PAPAyA: A Highly Scalable Cloud-based Framework for Genomic

Processing

Francois Andry

, Nevenka Dimitrova

, Alexander Mankovich

, Vartika Agrawal

Anas Bder

and Ariel David

Philips, HISS, 4100 East Third Ave, Foster City, U.S.A.

Philips, CISS, 19 Skyline Drive, Hawthorne, U.S.A.

Philips, HISS, 18 Aharon Bart St, Petah Tikva, Israel

Keywords: Genomics, Cloud, PaaS, IaaS, Security, Privacy, Asynchronous Processing, Pipelines, Workflow, Big Data,

Analytics, NGS, Oncology, Infectious Diseases.

Abstract: The PAPAyA platform has been designed to ingest, store and process in silico large genomics datasets using

analysis algorithms based on pre-defined knowledge databases with the goal to offer personalized therapy

guidance to physicians in particular for cancers and infectious diseases. This new highly scalable, secure

and extensible framework is deployed on a cloud-based digital health platform that provides generic

provisioning and hosting services, identity and access management, workflow orchestration, device cloud

capabilities, notifications, scheduling, logging, auditing, metering as well as specific patient demographic,

clinical and wellness data services that can be combined with the genomics analytics results.

1 INTRODUCTION

Progress in next-generation sequencing (NGS),

including whole genome and exome sequencing,

transcriptome profiling and detection of single

nucleotide polymorphisms (SNPs), together with

innovations in cloud-based architectures and big-

data analytics stacks is unlocking the power of

genomes towards personalization of healthcare.

There are multiple clinical areas, such as

oncology, infectious diseases, clinical genetics and

others, where genomics can have big impact on

clinical decision-making.

In oncology, it is well known that tumors are

driven by the accumulation of numerous molecular

alterations. In this case, the care for the patient can

be personalized: based on the patient genomic

fingerprint and other sources of information (e.g.

radiology, biopsy, clinical data, family history and

population management data), new therapy options

and clinical trials can be proposed by an oncologist.

When it comes to infectious diseases, the

genomic sequence can help characterize the precise

sub-strains of a particular pathogen and reveal any

inherent resistance it may have to antibiotics. This is

important in molecular epidemiology especially in

the light of the emergence of so-called superbugs

that can spread as hospital acquired infections.

To address these applications, we started with a

prototype: Physician Accessible Personalized

Analytics Application (aka PAPAyA) for research in

breast cancer that relies on multiple genomics

modalities: gene expression and differential DNA

methylation. The application was oriented towards a

patient centric interpretation of microarray data and

biomarker (signature) discovery.

However, with the evolution of genomics and

introduction of next generation sequencing, the

amount of input data increased many-fold and there

was a need for more versatile platform to address

different types of cancer, and allow for building

clinical applications in various disease domains such

as infectious diseases and cardiology.

The initial prototype was to present the concept

of a research platform for biomarker discovery and

decision support in breast cancer (Janevski et al.,

2009).

The focus of the current platform in this paper is

to enable full scalable cloud based architecture and

implementation to support extensibility, reliability,

security and use it for any cancer and any biological

or clinical domain. Another dimension of the new

platform is the versatility to handle any analysis of

next generation sequencing data (expression, copy

198

Andry, F., Dimitrova, N., Mankovich, A., Agrawal, V., Bder, A. and David, A.

PAPAyA: A Highly Scalable Cloud-based Framework for Genomic Processing.

DOI: 10.5220/0005668401980206

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 3: BIOINFORMATICS, pages 198-206

ISBN: 978-989-758-170-0

number variations, methylation, and fusions).

In this paper we describe how the Philips

PAPAyA has been designed to associate genomic

data with clinical data while leveraging the power of

our PaaS: HealthSuite Digital Platform (HSDP) in

order to process genomic data and be able to provide

precision diagnostic, for example, the following

questions:

 How to match a tumor’s genotype with a

drug for best outcome?

 How to elucidate the cancer subtypes in a set

of RNAseq samples?

 How to tackle and prevent the spread of

hospital acquired infections?

However, converting high-throughput genomic

data into clinically actionable information is not a

straightforward task.

The first challenge is to be able to ingest and

store extremely large amounts of genomic data (up

to 1TB for a single patient whole genome) in a

reliable and secure manner while satisfying legal

requirements for long-term storage.

The second challenge is to be able to run

asynchronously parallel processing heterogeneous

pipelines and associated jobs (e.g. sequence

alignment, variant and mutation calling, copy

number variation detection), written in various

programming languages, in a highly quality

controlled, reliable, reproducible and scalable

manner.

The third challenge is to dynamically integrate

domain-specific knowledge from various databases

that may require frequent updates and to generate

clinically actionable results that are reproducible

during subsequent runs.

A fourth difficulty is to manage the potential

users of the platform (researchers, oncologists,

pathologists, physicians, technicians, administrators)

and to create an interactive, role-based and secure

user interface that provides several key functions: to

query the framework; to display the data, metadata

and knowledge-bases used; to define and obtain the

definition of pipelines and modules; to retrieve the

output of the tools; and to generate an intuitive and

comprehensive visualization of reports and

analytics.

There have been many efforts to automate next

generation sequencing data analysis. Galaxy is such

a workflow system that has been used in sequence

analysis and other bioinformatics applications

(Goecks et al., 2012).

Another group developed infrastructure and tools

to support multisite

comparative effectiveness studies using web services

for multivariate statistical estimation in the

SCANNER federated network. (Meeker et al.,

2015).

While these public efforts are excellent vehicles

for research activities, they are not meant for using

in clinical applications.

GenePattern was one of the first systems that

offered to a broad audience a repository of analytic

tools for genomic data (Reich et al., 2006).

Focusing on the web access, Mobyle system

offered a large panel of curated bioinformatics tools

available in a homogeneous environment, to invoke

services distributed over remote Mobyle servers,

thus enabling a federated network of curated

bioinformatics portals without the user having to

learn complex concepts or to install sophisticated

software (Néron et al., 2009).

2 USE CASES

PAPAyA is a framework, deployed on the

HealthSuite Digital Platform, for hosting multiple

genome informatics applications. These applications

inhabit diverse clinical domains and assemble

information from various sources, across many

hospitals and their affiliates either on premise or on

the cloud, and can deliver real-time actionable

information to the clinical experts.

Examples of use cases include breast, prostate

and lung cancer scenarios with oncologists,

pathologists, urologists, and genome informaticists

as users.

3 SYSTEM SPECIFICATIONS

In redesigning the PAPAyA architecture, our team

always had in mind to build a world-class platform

with the following intrinsic properties:

 Reliability: Offers dependable mechanisms for

automatic, secure, quality assured acquisition

of demographic, clinical and genomic data

from healthcare organizations. Provide a

reliable way to schedule and execute various

pipelines such as those for detecting variants,

mutations, copy number variation (CNV),

differential gene expression and differential

DNA methylation.

 Security: Creates and manages user accounts

with highly secure authentication and

authorization mechanisms, and encrypted data

in flight, but also at rest (including secure

PAPAyA: A Highly Scalable Cloud-based Framework for Genomic Processing

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Table 1: PAPAyA cancer related usage scenarios.

Patients history/context PAPAyA process/output

Patient is 63 years old,

diagnosed with primary

localized prostate cancer.

Biopsy shows a Gleason

score of 3 (moderately

differentiated carcinoma).

Urologist wants to know

the aggressiveness of the

tumor and options for any

surgical procedure,

chemotherapy or active

surveillance.

Patient's RNA is extracted

from the patient tissue and

PAPAyA runs the RNAseq

pipeline.

Assessment of tumor-driving

cancer signalling pathways

indicates that the patient

does not have an aggressive

disease. The urologist may

suggest a simple follow up

without immediate

intervention.

Patient is a 48-year-old

woman with a distant

history of light smoking

with progressive dyspnea

has been diagnosed with

stage IV lung carcinoma

and has already started

chemotherapy.

However, her condition has

recently deteriorated: fluid

output from a right pleural

catheter, fatigue, weight

loss, hypoxemia, fever and

hypercalcemia.

Oncologist is considering

using a targeted therapy as

an alternative.

Patient RNA transcriptome

sequencing is ordered.

PAPAyA analysed the

sequencing data and whole

exome revealed several

fusions (such as EML4-

ALK). This information

along with radiology and

pathology is presented.

The genomic information

oints to efficient drugs

specifically targeting these

fusions (Crizotinib), and the

doctor may propose a few

related clinical trials

targeting the EML4-ALK

fusion.

New renal cell carcinoma

atient study is already

underway and the principal

investigator needs to

rocess RNAseq data and

exome data.

PAPAyA analysed the

sequencing data using whole

transcriptome detection,

quantification pipeline,

exome alignment and variant

calling.

New renal cell carcinoma

study is underway and the

Principal investigator is

interested in figuring out

which patients have good

rognosis and what are the

set of genes (signature) that

stratifies for best outcome.

PAPAyA enabled analysis of

the quantified RNAseq data

to derive a new signature for

rognosis of different

subtypes of renal cell tumors

using unsupervised learning

(including deep learning

methods), statistical

evaluation and differential

survival analysis.

Multiple RNAseq studies

are available for study to a

researcher who is looking

for biomarker positive or

signature positive patients

in order to create a clinical

study or enroll patients in a

clinical study.

Cohort analysis within

PAPAyA is conducted with

run-time execution of

“missions” which include

genomic pipelines and

analytics in a cohort

discovery mode, evaluate

classification methods like

random forests for patient

selection (through inverted

matrices).

Table 2: PAPAyA infectious disease scenario.

Patients history/context PAPAyA process/output

More than 50% of patients

in a 26-

atient ward of a

ublic hospital have been

infected with Methicillin-

resistant Staphylococcus

aureus (MRSA).

The hospital institution

wants to determine the

source of the outbreak.

Swabs are collected from

each patient and sent to

PAPAyA for sequencing the

DNA of the pathogens.

From the resulting genomic

analysis, a phylogenetic tree

of the pathogen SNP data is

derived, to determine

clusters of close genetic

relatedness and a possible

transmission route. Gene

classification for each sub-

sub-strain and associated

drug resistances can help

identify possible treatment.

short- and long-term storage for raw and

analysed genomic data).

 Reproducibility: Requires that running a

mission with identical inputs (data and

knowledge-bases) produce the same results. It

must also record all specific versions of

algorithms and databases used for each sample

processed.

 Extensibility: Creates workflows for new heath

care domains where genomics can be applied.

Enables the ability to create new mission

cohorts, add new and up-to-date genomic and

medical knowledge bases, as well as create

new access points for advanced research and

clinical trials.

 Scalability: Builds new distributed instances of

the pipelines and workflows that can meet

sudden demand increase.

 Compliance: Maintains compliance with

HIPAA, HITECH, 21 CFR part 11.

3.1 Data Acquisition

The PAPAyA platform must not only import and

manage new genomic data delivered from NGS

hardware residing in the hospital or at a service lab,

but also patient demographic and clinical data (e.g.

from an EMR), pathology and histological data from

the Laboratory Information Management System

(LIMS) and optionally radiology information from a

Radiology Information System (RIS).

3.1.1 Genomic Data

Genomic input data coming from sequencers (bar-

coded genomic data) will need to be moved to a

database for storing the raw data (FASTQ format),

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processed data (BAM format, VCF format) and all

the metadata associated with the various steps of

library preparation, sequencing and analysis.

Table 3: Genomic data storage requirements.

Data type Raw data Processed

data

whole genome 0.5-1TB 1-2 TB

whole transcriptome 10-20 GB 20-40 GB

targeted

transcriptome

1-4 GB 4-8 GB

targeted,

500 translocations

4-8 GB 8-16 GB

whole exome 20-40 GB 40-80 GB

targeted exome,

500 genes

4-8 GB 8-16GB

3.1.2 Other Patient Data

Demographic and clinical data from EMRs and other

various clinical systems, including pathology and

radiology data from the patient (in HSDP done via a

VHR and an associated EMPI), is imported,

reconciled and associated with biological sequencing

data. We can potentially implement the enterprise

master patient index (EMPI) across multiple

organizations. Our Virtual Health Record (VHR) is

capable of extracting and repurposing data from

multiple different EMR systems (e.g. EPIC, Cerner

and others).

To ensure interoperability, we implement data

standardization aspects following HL7, NAACCR,

CCR, CDA and CCD specifications. We are also

closely involved with GA4GH to keep up with the

various discussions and developments in the

working groups.

3.2 Defining Pipelines and Modules

Pipelines used in PAPAyA are defined as a sequence

of standard jobs (i.e., series of steps embodying

bioinformatics tools and commands) used to

transform data (e.g. sequencing data in FASTQ

format) from a raw state to a processed state in

which various analyses can be performed on variant

calling data in VCF format (Danecek et al. 2011).

Our first pipelines include the steps that help the

creation of phylogenetic tree for infectious diseases

and exome based pipelines for oncology.

Modules used in PAPAyA take this 'usable' data

and apply custom algorithms as well as various

knowledge-base-driven annotations to provide the

user with what is the crux of our platform: providing

clinically actionable information based on a person's

genome or biome.

The flexible design enables incorporation of a new

cancer signature or a new phylogeny building

method to be added.

The modules are implemented using univariate

or multivariate statistical methods as well as various

machine learning and computational biology

algorithms. For example, cancer subtyping can be a

specific module that leverages unsupervised learning

capabilities of the analytics platform.

The distinction that we make is that the role of

modules is to answer specific clinical questions

while pipelines perform computationally intensive

processing that generate intermediate results used as

input to modules themselves.

Certain modules include genomics-oriented

analytics, as well as NLP-related analytics. Our

clinical trial matching module relies upon a well

proven NLP engine that has parsing, tokenizing

capabilities and leverages ontologies such as

SNOMED and LOINC.

3.3 Defining Missions

The highest level of operation within PAPAyA is

accomplished with the concept of “missions”. A

mission is a run time construct that defines several

parameters in a workflow:

 Sample or list of samples

 Raw, intermediate, or processed genomic data

 Pipelines and/or analytics Modules and any

relevant input parameters

 Cohort identification

 Visualizations of complex genomic data

 Querying and Visualizations of integrated

patient and genomic data

The mission is then executed to obtain a desired

output such as performing cohort survival analysis

on a set of patients and returning Kaplan-Meier

curve data. It can also be re-run with identical or

new inputs such as updated pipelines or tools.

A mission can also encompass multiple modules

so that computational biology workflows can be

automated – for example, searching for the most

optimal number of genes that increases the

difference between survival curves of patient

subgroups.

Missions seamlessly enable flexible task

execution, detailed process documentation (for any

retrospective data auditing), and universal

applications for data from infectious disease,

oncology.

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3.4 Running Asynchronous Jobs

Running a large diversity of genomics tools

associated with pipelines and modules (C, C++,

Java, Perl, Ruby, Python, R) asynchronously in

parallel and at scale requires the platform to be able

to:

 provision and host the variety of jobs and their

ecosystems dynamically on demand,

 schedule and run these jobs as part of pre-

defined workflows asynchronously,

 pass parameters and results that are output

between jobs,

 check the status and progress of jobs,

 cancel and retry specific jobs,

 isolate jobs and associated data for each

specific tenant

3.5 Super Users

PAPAyA provides its users the ability to define

missions and workflows and design their own

pipelines based on the available tools.

It also allows them to monitor the current

processes that are being executed and alerts them to

any errors that may result from substandard input

data.

Figure 1: Super-user design and manage pipelines.

While there are default pipelines and workflows

predesigned in the system, the software enables

users to customize pipelines by mix-and-match of

tools.

3.6 Genomic Knowledge Bases

The final and one of the most important aspects of

genomic analysis is the interpretation of the genomic

results in a functional and/or a clinical context.

For example, when analysing variants for

patients, it is important to identify causal/driver

mutations or clinically actionable variants. In order

to do this, a comprehensive data repository has to be

created that can be used efficiently to annotate

genomic aberrations/alterations (variants, mutations,

CNVs, gene fusions).

This repository is created by integrating various

publicly available data sources such as dbSNP,

Refseq etc. Another important source of clinical

information is the vast pool of literature available on

PubMed.

The entire scientific community is contributing

to this source at an unprecedented level. Using a

blend of natural language processing (NLP) and

manual curation of data can help us extract

meaningful genotype-clinical associations which in-

turn can help make valuable inferences for the

patients’ response/prognosis.

The variant annotation repository of PAPAyA

incorporates all the above-mentioned sources to

annotate aberrations at four levels and thus place the

genomic results in a clinically relevant context.

Figure 2: Knowledge bases for variant annotations.

3.7 Decision Support

Multiple levels of decision support are feasible on

the platform starting with the replacement of the so-

called IVD tests for single genes (for example,

assessment of the mutation status of the BRAF or

the EGFR gene). We have developed the concept of

in-silico assays and the workflows on the platform

are intended to support both clinical practice.

 Executing already validated biomarkers

(signatures) as in-silico assays which take

certain cohort patient characteristics,

genomic/transcriptomic data and a decision

function in order to make an inference.

 Applying signatures as in-silico assays for

cancer subtyping within an organ based on

classification of tumors as well as pan-cancer.

An example would be breast cancer subtyping

into Luminal A, Luminal B, Her2, and basal

tumors based on a set of 50 genes. Similar

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types of signatures have been used for brain,

ovarian, renal cell carcinoma and others.

 Applying predictive signature that can

determine the probability of response to a

certain drug.

3.8 Clinical Research

On the clinical and pharma research side, we

envision support for workflows such as:

 Discovery of signatures based on whole

transcriptome data as well as multimodality

signatures leveraging the fact that the platform

has access to genomic, transcriptomic and

methylation data.

 Multimodal pathway evaluation in a

neoadjuvant setting to determine the effect of a

certain drug or combination of drugs on the

signalling pathways perturbed by the therapy.

 Using genome analytics combined with

machine learning to develop signatures

encompassing stratification/subtyping for best

outcome, prediction to response, prognosis, and

inference of candidates to clinical trials.

4 ARCHITECTURE

The PAPAyA platform is composed of the following

distinct subsystems:

 Public Gateway Services: a REST API

exposing all underlying functionalities to be

consumed by PAPAyA front-end application

components.

 Genomic Data Persistence: configuration, data

acquisition, read/write operations.

 Genomics Pipelines & Modules Processing:

configuration, scheduling, execution and

monitoring.

 User Management: user registration (register a

new user account, update profile information,

change and reset password information),

authenticate a user and validate user access

token.

In addition the platform is using the Philips

HealthSuite Digital Platform (HSDP) foundation

services for storage, job execution, user management

and other basic infrastructure functions (Andry et al.,

2015).

4.1 Public Gateway Services

The public gateway services form a thin layer

exposing the public PAPAyA HSDP API for

integration. This layer serves a system entry-point

supplying a client with a public REST interface. The

main logic of the public web services is only to be a

functional secure bridge between public and internal

APIs (data and metadata persistence, configuration,

monitoring, user management).

Figure 3: PAPAyA deployment architecture.

4.2 Genomic Data Persistence Services

The genomic data persistence API service is

responsible for managing genomic data and

associated metadata including:

 Create/Update Data: an interface for saving

any genomic data, including raw data and

pipeline intermediate results. The user may

specify additional user-defined metadata for

the file such as batch ID. Asynchronous

uploads of large files is also offered. The user

specifies the location of the data and the

service will copy the data to the persistence and

provide a transaction ID for follow-up.

 Read Data: retrieve data from the persistence

according to a specified filter (name, type,

pipeline step). API operations are also

available to check the status of an upload

request. The response will return the state of

the copy and additional metadata.

This internal REST API encapsulates two sets of

services: the genomic data persistence API itself and

an associated configuration API and back-end

façade integration components: genomic metadata

persistence data access object (DAO) and genomics

HSDP storage adapter that decouple the business

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logic of the API and the actual databases.

Figure 4: PAPAyA genomic data persistence services.

4.3 Genomics Processing Module

The genomics processing module is at the heart of

PAPAyA. It includes the following REST APIs:

 Genomic processing configuration: to define,

upload, schedule, run or cancel the executions

of missions and associated modules and

pipelines steps and jobs.

 Genomic processing monitoring: to get the

status of the execution of missions, modules

and pipelines.

 Genomic knowledge database services: to

update or query the genomic knowledge bases

used by the pipelines or the execution of

clinical missions.

Table 4: Genomic processing API operations subset.

Operations description

POST

Create a module

https://<host>/…/modules/{module-id}

GET

Retrieve a module

…/…/modules/{module-id}

POST

Create a pipeline

…/…/pipelines/{pipeline-id}

GET

Retrieve a pipeline

…/…/pipelines/{pipeline-id}

POST

Create a mission

…/…/missions/{mission-id}

GET

Retrieve all missions

…/…/missions

POST

Launch a mission

../../missions/{mission-

id}/sequences/{sequence-id}/executions

DELETE

Cancel the execution of a mission

…/…/executions/{execution-id}

DELETE

Cancel the execution of a mission

…/…/missions/executions/{execution-id}

GET

List all executions of a specific mission

…/…/missions/{missions-id}/executions

The Genomic processing configuration API is the

most complex one since it includes the definition of

the missions and the pipelines steps. Initially, we are

supporting simple sequential pipelines, but in the

future we plan to introduce the ability to create

complex workflows.

Figure 5: PAPAyA processing services.

4.4 User Management

According to the HIPAA regulation, protected health

information (PHI) is individually identifiable health

information including demographic data (HIPAA,

1996), but also genomic information which is very

specific to a particular individual. The disclosure of

genomic data can expose details about health status

and risks, not only for the patient, but for his

relatives as well.

PAPAyA is a tool offered to both clinicians and

researchers. While there is a motivation to increase

the amount of data to be analysed and processed for

clinical trials and research projects, it is imperative

to prevent the accidental re-identification of the data

and leaks of PHI information from cohort(s) of

patients. Frameworks such as GeneCloud (Carey et

al., 2014) used by PAPAyA try to address these

privacy concerns providing stronger security and

privacy guarantees.

The current implementation of PAPAyA is using

the Philips HSDP identity and access management

(IAM) services. This security façade API enables an

application such as PAPAyA to register and log in to

HSDP infrastructure by providing end user

authentication and authorization based on his/her

role (e.g. a researcher does not have the same data

access rights as an oncologist or a system

administrator).

The HSDP IAM services also provide means to

update user profiles, credentials, roles, groups and

token management. HSDP logging and auditing

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services are also used in tracking suspicious data

access transactions, troubleshooting and ensuring

regulatory compliance.

5 DEPLOYMENT

Most of the PAPAyA modules are implemented

using R/Bioconductor, python and Java/Spring and

can be provisioned in a Linux-based container and

deployed in a healthcare organization IT data center

or in a cloud-based environment.

Through the Philips HSDP PaaS layer, it is

possible to provision on-demand access to a shared

pool of configurable and elastic computing resources

(networks, servers, storage, services) based on the

processing needs (Andry et al., 2015).

HSDP runs on top of various types of

infrastructure as a service (IaaS) architectures and

vendors, including private or public clouds, on-

premise, or any hybrid combination.

The fact that PAPAyA is deployed on top of

HSDP makes it very scalable. Through service

brokering and specific REST APIs, PAPAyA micro

services components instance can be scaled up and

down on demand extremely quickly and jobs

associated to missions and pipelines can be

scheduled and run in parallel.

In addition to this, the status of VMS, CPU,

memory usage and storage can be monitored and

trigger notifications if needed so the DevOps and

support teams can make proper adjustments when

necessary.

Through the HSDP asynchronous jobs APIs,

pipeline steps are deployed and run inside Linux

Docker containers, which provide portable, light

footprint runtime requirements and extremely quick

deployment of managed images. As a result, any

genomics tool can be easily integrated as long as

they are initially “cloudified”; in other words, able to

read/write data inputs/outputs from and to a cloud

infrastructure securely and at scale.

6 CONCLUSIONS

Currently, there are 13 million cancer survivors in

the US alone and 1.6 million new cases every year.

Many new therapies are being introduced (Hal,

2013) that require more detailed knowledge of the

genomic makeup of the tumor in order to make a

difference in patient outcome. Some of these new

therapy agents are in the ballpark of $100,000 per

year per patient. While the cost of sequencing has

been decreasing, it is still over $1000 to profile a

tumor and a normal sample.

To match a targeted therapy with a targeted

molecule, a specific genomic candidate aberration

must be identified. Many of these aberrations are

statistically rare, their real direct effect is unknown,

and even after matching, only certain subpopulations

of patients may have better outcome. Our platform is

capable of learning from each one of these outcomes

to put this knowledge back in the service of

practicing clinicians.

Our PAPAyA platform for genome analytics,

and execution of validated tests, can be used for

decision support in clinical practice only when we

instantiate modules that perform assessment of

genomic aberrations with known clinical

significance. In addition, it can serve as the

continuous learning platform for cancer research to

provide unique value to patient care by deriving

more tailored therapy plans, aiding in decisions

around aggressiveness of treatment approach, and

recommending patients to clinical trials.

As part of the planned extensions of the

PAPAyA platform, we would like not only to

provide pre-defined workflows for the missions and

pipelines via an API, but also the ability for certain

advanced users to define these workflows through

visual graphic intuitive standards tools, including

support for Business Process Model and Notation

(BPMN 2.0) processes definition.

ACKNOWLEDGEMENTS

We are very grateful to Patrick Cheung, Yong Mao,

Konstantin Volyanskyy, Mine Danisman-Tasar, who

have been contributing to this project. Thank you to

the PAPAyA R & D team in New York who made

this project possible over many years of hard work

especially Nilanjana Banerjee, A. Janevski, Vinay

Varadan, Sitharthan Kamalakaran. Thank you to the

Philips PAPAyA and the hybrid storage teams in

Petah Tikva, Israel for help in making PAPAyA a

full commercial product. We thank Chad Evans and

the HSDP DevOps team for their contribution on

asynchronous processing. We are also very grateful

to the Iron.io team, especially Chad Arimura for

their help with the IronWorker stack.

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