The Data Tsunami in Biomedical Research

Guillaume Bourque

McGill University and Genome Quebec Innovation Center, Dept. of Human Genetics, McGill University

June 5th, 2013

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Next-generation sequencing (NGS)

Stein, Genome Biol. 2010

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Falling cost of sequencing

DeWitt, Nat. Biotechnol. 2012

Sequencing human genomes

1000 Genomes

Project

~ 10 000 $

The Human

Genome

~ 3 Billion $

Your Genome

100 - 1000 $

20112001 2013 (?)

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Outline

• Overview of Next-Generation Sequencing (NGS)• Applications• Challenges• Solutions

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Sequencing Revolution

http://www.brusselsgenetics.be

Sanger sequencing Next-Generation sequencing

Metzker, Nat. Rev. Genet. 2010

100s of reactions… 10000s of base pairs…

Millions of reactions!Billions of base pairs!

High-throughput Sequencing

6 Gbases2009 36bp X 20MX 8 lanes

2013 600 Gbases2 X 150bp X 250M

X 8 lanes

200 Human Genomes in 1 run!!!

NGS Technology Comparisoninstrument Pacbio Ion Torrent 454 Illumina SOLiD

Method Single-molecule in real-time

Ion semiconductor Pyrosequencing synthesis Ligation

Read length 3kb average 200 bp 700 bp 50 to 250 bp 50+35 or 50+50 bp

Error type indel indel indel substitution A-T bias

single-Pass Error rate % 13 ~1 ~0.1 ~0.1 ~0.1

Reads per run 35000–75000 up to 4M 1M up to 3.2G 1.2 to 1.4G

Time per run 30 minutes to 2 hours 2 hours 24 hours 1 to 10 days, 1 to 2 weeks

Cost per 1 million bases

(in US$) $2 $1 $10 $0.05 to $0.15 $0.13

Advantages Longest read length. Fast.

Less expensive equipment.

Fast. Long read size.

Fast. high sequence

yield, cost, accuracy

Low cost per base.

Disadvantages

Low yield at high accuracy. Equipment can

be very expensive.

Homopolymer errors.

Runs are expensive.

Homopolymer errors.

Equipment can be very

expensive.

Slower than other methods,

read length, longevity of the

plateform

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Genome Canada

• > $915M investment and > $900M in co-funding• 100s Large-scale genomics projects• 5 Innovation centers

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Outline

• Overview of Next-Generation Sequencing (NGS)• Applications• Challenges• Solutions

Applications (I)• De novo sequencing

– From the human genome… To all model organisms… To all relevant organisms (e.g. extreme genomes)… To “all” organisms?

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Human Genome• 3 Billion DNA base pairs (bp)• Two human genomes are

~99.9% identical • There are about ~3M bp

differences between you and me

• Some of these differences explain variation in:– Disease susceptibility– Differences in drug metabolism– …www.dnacenter.com

Applications (II)• Genome re-sequencing

– Genetic disorders– Cancer genome sequencing– Map genomic structural variations across individuals – Genealogy and migration– Agricultural crops– …

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1000 Genomes Project

The Cancer Genome Atlas

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Exome sequencing for Mendelian disease

“… about one-half to one-third (~3,000) of all known or suspected Mendelian disorders (for example, cystic fibrosis and sickle cell anaemia) have been discovered. However, there is a substantial gap in our knowledge about the genes that cause many rare Mendelian phenotypes.”

“Accordingly, we can realistically look towards a future in which the genetic basis of all Mendelian traits is known, …”

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Exome sequencing

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Cancer genome sequencing

Can obtain a full catalogue of mutations

Michael Stromberg, bioinformatics.ca

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Mutations in paediatric gliblastoma

Jabado, Pfister and Majewski

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Mutations in paediatric gliblastoma

Sequenced the exomes of 48 paediatric GBM samples, found:

• Somatic mutations in the H3.3-ATRX-DAXX chromatin remodelling pathway in 44% of tumours

• Recurrent mutations in H3F3A, which encodes the replication-independent histone 3 variant H3.3 in 31% of tumours

Applications (III)

• Quantitative biology of complex systems– New high-throughput technologies in functional genomics: ChIP-Seq,

RNA-Seq, ChIA-PET, RIP-Seq, …– From single-gene measurements, to thousands of probes on arrays, to

profiles covering all 3B bases of the genome– Important systems: Stem cells, Cancer, Infectious diseases…

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Outline

• Overview of Next-Generation Sequencing (NGS)• Applications• Challenges• Solutions

High-throughput Sequencing

6 Gbases2009 36bp X 20MX 8 lanes

2013 600 Gbases2 X 150bp X 250M

X 8 lanes

200 Human Genomes in 1 run!!!

Big Data

1 TBytes2013 2 X 10 TBytes

Intensity files Reads + qualities

70 TBytes

Image files

Big Data

1 TBytes2013 2 X 10 TBytes

12 TBytes240 TBytes

Intensity files Reads + qualities

25 TB of raw data / month300 TB of raw data / year

From: Alexandre Montpetit Subject: news from IlluminaDate: 4 June, 2013 2:15:16 PM EDTTo: Guillaume Bourque

De Mark Van Oene (vp Illumina ventes): dans la prochaine annee on doit s'attendre a 2x plus de reads en 2x moins de temps (et 2x plus longs) Ca cause probleme?

Alex

Large NGS project

Cancer project with whole genome data:

125 TB raw

500 X 3 lanes = 500 X 250GB

125 TB raw

500 tumors 500 matched-normal

500 X 3 lanes = 500 X 250GB

vs

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DNA bases sequenced at the Innovation Center

DNA

base

s

12 HiSeqs

72 Trillions!

0r 800 genomes at 30X

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adventure.nationalgeographic.com

Biomedical research is built on data integration

Your data

Biomedical research is built on data integration

100X

Your data

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Challenges

• NGS instruments generate TBs of data• NGS instruments are getting faster, cheaper and will

increasingly be found in small research labs and hospitals

• Data sharing and integration is critical in biomedical research

• Sequencing data represents sensitive private data and is identifiable

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Outline

• Overview of Next-Generation Sequencing (NGS)• Applications• Challenges• Solutions

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Nanuq softwareHas tracked data and meta-data for more than:

• 2.6 million sample aliquots, • 20,500 reagents, • 17,000 plates, • 140,000 tubes, • Multiple platforms, technologies and

workflows(sequencing, genotyping, microarray, etc.)• 3,900 external users

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Standardized analysis pipelines

ChIP-Seq Analysis report

RNA-Seq Analysis report

MethylationAnalysis report

…

…

… … …

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Data center at the Innovation Center

> 1200 cores> 2 PB disk> 5 PB tape

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Need more!

McGill Guillimin – 16000 cores

UdeS Mammouth – 39168 cores

Data processing issues

• We have many different projects all needing space and processing.

• We want to use the Compute Canada clusters for scalability but also to facilitate data distribution (we have >800 users).

• This brings uniformity problems:– Different setups Hardware and Software– Different configurations– Etc.

Our strategy

• We wrote analyses pipelines to be easily configurable across clusters.

• Same code, one ini file to customize (we already have templates for 3 cluster sites)

• We install Linux modules readable by all on all these clusters so we know exactly what is available everywhere

• We also deploy common genomes across sites.

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Usage on Compute Canada

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Canadian Epigenetics,

Environment and Health Research

Consortium (CEEHRC)$1.5M

(2012-2017)

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PORTal for the Analysis of Genetics and Genomics Experiments (PORTAGGE)

Conclusions• NGS offers a variety of technologies and numerous exciting

applications• Many areas of NGS data analyses are still under active

development (e.g. RNA-Seq)• A major challenge is to ensure sufficient compute and storage

capacities not to limit more advanced analyses• Need to work together to avoid duplication of efforts in

installing tools but also to develop efficient ways to use HPC in biomedical research

Acknowledgements

IT teamTerrance McquilkinMarc-André LabontéGenevieve DancausseAndras FrankelAlexandru Guja

Development teamNathalie ÉmondDavid BujoldFrancois CantinCatherine CôtéBurak DemirtasDaniel GuertinLouis Dumond JosephFrancois KorbulyMarc MichaudThuong Ngo

Analysis teamLouis LetourneauMathieu BourgeyMaxime CaronGary LévesqueRobert EveleighFrancois LefebvreJohanna SandovalPascale Marquis

guil.bourque@mcgill.ca

EDCC teamDavid Morais (UdeS)Carol Gauthier (UdeS)Bryan Caron (McGill)Alain Veilleux (UdeS)ME Rousseau (McGill)

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Questions?