introduction to next generation sequencing

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

Introduction toNext Generation Sequencing

Statistics and Bioinformatics Research GroupStatistics department, Universitat de Barelona

Statistics and Bioinformatics UnitVall d’Hebron Institut de Recerca

Alex Sánchez


Outline

Introduction, Presentation, Goals.Next generation sequencing technologies.

Evolution, Description, Comparison.Applications of NGS.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 genomesequencing 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: from DNA to GenomesSanger chain termination (1977) Hierarchical and

Shotgun sequencing (1996)


The human genome project


Next generation sequencing

The future is here, now


Next generation Sequencing• By the middle decade new technologies consolidated

allowing the massive production of tens of millions ofshort sequencing fragments.

• These techniques could be used to– Deal with similar problems than microarrays,– But also with many other.

• “Again” they raised the promise of personalizedmedicine..


NGS technologies


Next-generation DNA sequencingSanger sequencing Cyclic-array sequencing


Next-generation DNA sequencingSanger sequencing Next-generation sequencing

Advantages of NGS

- Construction of a sequencinglibrary clonal amplification togenerate sequencing features



Advantages:


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



Advantages:



- Array-based sequencing



Advantages:



- Array-based sequencing

Higher degree of parallelismthan capillary-based sequencing


NGS means high sequencing capacity

GS FLX 454(ROCHE)

HiSeq 2000(ILLUMINA)

5500xl SOLiD(ABI)

Ion TORRENT

GS Junior


454 GS Junior35MB

NGS Platforms Performance


Next-generation DNA sequencing

454

SOLiD

SOLEXA

Workflow?


454 Sequencing


ABI SOLID Sequencing


Solexa sequencing


Comparison of 2nd NGS


Some numbers


The sequencing process, in detail

DNA fragmentationand in vitroadaptor ligation

111 Library preparation




emulsion PCR

1

2

11

22

Library preparation

Clonal amplification




emulsion PCR bridge PCR

1

2

11

22

Library preparation






Pyrosequencing

1

2

3

11

22

33 Cyclic array sequencing

Library preparation


454 sequencing





454 sequencing SOLiD platform

Pyrosequencing Sequencing-by-ligation

1

2

3

11

22


Library preparation






Solexa technologySOLiD platform

Pyrosequencing Sequencing-by-ligation Sequencing-by-synthesis

1

2

3

11

22


454 sequencing

Library preparation



Next next generation sequencing

• Pacific Biosystems– Real time DNA

synthesis– Up to 12000nt (?)– 50 bases/second (?)

• Promises delivery ofhuman genome in minutes?– Company on track for

2013


NGS Applications


Bioinformatics challenges of NGS


NGS pushes bioinformatics 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 study 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 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 (sysadmin, developers)

• CNAG, in Barcelona: 30 people, more than 50% of theminformaticians


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.


NGS data analysis


NGS data analysis stages


A typical workflow (Seq-to-variant wf)


Whole Genome Sequencing

Resequencing

Transcriptome Analysis

Gene Regulation

Epigenetic Changes

Metagenomics

Paleogenomics

NGS Applications are sequencing applications


Metagenomics and other community-based “omics”

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


De novo sequencing


Transcriptomics by NGS: RNASeq

• Digital Signal

• Harder to achieve & interpret• Reads counts: discrete values• Weak background or no noise

• Analog Signal

• Easy to convey the signal’sinformation

• Continuous strength• Signal loss and distortion


Quality control and preprocessing ofNGS data


Preprocessing sequences improves results


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 are big... tens of Gbs (even hard tobrowse them)


How is quality measured?

• Assign quality score to each peak• The frequently used Phred scores provide log(10)-transformed error• probability values:

– 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.


Sequence 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 inputsequence

http://maq.sourceforge.net/fastq.shtml


Some tools to deal with QC

• Use FastQC to see your starting state.

• Use Fastx-toolkit to optimize different datasets and thenvisualize 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...


AcknowledgementsGrupo 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 borrowedManel Comabella, Rosa Prieto, Paqui Gallego, Javier Santoyo, Ana Conesa, Pablo Escobar, Thomas Girke…


Gracias por la atención y la paciencia

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