage genome pair (tumor/normal) ~500 GB
• 50 genome pairs ~ 25 TB


So what?

• In NGS we have to process really big amounts of data,
which is not trivial in computing terms.

• Big NGS projects require supercomputing infrastructures

• Or put another way: it's not the case that anyone can do
everything.
– Small facilities must carefully choose their projects to be scaled
with their computing capabilities.


Computational infrastructure for NGS
• There is great variety but a good point to start with:

– Computing cluster
• Multiple nodes (servers) with multiple cores
• High performance storage (TB, PB level)
• Fast networks (10Gb ethernet, infiniband)
– Enough space and conditions for the equipment
("servers room")
– Skilled people (sysadmin, developers)
• CNAG, in Barcelona: 36 people, more than 50% of them
informaticians


Big computing infrastructure
• Distributed memory cluster
– Starting at 20 computing nodes
– 160 to 240 cores
– amd64 (x86_64) is the most used cpu architecture
– 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.


Big infrastructure is expensive
• Starting at 200.000€
– 200.000€ is just the hardware
– Plus data center (computers room)
– Plus informaticians salary
• Not every partner knows about supercomputing.
– SGI
– Bull
– IBMHP


Middle size infrastructure
• "Small” distributed filesystem ( around 50TB).

• "Small” cluster (around 10 nodes, 80 to 120 cores).

• At least gigabit ethernet network.

• Price range: 50.000 – 100.000 € (just hardware)
– plus data center and informaticians salary


Small infrastructure
• Recommended at least 2 machines
– 8 or 12 cores each machine.
– 48Gb ram minimum each machine.
– BIG local disk. At least 4TB each machine
• As much local disks as we can afford

• Price range: starting at 8.000€ - 10.000€ (2 machines)


Alternatives (1): Cloud Computing
• Pros
– Flexibility.
– You pay what you use.
– Don´t need to maintain a data center.
• Cons
– Transfer big datasets over internet is
slow.
– You pay for consumed bandwidth.
That is a problem with big datasets.
– Lower performance, specially in disk
read/write.
– Privacy/security concerns.
– More expensive for big and long
term projects.


Alternatives (2): Grid Computing
• Pros
– Cheaper.
– More resources available.
• Cons
– Heterogeneous
environment.
– Slow connectivity (specially
in Spain).
– Much time required to find
good resources in the grid.


So what?
• Think before you NGS
• Decide what you …
– want to do,
– can afford
– know how to do
• Consider all alternatives
• Look for expert advice …


NGS data analysis


NGS data analysis stages


Applications of Next-Generation Sequencing

Metagenomics and other community-based “omics”

Zoetendal E G et al.
Gut 2008;57:1605-1615


De novo sequencing


Transcriptomics by NGS: RNASeq

• Analog Signal • Digital Signal
• Easy to convey the signal’s
• Harder to achieve & interpret
information
• Continuous strength • Reads counts: discrete values
• Signal loss and distortion • Weak background or no noise


Which software for NGS (data) analysis?
• Answer is not straightforward.
http://seqanswers.com/wiki/Software/list
• Many possible classifications
– Biological domains
• SNP discovery, Genomics, ChIP-Seq, De-novo assembly, …
– Bioinformatics methods
• Mapping, Assembly, Alignment, Seq-QC,…
– Technology
• Illumina, 454, ABI SOLID, Helicos, …
– Operating system
• Linux, Mac OS X, Windows, …
– License type
• GPLv3, GPL, Commercial, Free for academic use,…
– Language
• C++, Perl, Java, C, Phyton
– Interface
• Web Based, Integrated solutions, command line tools, pipelines,…


Combining tools in a typical workflow


Other popular tools


Quality control and preprocessing of
NGS data


Data types


Why QC and preprocessing
• Sequencer output:
– Reads + quality
• Natural questions
– Is the quality of my sequenced
data OK?
– If something is wrong can I fix it?
• Problem: HUGE files... How
do they look?
• Files are flat files and big...
tens of Gbs (even hard to
browse them)


Preprocessing sequences improves results


How is quality measured?

• Sequencing systems use to assign quality scores to each peak
• Phred scores provide log(10)-transformed error probability values:
If p is probability that the base call is wrong the Phred score is
Q = .10·log10p
– score = 20 corresponds to a 1% error rate
– score = 30 corresponds to a 0.1% error rate
– score = 40 corresponds to a 0.01% error rate
• The base calling (A, T, G or C) is performed based on Phred scores.
• Ambiguous positions with Phred scores <= 20 are labeled with N.


Data formats
• FastA format (everybody knows about it)
– Header line starts with “>” followed by a sequence ID
– Sequence (string of nt).

• FastQ format (http://maq.sourceforge.net/fastq.shtml)
– First is the sequence (like Fasta but starting with “@”)
– Then “+” and sequence ID (optional) and in the following line are
QVs encoded as single byte ASCII codes
• Different quality encode variants

• Nearly all downstream analysis take FastQ as input
sequence


The fastq format
• A FASTQ file normally uses four lines per sequence.
– Line 1 begins with a '@' character and is followed by a sequence
identifier and an optional description (like a FASTA title line).
– Line 2 is the raw sequence letters.
– Line 3 begins with a '+' character and isoptionally followed by the same
sequence identifier (and any description) again.
– Line 4 encodes the quality values for the sequence in Line 2, and must
contain the same number of symbols as letters in the sequence.
• Different encodings are in use
• Sanger format can encode a Phred quality score from 0 to 93 using ASCII 33 to 126

@Seq description
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65


Some tools to deal with QC
• Use FastQC to see your starting state.

• Use Fastx-toolkit to optimize different datasets and then
visualize the result with FastQC to prove your success!

• Hints:
– Trimming, clipping and filtering may improve quality
– But beware of removing too many sequences…

Go to the tutorial and try the exercises...


Acknowledgements
Grupo de investigación en Estadística y Bioinformática del
departamento de Estadística de la Universidad de
Barcelona.

Xavier de Pedro and Ferran Briansó (but also Jose Luis
Mosquera and Israel Ortega) de la Unitat d’Estadística i
Bioinformàtica del VHIR (Vall d’Hebron Institut de
Recerca)

Unitat de Serveis Científico Tècnics (UCTS) del VHIR
(Vall d’Hebron Institut de Recerca)

People whose materials have been borrowed
Manel Comabella, Rosa Prieto, Paqui Gallego, Javier
Santoyo, Ana Conesa, Pablo Escobar, Thomas Girke
…


Gracias por la atención y la paciencia


Introduction to next generation sequencing

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