1. Mongo Boston 2013
Accelerate Pharmaceutical R&D with
Big Data and MongoDB
Jason Tetrault
Architect - AstraZeneca

2. AstraZeneca at a glance
We are a global, innovation led biopharmaceutical company
with a mission to make a meaningful difference to patient health
through great medicines and a belief that health connects us all
Global
Targeted
Collaborative
57,000 people
Sales in 100 countries
Manufacturing in 16
R&D across 3 continents
$4 bn invested in R&D
$33 bn sales in 2011
Cancer
Cardiovascular
Gastrointestinal
Infection
Neuroscience
Respiratory & inflammation
HCPs
Patients
Payers
Regulators
Partners
Local communities
Constantly anticipating and
adapting to the needs of a
changing world.
Driving continued innovation
where we can make the most
difference.
Connecting with others to
achieve common goals
in improving healthcare.
Committed to driving business success responsibly

3. Architect: R&D Information
What does this mean?
• Support the Researchers
• AstraZeneca has Multiple iMeds that are
focused on different areas of R&D
• Specifically, I work with the Oncology and
Infection iMeds here in Waltham
• Support different software and system
builds and / or purchases
• Looking to apply new technologies to
enable Researchers
• Core Focus:
• Next Generation Sequencing
Scaling
• IAAS
• Big Data Pilots and Exploration

4. Introduction of Disruptive Technology:
Step 1: Introduce Concepts
• What
• Unstructured Data
• NoSQL
• Categories (Document, Key Value, Graph)
•
•
•
•
Hadoop
Map Reduce
Horizontal Scalability
Cloud (IAAS and SAAS)
• How
•
•
•
•
Lunch and Learns
Examples (Craigslist uses this)
“Big Cookies for Big Data”
Demonstrations

5. Introduction of Disruptive Technology:
Step 2: Pilots
• Goals:
• We needed to show what “Unstructured Data” actually means.
• We needed to prove what these technologies can and cannot
do for us.
• Find something difficult and make it easy!
• We needed to find the best way to enable researchers.

6. Iterative Agile Analytics
How quickly can I make indirect associations between gene sequence
features and structural fingerprints?
Now scale up to 4M compounds, 20K
assays…and more decoration – 5to50 Tb
Data sources
Compound
JSON
Pivot
Map Reduce
Matrix
AssayResults
(300K Compounds) – 200Gb
GeneCatalog
(1.4M fingerprints) – 1Gb
• Compound with Fingerprints
• Gene sequence
• Target mappings
• Assay results
Gather
Fingerprint with
compounds
Aggregate
(500m pairs) – 81Gb
Tanimoto matrix
Gene matrix
Analyze
Target mappings
Decorate
• Easily convert to JSON and import an initial cut of data from different sources (e.g.
spreadsheets, RDBMS, …)
• Embrace unstructured data, massage it into a more useful format: Rinse, Wash, Repeat!
• Ability to decorate data, adding fields and additional datastores quickly
6

7. Introduction of Disruptive Technology:
Pilot Findings
• Tech Findings:
• GSON can help with weird character
conversions.
• Per Node write limits (500 per second)
but, you can save a bunch of documents
at once (Change to bulk Insert).
• Users think that even though they could
do it relationally, this was way quicker.
• Using arrays for multiple results in a doc
can be interesting.
• JSON and JavaScript is fairly natural to
technical researchers (python).
• We are not alone…
•
•
•
•
Davy Suvee
tranSMART
Seven Bridges
…

8. Next Generation Sequencing:
Driving Question:
Can we predict which drug is
most effective against
specific tumors?
How many other cancer types
that I have processed have the
same variation as the cancer
type I am working on?

9. Fairly Inaccurate Overview of Genetics
Processing
A 2 Minutes Over Simplification to a Really Hard
Problem
9

10. Fairly Inaccurate Overview of Genetics
Processing
Sequencing
10

11. Fairly Inaccurate Overview of Genetics
Processing
Sequencing
11

12. Fairly Inaccurate Overview of Genetics
Processing
Alignment
HG19
12
Set area descriptor | Sub level 1

13. Fairly Inaccurate Overview of Genetics
Processing
Down Stream Processing (Variant)
HG19
13

14. Can I Process 88 Whole Human Genomes?
Researcher: I would like to process 88 public Genomic Samples from of Cancer Patients. They are Whole Human
Genomes. Each patient has 2 genomic sequences, one of the tumor and one from a normal cell.
Tech:
• 200 GB raw uncompressed fastq per
experiment
• 176 Genome Pipelines to process
• Each “pipeline” runs on a m1.xlarge
• We ran 4 runs of ~3.5 days on 50 nodes
• Total processed data in the pipeline may be 5X
per experiment
•Could expand to 10X or more for more complex
pipelines
• ~86 GB result average to save
• Stored in S3 / Glacier
• Totals:
• ~171 TB Total Processed Storage
• ~14,784 hours of processing
• ~15 TB of results
Elastic HPC
Infrastructure
Scripts, progr
ams, referenc
e
Shared Storage
Compute
Amazon
StarCluster
Elastic Node Expansion
Local Storage
Processing
Result offload to S3
Transition to
Glacier

15. A Possible Vision for Experiment Management
NGS Data
Explants
TumorsFFPE
Tumors –
fresh frozen
Cell lines
 Patient stratification
 Biomarkers for
prognosis, drug
response, safety
Expression
RNASeq
Variants
Amplicon
DNASeq
Whole
exome
Whole
genome
Coding and
non-coding
variants
Coding
variants
 Mechanism of drug
action
 Mechanism of disease
New Target ID
Inbound
Seven Bridges
GenePattern
Storage
Partners
Big Data
Store
Experiment
Management /
Metadata
Management
Services
Genome Upload /
Curation
Pipeline
Engines
Long Term
Storage
Partner
Integration
Big Data Storage
and Analytics

16. Lets look at a Variant …
Another Area Mongo May Help
16

17. VCF Format
##fileformat=VCFv4.1
##fileDate=20090805
##source=myImputationProgramV3.1
##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta
##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens",taxonomy=x>
##phasing=partial
##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">
##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">
##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">
##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele">
##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129">
##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership">
##FILTER=<ID=q10,Description="Quality below 10">
##FILTER=<ID=s50,Description="Less than 50% of samples have data">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth">
##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality">
#CHROM POS
ID
REF
ALT
QUAL FILTER INFO
FORMAT
NA00001
NA00002
NA00003
20
14370
rs6054257 G
A
29
PASS
NS=3;DP=14;AF=0.5;DB;H2
GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51
1/1:43:5:.,.
20
17330
.
T
A
3
q10
NS=3;DP=11;AF=0.017
GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3
0/0:41:3
20
1110696 rs6040355 A
G,T
67
PASS
NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2
2/2:35:4
20
1230237 .
T
.
47
PASS
NS=3;DP=13;AA=T
GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51
0/0:61:2
20
1234567 microsat1 GTC
G,GTCT 50
PASS
NS=3;DP=9;AA=G
GT:GQ:DP
0/1:35:4
0/2:17:2
1/1:40:3
17

18. VCF as JSON
Header and Variant Information
{
{
"_id" : ObjectId("52617b613004b77f64efed62"),
"ALT" : [
"A"
],
"QUAL" : "29",
"NA00001" : "0|0:48:1:51,51",
"POS" : 14370,
"NA00002" : "1|0:48:8:51,51",
"FILTER" : "PASS",
"CHROM" : "20",
"NA00003" : "1/1:43:5:.,.",
"FORMAT" : "GT:GQ:DP:HQ",
"__vcfid" : "40770f6f-165a-4930-8092-05e98e4e0b27",
"ID" : "rs6054257",
"INFO" : {
"DP" : "14",
"AF" : "0.5",
"NS" : "3"
},
"REF" : "G"
}
18
"_id" : ObjectId("52617b613004b77f64efed67"),
"phasing" : "partial",
"fileformat" : "VCFv4.1",
"fileDate" : "20090805",
"source" : "myImputationProgramV3.1",
"FORMAT" : {
"Description" : ""Haplotype Quality"",
"Type" : "Integer",
"Number" : "2",
"ID" : "HQ"
},
"__vcfid" : "40770f6f-165a-4930-8092-05e98e4e0b27",
"contig" : {
"species" : ""Homo sapiens"",
"assembly" : "B36",
"md5" : "f126cdf8a6e0c7f379d618ff66beb2da",
"length" : "62435964",
"ID" : "20",
"taxonomy" : "x"
},
"INFO" : {
"Description" : ""HapMap2 membership"",
"Type" : "Flag",
"Number" : "0",
"ID" : "H2"
},
"reference" : "file:///seq/references/1000GenomesPilotNCBI36.fasta",
"FILTER" : {
"Description" : ""Less than 50% of samples have data"",
"ID" : "s50"
}
}

19. Query
Search Variant Ranges
// Here is our range definition
var begin = 10000;
var end = 10200;
// The Chromosome position is fuzzy in format so, we use a regex
var chromosome = ".*17$";
var variant = "A"
// Query for range and chromosome position.
db.publicvariants.find(
{"POS":{$gte: begin, $lt: end},
"CHROM":{$regex : chromosome}
})
db.variants.find(
{"POS":{$gte: begin, $lt: end},
"CHROM":{$regex : chromosome}
})
// Query for a specific variant in a range
db.publicvariants.find(
{"POS":{$gte: begin, $lt: end},
"CHROM":{$regex : chromosome},
"ALT":variant})
db.variants.find(
{"POS":{$gte: begin, $lt: end},
"CHROM":{$regex : chromosome},
"ALT":variant})
19

20. Wrap Up and Panel
• Panel
• DenizKural: Founder and CEO – SevenBridges
• Code:
• https://github.com/jjtetrault/bio-mongo
• Thanks
• Todd Nelson, Rajan Desai
• Sebastien Lefebvre, Robin Brouwer
• Sara Dempster
20

21. The Panel
…
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22. Confidentiality Notice
This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and
remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or
disclosure of the contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 2 Kingdom Street, London, W2
6BD, UK, T: +44(0)20 7604 8000, F: +44 (0)20 7604 8151, www.astrazeneca.com
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