This document discusses how cloud computing and big data analytics can optimize life sciences research. It outlines some key benefits of cloud computing like scalability, flexibility, and pay-as-you-go models. The document also discusses challenges in healthcare big data like volume, velocity, variety and veracity of data. It provides examples of data analysis pipelines and tools for tasks like quality control, variant calling, clustering and enrichment analysis that can help researchers. Finally, it argues that cloud computing has the potential to address challenges and transform biology and healthcare research.

1. Cloud Computing and Innovations
for Optimizing Life Sciences
Research
Krittika Sasmal @ InterpretOmics
19th
February 2014

2. 2
Acknowledgement
Organizers of this event
Scientists and Researchers who work for others and
make their research findings OPEN
Entrepreneurs who translate this knowledge
GNU, Open Software, NIH and other funding bodies
that keep Biology & Medical information OPEN
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4 Grand Social Challenges
Food Security
Health Security
Energy Security
Environmental Security
Common Thread is Biology

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The 21st Century Biology
– The Quantitative Biology
Ref: A New Biology for the 21st Century, The National Academies
Will create a discovery
engine able to
tackle extremely
complex biological
and societal
problems

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Decoding the Book of Life
– milestone for Quantitative Biology
A Milestone for Humanity – the Human genome
Human Genome Completed, June 26th, 2000

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Journey is
- From Reduction to Integration
− There are many diseases that were researched and understood through the process of reduction
− However as the understanding of diseases mature, and the need for proactive medicine increases, researchers find
that many diseases including cancer are due to somatic mutations that cannot be understood in the reduced space
− Understanding of such disease and discover a drug for these diseases will need a reverse operation - integration
and systems biology
Ref: Hiroaki Kitano, et al. Systems Biology: A Brief Overview, Science 295, 1662 (March 2002);

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Translational Medicine
– Genomics + Clinical + Non-clinical Data to Discovery of Novel therapeutics
Data
Information
Knowledge
Literature/
Molecular Data
Clinical/Bedside Data
Medical
Knowledge
Target Data
Preprocessed
Data
Transformed
Data
Patterns
iOmics
Disease/Drug
Data

8. 9
Next Generation Sequence Data
• FASTQ (Illumina)
• Sff (454)
• CCS (PacBio)
• ...
• Microarray
Single End
Sequences
Insert Size
Library Size
Sequence SequencePaired End or
Mate-paired
 
   


DNA/RNA/miRNA
OverlappedOverlapped reads

Random Order & Orientation
Long reads
Short reads
Fixed length reads
Variable length reads
cDNA/mRNA
Hundreds to Billions Bases
Circular Consensus reads
Billions to Hundreds Bases
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9. Data domains and Challenges
Source: Clevergene Biocorp
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Exploratory Data Analysis
Exploratory Data Analysis (EDA) is an approach/philosophy
for data analysis that employs a variety of techniques (mostly
graphical) to
1. Maximize insight into a data set
2. Uncover underlying structure
3. Extract important variables
4. Detect outliers and anomalies
5. Test underlying assumptions
6. Develop parsimonious models
7. Determine optimal factor settings
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11. Each of these is addressed through
algorithms to solve a computational
problem, typically based upon
optimizing some mathematical
criterion
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Trends in Genomic Medicine
J. J. McCarthy et al., Sci Transl Med 2013;5:189sr4
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Data Driven Healthcare
Personalized Health Care
Translational
Medicine
Health Care
Today Digital Imaging
Episodic Treatment Electronic Health Records Artificial Expert Systems
Clinical Genomics
Genetic Predisposition Testing
Molecular Medicine
CA Diagnosis
Pre-symptomatic Treatment
Lifetime Treatment
Evolutionary Practices
RevolutionaryTechnology
Automated
Systems
Non-specific
(Treat Symptoms)
Information
Correlation
Organized
(Error Reduction)
Personalized
(Disease Prevention)
Data and Systems Integration
DistributedHigh-Throughput
Analytics
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P6 Medicine
– Preventive, Predictive, Participatory, Personalized, Precision, and Pervasive
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Personal Data
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Population Data
Registry
Registry
Claims Data
Clinical Trial
Drug reaction
Literature
Genomic Data
Survivability
Public Health Epidemiology
Population Data
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Biomedical DataClinical
Repositories
Online Mendelian
Inheritance in Man
Medical Subject
Heading
Genome/Gene
Annotations
University of Washington
Digital Anatomist
UWDA
NCBI
TaxonomiesHuman
Metabolome
RxNorm
Drug
ICD10
Logical Observation
Identifiers Names and
Code
UMLS
(Unified Medical
Language System)
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18. 7V's in Healthcare Big-data
 Vexing. Proper algorithm needs to be designed to ensure data
processing time is linear. Genomic data are generally NP-Hard and
proper parallel algorithms need to be designed to access data in a
near real-time manner.
 Volume. Physical volume of data that needs to be online. This
includes structured and unstructured data. Storage is available,
however, the challenge is to determine relevance within large data
volumes and how to use analytics to create value from relevant
data.
 Velocity. Data must be retrieved in a timely manner. In healthcare,
many data sources are outside the enterprise. Reacting quickly
enough to deal with data velocity is critical for most healthcare
applications.
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19.  Variety. Data today comes in all types of formats. Structured,
numeric, unstructured data like CT scan, MRI, Ultrasound, X-Rays
etc. in different forms. Also, most healthcare data is categorical,
with or without any order. Managing, merging and governing
different varieties of data is a challenge.
 Variability. Healthcare data are highly inconsistent with periodic
peaks. Cancer for example has four different types of variability viz.,
Intratumoral, Intermetastatic, Intrametastatic, and Interpatient.
Discovery of independent variables and the causal attributes are
critical.
 Veracity. Quality, relevance, repeatability, quantification,
meaningfulness, predictive value, reduction of error
 Value. The final result and its quantification from ROI or reduction of
readmission or reducing the morbidity is the key that will finally be
measured.
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Healthcare Analytics & Decision
Support System
Analytics
of 7Vs
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Big Data in Life Sciences
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Question is, NOT whether we can
do it! But, HOW QUICKLY and
HOW ACCURATELY we can do
it; where, Speed, Repeatability,
Reliability, Predictability, and
Precision matter!
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The Cloud
You don't buy a COW when you need
Milk!
Likewise, a Biologist, a Breeder, or a Clinician
need not Worry about Data Analysis, Algorithms,
Supercomputer, Pipelines, or even the Analytics and
the
Biomedical Informatics!
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Cloud Computing Defined
Cloud computing is an emerging computing paradigm
where data and applications reside in the cyberspace,
it allows users to access their data and information
through any web-connected device be it fixed or
mobile.
Source: John B. Horrigan, Use of Cloud Computing Applications & Services, Data memo, PEW Internet &
American Life project, September 2008
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Cloud Computing User – I (Amir)
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Cloud Computing User – II (Fakir)
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Characteristics of Cloud Computing
Virtual – Physical location and underlying
infrastructure details are transparent to users
Scalable – Able to break complex workloads into
pieces to be served across an incrementally
expandable infrastructure
Efficient – Services Oriented Architecture for
dynamic provisioning of shared compute resources
Flexible – Can serve a variety of workload types –
both consumer and commercial
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Benefits of Cloud
Unlimited Resource
 Unlimited Computing power
 Unlimited storage (Filestore & online memory)
Users can use resources without owning anything –
converting Capex to Opex
Helping Green computing by lending out idle resources
through Cycle Scavenging
Pay as you go
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Cloud Computing Stack
Facilities
Hardware
Facilities
Integration & Middleware
Data Metadata Content
Application
API
Presentation Modality Presentation Platform
Infrastructure as
a Service
Platform as a
Service
Software as a
Service
Connectivity & delivery
API
Facilities
Hardware
Facilities
Connectivity & delivery
API
Integration & Middleware
Q
O
E
&
Q
O
S
S
E
C
U
R
I
T
Y
User/
Customer/
Device
M
I
D
D
L
E
W
A
R
E
Original Cloud ProviderCloud VendorCloud User Next Gen
Network
Next
Generation
Network /
Intranet
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30. 33
Divide and Conquer: MapReduce
Output files
Split 1
Split 2
Split 3
Split 4
Worker
Worker
Worker
k1:v1
k3:v2
k1:v3
k2:v4
k2:v5
k4:v6
k1:v1,v3
k2:v4,v5
k3:v2
Worker
Worker
Worker Output 1
Output 2
k4:v6
Output 3
Master
Input Files
Map
Intermediate files
Reduce
Sort/Group
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Open Source MapReduce
Hadoop
− Implemented in Java; enabled on Amazon
Twister
− Light weight new arrival in town
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Security in the Cloud
Security in the cloud needs to answer few specific questions
like:
1. How much trust do you have on virtualized environment or
the hypervisors in the cloud as against your own physical
hardware?
2. How much trust do you have on cloud vendor versus your
own infrastructure?
3. How do you address regulatory and compliance
requirement in an environment when your application might
be running on an infrastructure in a foreign country?
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New generation software in
bioinformatics needs to be:
Fast/Very fast software, with a low memory
consumption
Be able to handle and analyze TB of data
Store data efficiently to query
Distribute computation, not data
Focused, and useful in analysis
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www.iomics.in
The Omics Lab in the Internet
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35. Crop to Cancer
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37. Analysis Pipelines in iOMICS
QA/QC of raw reads
SNP/InDel/CNV analysis
miRNA discovery
Exome sequencing
ChIP sequencing
... (New additions)
Meta-analysis
Visualization of results
Omics data
Systems Biology
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SNP & CNV Analysis
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Hierarchical Clustering
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Gene Interaction/Enrichment
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Cloud computing Holds the Potential to
Address the Challenges and Transform
Biology and Heathcare
Krittika Sasmal
Email: “krittika” dot “sasmal” (at) “interpretomics” dot “co”
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