BiobankCloud: a Platform for the Secure Biomedical Data Sets

BiobankCloud: a Platform for the Secure Biomedical Data Sets
BiobankCloud: a Platform for the Secure
Storage, Sharing, and Processing of Large
Biomedical Data Sets
Alysson Bessani5 , Jörgen Brandt2 , Marc Bux2 , Vinicius Cogo5 , Lora
Dimitrova4 , Jim Dowling1 , Ali Gholami1 , Kamal Hakimzadeh1 , Micheal
Hummel4 , Mahmoud Ismail1 , Erwin Laure1 , Ulf Leser2 , Jan-Eric Litton3 ,
Roxanna Martinez3 , Salman Niazi1 , Jane Reichel6 , Karin Zimmermann4
KTH - Royal Institute of Technology,
{jdowling, gholami, mahh, maism, erwinl, smkniazi}
Humboldt University
{leser, bux, joergen.brandt}
Karolinska Institute
{Jan-Eric.Litton, Roxanna.Martinez}
{Michael.Hummel, Lora.Dimitrova, Karin.Zimmermann}
LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
{bessani, vielmo}
Uppsala University
Abstract. Biobanks store and catalog human biological material that
is increasingly being digitized using next-generation sequencing (NGS).
There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud
project, we are building a Hadoop-based platform for the secure storage,
sharing, and parallel processing of genomic data. We extended Hadoop to
include support for multi-tenant studies, reduced storage requirements
with erasure coding, and added support for extensible and consistent
metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple
integration and chaining of existing tools, adaptive scheduling on Apache
Yarn, and support for iterative dataflows. Our platform also supports
the secure sharing of data across different, distributed Hadoop clusters.
The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure
2-factor authentication. Initial tests have shown that the engine scales
well to dozens of nodes. The entire system is open-source and includes
pre-defined workflows for popular tasks in biomedical data analysis, such
as variant identification, differential transcriptome analysis using RNASeq, and analysis of miRNA-Seq and ChIP-Seq data.
Biobanks store and catalog human biological material from identifiable individuals for both clinical and research purposes. Recent initiatives in personalized
medicine created a steeply increasing demand to sequence the human biological
material stored in biobanks. As of 2015, such large-scale sequencing is under
way in hundreds of projects around the world, with the largest single project
sequencing up to 100.000 genomes7 . Furthermore, sequencing also is becoming
more and more routine in a clinical setting for improving diagnosis and therapy
especially in cancer [1]. However, software systems for biobanks traditionally
managed only metadata associated with samples, such as pseudo-identifiers for
patients, sample collection information, or study information. Such systems cannot cope with the current requirement to, alongside such metadata, also store
and analyze genomic data, which might mean everything from a few Megabytes
(e.g., genotype information from a SNP array) to hundreds of Gigabytes per
sample (for whole genome sequencing with high coverage).
For a long time, such high-throughput sequencing and analysis was only available to large research centers that (a) could afford enough modern sequencing
devices and (b) had the budget and IT expertise to manage high performance
computing clusters. This situation is changing. The cost of sequencing is falling
rapidly, and more and more labs and hospitals depend on sequencing information for daily research and diagnosis/treatment. However, there is still a pressing
need for flexible and open software systems to enable the computational analysis
of large biomedical data sets at a reasonable price. Note that this trend is not
restricted to genome sequencing; very similar developments are also happening
in other medical areas, such as molecular imaging [2], drug discovery [3], or data
generated from patient-attached sensors [4].
In this paper, we present the BiobankCloud platform, a collaborative project
bringing together computer scientists, bioinformaticians, pathologists, and biobankers. The system is designed as a “platform-as-a-service”, i.e., it can be easily
installed on a local cluster (or, equally well, in a public cloud) using Karamel
and Chef8 . Primary design goals are flexibility in terms of the analysis being
performed, scalability up to very large data sets and very large cluster set-ups,
ease of use and low maintenance cost, strong support for data security and data
privacy, and direct usability for users. To this end, it encompasses (a) a scientific
workflow engine running on top of the popular Hadoop platform for distributed
computing, (b) a scientific workflow language focusing on easy integration of existing tools and simple rebuilding of existing pipelines, (c) support for automated
installation, and (d) role-based access control. It also features (e) HopsFS, a new
version of Hadoop’s Distributed Filesystem (HDFS) with improved throughput,
supported for extended metadata, and reduced storage requirements compared
to HDFS, (f) Charon, which enables the federation of clouds at the file system
level, and (g) a simple Laboratory Information Management Service with an
integrated web interface for authenticating/authorizing users, managing data,
designing and searching for metadata, and support for running workflows and
analysis jobs on Hadoop. This web interface hides much of the complexity of
the Hadoop backend, and supports multi-tenancy through first-class support for
Studies, SampleCollections (DataSets), Samples, and Users.
In this paper, we give an overview on the architecture of the BiobankCloud
platform and describe each component in more detail. The system is currently
under development; while a number of components already have been released
for immediate usage (e.g., Hops, SAASFEE), a first overall platform release is
planned for the near future. The system is essentially agnostic to the type of data
being managed and the types of analysis being performed, but developed with
genome sequencing as most important application area. Therefore, throughout
this paper we will use examples from this domain.
Related Work
Our platform covers a broad set of technologies providing components that solve
problems in the areas of security in Hadoop, sharing data, parallel data processing, and data management. Here, we focus on related work in the area of parallel
data analytics and discuss some general and some domain-specific solutions. In
general, research in platforms for large-scale data analysis is flourishing over the
last years. General purpose data parallel processing systems like Spark [5] or
Flink [6] support efficient and distributed data analysis, but focus on providing
SQL support for querying data. This leads to some design choices that make supporting scientific analysis pipelines (or scientific workflows) rather cumbersome.
In contrast, specialized scientific workflow management systems like Taverna,
Kepler, or Galaxy, typically neglect the Big Data aspect, i.e., they are not able
to scale workflow executions efficiently over large clusters [7].
The lack of a flexible and open platform for running complex analysis pipelines
over large data sets led to the development of a number of highly specialized
systems. For instance, tools like Crossbow [8] perform one specific operation (sequence alignment) in a distributed manner, but cannot be used for any other
type of analysis. Also for the most important types of complex analysis, like
sequence assembly or mutation detection and evaluation, specialized systems
exist. Some very popular systems, like GATK [9], only support parallelism on
a single machine and cannot scale-out to make use of additional computational
resources. Technically more advanced solutions like Adam [10], Halvade [11],
Seal [12], and PigSeq [13] show much better scaling, but are not general purpose solutions but specifically developed for one type of analysis. Appropriate
models of data security and privacy are not supported in any of the systems
we are aware of. There are frameworks that enhance Hadoop with access control, such as Apache Ranger that supports attribute-based access control and is
general and expressive enough to be applied to most Hadoop services. However,
attribute-based access control does not have high enough throughput to support
authorization of all HDFS operations at the NameNode or all application re-
quests at the ResourceManager. Overall, we see a clear lack of a flexible, open,
secure, and scalable platform for scientific data analysis on large data sets.
LIMS with 2-Factor Authentication
Fig. 1: BiobankCloud Architecture
Our platform has a layered architecture (see Figure 1). In a typical installation, users will access the system through the web interface with 2-factor authentication. From there, she can access all services, such as the enhanced file system
HopsFS (see section 5), the workflow execution engine SAASFEE (see section
6), the federated cloud service CharonFS (see section 8), and an Elasticsearch
instance to search through an entire installation. SAASFEE is built over YARN,
while CharonFS can use HopsFS as a backing store. HopsFS and Elasticsearch
use a distributed, in-memory database for metadata management. Note that all
services can also be accessed directly through command-line interfaces.
Data Sets for Hadoop
The web interface has integrated a LIMS to manage the typical data items
inside a biobank, and to provide fine-grained access control to these items. These
items are also reflected in the Hadoop installation. Specifically, BiobankCloud
introduces DataSets as a new abstraction, where a DataSet consists of a related
group of directories, files, and extended metadata. DataSets can be indexed and
searched (through Elasticsearch) and are the basic unit of data management in
BiobankCloud; all user-generated files or directories belong to a single DataSet.
In biobanking, a sample collection would be a typical example of a DataSet.
To allow for access control of users to DataSets, which is not inherent in the
DataSet concept, we introduce the notion of Studies. A Study is a grouping of
researchers and DataSets (see Figure 2) and the basic unit of privacy protection
(see below).
Fig. 2: Study1 has John and Mary as users and includes DataSet1, while Study2
has only John as as a user and includes DataSet1, DataSet2, and DataSet3.
Security Model
The BiobankCloud environment deploys strong security features for concerns
such as confidentiality, integrity, and non-repudiation [14] of data access. This
includes authentication, authorization, and auditing. The system allows defining
different roles with different access privileges. In designing the system, we applied the Cloud Privacy Threat Modeling [15] approach to identify the privacy
requirements of processing sensitive biomedical data. This model implements a
data management policy that aligns with the European data protection directive. The project further implements tools to ensure the correct allocation of
legal responsibilities for the data processed within the platform.
Figure 3 shows the different components of the employed security mechanisms. All BiobankCloud services are protected behind the firewall and only
accessible through the secure interfaces over HTTPS channels.
2-Factor Authentication
The authentication services map the person accessing the platform to a user identity. We provide 2-factor authentication using smart mobile devices or Yubikey9
hardware tokens to support different groups of users. Users send authentication
requests via a Web browser to the authentication service that runs instances
of the time-based one-time password (TOTP) and Yubikey one-time password
(YOTP) protocols.
In a mobile scenario, a user supplies an one-time generated password by a
commercial authenticator in addition to a simple password that was decided
during the account registration. The login page authenticates the user to the
Yubikey Manual,
Fig. 3: Secure access to the BiobankCloud via a web front-end.
platform using the TOTP module (Time-Based One-Time Password) as an implementation of the RFC 6238. In contrast, a Yubikey user would enter the
Yubikey device into a USB port. The user enters the simple password that was
decided during the account registration and pushes the Yubikey button. The Yubikey login page authenticates the user to the platform via the YOTP module.
Role-based Access Control
The access control component ensures authorized access to all data and services
within a platform installation. Therefore, the system defines several roles10 which
are granted certain access rights to certain studies. Examples are a DataOwner
(users who may create new data sets), a DataScientist (users who can run workflows on the data), or an auditor (users with access to audit trails for auditing).
DataSets technically can be shared between studies, and users may have different roles in different studies. We use the access control mechanism of HopsFS to
implement the Study- and DataSet-based authorization model.
Auditing Service
Finally, the auditing service enables the platform administrator or an external
auditor to discover the history of accessing the platform to detect any violation to
a policy. It includes several contexts such as role, account, study, and login audits.
The concrete roles should be seen as implementations of the European Data Protection Directive.
The secure login service assures that actions that are taken by the users are
registered for tracing and auditing purposes. Each log event contains information
such as initiator, target, IP/MAC addresses, timestamp, action, and outcome.
Hadoop Open Platform-as-a-Service (Hops)
A full installation of our platform builds on an adapted distribution of the
Hadoop File System (HDFS), called HopsFS, which builds on a new metadata
management architecture based on a shared-nothing, in-memory distributed
database (see Figure 4). Provided enough main memory in the nodes, metadata
can grow to TBs in size with our approach (compared to 100GB in Apache
HDFS [16]), which allows HopsFS to store 100s of millions of files. The HopsFS
architecture includes multiple stateless NameNodes that manage the namespace
metadata stored in the database (see Figure 4a). HopsFS’ clients and DataNodes are aware of all NameNodes in the system. HopsFS is highly available:
whenever a NameNode fails the failed operations are automatically retried by
clients and the DataNodes by forwarding the failed requests to a different live
NameNode. We use MySQL Cluster [17] as the database, as it has high throughput and is also highly available, although any distributed in-memory database
that supports transactions and row level locking could be used. On database
node failures, failed transactions are re-scheduled by NameNodes on surviving
database nodes.
(a) HopsFS
(b) HopsYARN
Fig. 4: HopsFS and HopsYARN architectures.
We ensure the consistency of the file system metadata by implementing serialized transactions on well-ordered operations on metadata [18]. A leader NameNode is responsible for file system maintenance tasks, and leader failure triggers
our own leader-election service based on the database [19]. HopsFS can reduce
the amount of storage space required to store genomic data, while maintaining
high availability by storing files using Reed-Solomon erasure coding, instead of
the traditional three-way replication used in HDFS. Erasure-coding can reduce
disk space consumption by 44% compared to three-way replication. In HopsFS,
an ErasureCodingManager runs on the leader NameNode, managing file encoding and file repair operations, as well as implementing a policy that places file
blocks on DataNodes in such a way that ensures that, in the event of a DataNode
failure, affected files can still be repaired.
Designing, Indexing, and Searching Extended Metadata
We store genomes in HopsFS. However, biobanks require much more extensive
metadata for genomes than is available for HDFS files. The limited metadata
available in HDFS files includes file size, time last modified, and owner. We also
need information such as the sample and sample collection the genome belongs
to, the type of sample, and donor information. Our LIMS provides a UI tool
for biobankers who are not programmers to design their own extended metadata
that is linked to genomes, sample collections, DataSets, or Studies. This extended
metadata is stored in the same database as the file system metadata and the
integrity of the extended metadata is guaranteed using foreign keys to the file or
directory the metadata refers to. To make this extended metadata searchable,
we asynchronously and transparently replicate it to Elasticsearch. This indexing
of extended metadata enables free-text searching for samples.
HopsYARN is our implementation of Apache YARN, in which we have (again)
migrated the metadata to MySQL Cluster. We partitioned YARN’s ResourceManager into (1) ResourceTracker nodes that process heartbeats from and send
commands to NodeManagers, and (2) a single scheduler node that implements
all other ResourceManager services, see Figure 4. If the scheduler node fails, our
leader election service will elect a ResourceTracker node as the new scheduler
that then loads the scheduler state from the database. HopsYARN scales to handle larger clusters than Apache YARN as resource tracking has been offloaded
from the scheduler node to other nodes, and resource tracking traffic grows linearly with cluster size. This will, in time, enable larger numbers of genomes to
be analyzed in a single system.
To process the vast amounts of genomic data stored in today’s biobanks, researchers have a diverse ecosystem of tools at their disposal [20]. Depending on
the research question at hand, these tools are often used in conjunction with one
another, resulting in complex and intertwined analysis pipelines. Scientific workflow management systems (SWfMSs) facilitate the design, refinement, execution,
monitoring, sharing, and maintenance of such analysis pipelines. SAASFEE [21]
is a SWfMS that supports the scalable execution of arbitrarily complex workflows. It encompasses the functional workflow language Cuneiform as well as
Hi-WAY, a higher-level scheduler for both Hadoop YARN and HopsYARN. See
Figure 5 for the complete software stack of SAASFEE.
Fig. 5: The software stack of the scientific workflow management system SAASFEE, which comprises the functional workflow language Cuneiform as well as
the Hi-WAY workflow scheduler for Hadoop. Cuneiform can execute foreign code
written in languages like Python, Bash, and R. Besides Cuneiform, Hi-WAY
can also interpret the workflow languages of the SWfMSs Pegasus and Galaxy.
SAASFEE can be run both on Hops as well as Apache Hadoop. SAASFEE and
HopsYARN can be interfaced and configured via the web interface provided by
the LIMS.
Analysis pipelines for large-scale genomic data employ many different software tools and libraries with diverse Application Programming Interfaces (APIs).
At the same time the growing amounts of data to be analyzed necessitate parallel and distributed execution of these analysis pipelines. Thus, the methods
for specifying such analysis pipelines need to meet both concerns – integration
and parallelism equally. The functional workflow Language Cuneiform has been
designed to meet these requirements [22]. Cuneiform allows the integration of
software tools and libraries with APIs in many different programming languages.
This way, command-line tools (e.g., Bowtie [23]) can be integrated with similar
ease as, for instance, R libraries (e.g., CummeRbund [24]). By partitioning large
data sets and processing these partitions in parallel, data parallelism can be
exploited in addition to task parallelism to speed up computation. Cuneiform
automatically detects and exploits data and task parallelism in a workflow specification. Editing and debugging workflows is supported by the tools and visualization features provided with the Cuneiform interpreter.
Hi-WAY is a higher-level scheduler that enables the execution of scientific
workflows on top of YARN. Hi-WAY executes each of the tasks comprising the
workflow in a separate container, which is the basic unit of computation in
YARN. Input, output, and intermediate files created during a workflow execution
are stored in Hadoop’s distributed file system HDFS. Consequently, Hi-WAY
benefits form the fault-tolerance and scalability of the Hadoop ecosystem. It has
been evaluated to scale to more than 600 concurrent tasks.
Hi-WAY provides a selection of established scheduling policies conducting
task placement based on (a) the locality of a task’s input data to diminish network load and (b) task runtime estimation based on past measurements to utilize
resources efficiently. To enable repeatability of experiments, Hi-WAY generates
exhaustive provenance traces during workflow execution, which can be shared
and re-executed or archived in a database. One of the major distinctive features
of SAASFEE is its strong emphasis on integration of external software. This is
true for both Cuneiform, which is able to integrate foreign code and commandline tools, and Hi-WAY, which is capable of running not only Cuneiform workflows, but also workflows designed in the SWfMSs Pegasus [25] and Galaxy [26].
Fig. 6: Static call graph of variant calling workflow. Box-shaped nodes represent
distinct tasks, edges represent data dependencies.
We demonstrate the applicability of SAASFEE for large-scale biobank use
cases by discussing an example workflow for variant calling. In this use case
we try to discover differences in the genome of an organism in comparison to
a reference genome. Furthermore, the discovered differences are annotated with
database information to ease their interpretation by a specialist. Figure 6 shows
the static call graph for this workflow which was automatically derived from
the workflow script written in Cuneiform. It enumerates the different data pro-
cessing steps and gives a coarse overview of task dependencies. Each of these
tasks integrates a different command-line tool which is wrapped in Cuneiform
and called like a function. The workflow is extensible in the way that each intermediate result can serve as the input to a new sub-workflow and tools can
be differently parametrized and, if file formats are standardized, exchanged with
different tools.
As a first step to test the scalability of SAASFEE for large-scale use cases
which are relevant for biobanks, we ran this variant calling pipeline on 10 GB
of compressed whole genome sequencing reads from the 1000 Genomes Project.
These reads were aligned against a reference genome, variants were called, and
the resulting sets of variants were annotated using publicly available databases.
Figure 7 shows the scalability of this workflow. Within the limits of the setup
chosen, linear scaling behavior could be achieved for the variant calling workflow.
Fig. 7: Scalability experiment for the SAASFEE software stack. A variant calling
workflow has been scaled out on up to 24 nodes. Both axes, the runtime in
minutes and the number of nodes are on a logarithmic scale (published in Brandt
et al. 2015 [22]).
Workflows Implemented in BiobankCloud
Next generation sequencing, a technology first introduced to the market in 2005,
has revolutionized biological research during the last ten years [27]. This technology allows scientist to study not only the genome of any species, but also the
transcriptome and the epigenome. We have implemented several pipelines for
the analysis of the most common types of next generation sequencing data into
the BiobankCloud.
Variant Calling pipeline: Genetic variations represent changes in the order
of the bases in a DNA sequence and variations in the human genome play a
decisive role in human disease. The most common variations in the genome
are the single nucleotide variations and short insertions and deletions. For this
pipeline, whole genome sequencing and/or whole exome sequencing data can
be chosen as input data and the workflow was derived from Thalheim [28].
Figure 8 shows a schematic overview on this Variant Calling pipeline built in
Fig. 8: Subsequent processing steps in BiobankCloud’s Variant Calling pipeline.
Gene Expression pipeline: This pipeline uses RNA-Seq data as input and
enables the user to study differential expression on different levels such as genes
and transcripts. Furthermore, it detects differential splicing and promoter use
between two or more groups of interest. The pipeline was implemented according
to Trapnell et al. [29, 30].
ChIP-Seq pipeline: ChIP-Seq (Chromatin immunoprecipitation coupled
with high-throughput sequencing) is the standard technique for studying the
genome-wide binding profiles of DNA-binding proteins, e.g. transcription factors,
as well as the distribution of histone modifications. ChIP-Seq NGS data are the
input data for this pipeline and the workflow is described by Dimitrova et al. [31]
was used.
microRNA pipeline: microRNAs are short expressed RNAs that play a
key role in many biological processes and in different diseases. Our pipeline for
analysis of the expression profiles of microRNAs is based on the publication of
Kozubek et al. [32] and is using small RNA-Seq data as input.
Sharing Data Between Clusters with Charon
An innovative aspect of the BiobankCloud PaaS is the capability to interconnect
several PaaS deployments in a self-managed federation [33], which enables data
sharing between biobanks and other BiobankCloud users. These federations can
also include public storage clouds, allowing biobanks to extrapolate the capacity
of their private resources to the virtually infinite capacity of clouds without
endangering the individuals’ privacy. These capabilities are implemented through
a novel distributed storage system called Charon.
Charon is a cloud-backed file system capable of storing and sharing big data
in a secure and reliable way using multiple cloud providers and storage repositories. It is secure and reliable because it does not require trust on any single
provider, and it supports the storage of different data sets in distinct locations to
comply with required privacy premises and regulations. Three types of data locations are supported in Charon: cloud-of-clouds, single (public) storage cloud,
and private repository (e.g., a private cloud). Furthermore, the private repository can be a disk in the client’s machine running the Charon file system client,
a disk in a remote machine (e.g., a private data center), or any other distributed
storage system (e.g., HopsFS).
Private repositories have variable dependability and are subject to local infrastructure restrictions. Charon transfers data from one private repository to
another securely through encrypted channels. The single-cloud scenario allows
the controlled data sharing between two biobanks, but its dependability directly
depends on a single entity – the chosen public cloud provider. The multi-cloud
(or cloud-of-clouds) data replication is a possible location to store data, and is
where Charon stores all its metadata securely. It avoids having any cloud service provider as a single point of failure, operating correctly even if a fraction
of the providers are unavailable or misbehave (due to bugs, intrusions, or even
malicious insiders) [34]. Independently from the chosen data location, the data
stored in Charon can be shared among mutually-trusted system users.
Figure 9 illustrates a deployment scenario where two biobanks store their data
in diverse storage locations—private repositories, single public cloud providers,
and a resilient cloud-of-clouds. In this scenario, the namespace tree has six nodes:
directories d1 and d2, and files A, B, C, and D. The namespace is maintained in
the cloud-of-clouds, together with file B. File D, less critical, is kept in a single
public cloud. File A is stored locally because it cannot leave biobank 2 (e.g., due
to legal constraints). File C is shared between the two sites (e.g., in the same
legal framework), thus being stored in both of them.
Two distinguishing features of Charon are its serverless design and its efficient management of large files. The former means that no client-managed server
is required in the cloud for Charon, which incurs in reduced maintenance costs.
The latter is achieved by employing state-of-the-art techniques for data management such as: working with data blocks instead of whole files, prefetching data
blocks, multi-level cache, data compression, and background writes.
Resilient Cloud‐of‐Clouds Storage
metadata d1
Public Cloud A
Biobank 1
Public Cloud B
Biobank 2
Fig. 9: Charon overview.
Automated Installation
BiobankCloud supports automated installation. It can easily be installed by nontechnical users who can click-through an installation using only a file that defines
a BiobankCloud cluster and account credentials for a cloud computing platform.
Our solution is based on the configuration management platform Chef [35]. The
main reason we adopted Chef is that it provides support for both upgrading
long-lived stateful software and parametrized installations. This contrasts with
container-based approaches, such as Docker, that are not yet suitable for online
upgrading of stateful systems and also have limited support for parametrization
and orchestration. Chef has, however, no support for orchestrating installations.
For distributed systems with many services, such as BiobankCloud, there is
often a need to start and initialize services in a well-defined order, that is, to
orchestrate the installation and starting of services. To this end, we developed
an orchestration engine for Chef called Karamel.
In Chef, the basic unit of installation is a recipe, a program written in a
domain-specific language for Ruby. In keeping with best practice, in BiobankCloud, each recipe corresponds to a single distributed system service. Recipes are
grouped together into software artifacts called cookbooks. We have written Chef
cookbooks and services for all our software components. Our Hadoop platform
stores its single cookbook in a repository on GitHub called hops-hadoop-chef.
For each Hadoop service, our hops cookbook has a recipe that both installs
and starts it. The recipes include the NameNode (hops::nn), the DataNode
(hops::dn), ResourceManager (hops::rm), and NodeManager (hops::nm). Hops
also uses a database called NDB, with its cookbook stored in ndb-chef. Similarly, our LIMS (hopsworks) and SAASFEE (hiway) have their own repositories.
Karamel augments cookbooks with orchestration rules for recipes defined in a
file called Karamelfile. The Karamelfile defines what services need to be running
within the cluster (and locally) before a given recipe is executed.
Provided the set of all recipes that need to be installed on all nodes, as well as
the orchestration rules for the cookbooks, Karamel can take a declarative cluster
definition and execute it to install the BiobankCloud platform. In Listing 1.1,
we can see a definition of a large BiobankCloud cluster, consisting of 110 nodes.
The cluster is defined for Amazon Web Services (ec2 ), but that section of the file
can be modified to deploy the same cluster in a different cloud platform, such as
OpenStack. The cookbooks section defines where the cookbook software artifacts
are located. Karamel uses this information to download orchestration rules for
the cookbooks and metadata, thus enabling smaller cluster definition files, since
they do not need their own orchestration rules. The attrs section defines a single
parameter for the user that will run the LIMS. In production installations, the
attrs section typically contains more extensive configuration parameters. Finally
the groups section defines groups of nodes that install the same software stack.
The software stack for each group is defined as a list of recipes. Here, we have
four different groups: one for the front-end LIMS (ui), one for the Hadoop and
database management services (mgmtnodes), one for the database (datanodes),
and one for the storage and processing nodes (workers).
In this paper, we introduced the BiobankCloud platform, which provides many
features necessary for biobanks to adopt Hadoop-based solutions for managing
NGS data. Critical security features that we have introduced for managing sensitive data include multi-tenancy to isolate Studies and 2-factor authentication.
Next-generation data management systems for NGS data must be massively
scalable. We introduced our scalable storage service, HopsFS, our processing
framework, HopsYARN, and our framework for scalable bioinformatics workflows, SAASFEE. We also provide metadata design and search services, while
ensuring the integrity of metadata. Finally, in Charon, we showed how we can
leverage public clouds to share data securely between clusters. BiobankCloud’s
secure and scalable open platform software is helping to remove the biobank
bottleneck and enabling population-level genomics.
This work is funded by the EU FP7 project “Scalable, Secure Storage and Analysis of Biobank Data” under Grant Agreement no. 317871.
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Listing 1.1: Karamel Cluster Definition for BiobankCloud
name : BiobankCloud
ec2 :
type : m3 . medium
r e g i o n : eu−west −1
cookbooks :
ndb :
g i t h u b : " hopshadoop /ndb−c h e f "
hops :
g i t h u b : " hopshadoop / hops−hadoop−c h e f "
hopsworks :
g i t h u b : " hopshadoop / hopsworks−c h e f "
hiway :
g i t h u b : " b i o b a n k c l o u d / hiway−c h e f "
attrs :
hopsworks :
u s e r : bbc
groups :
ui :
size : 4
recipes :
− hopsworks , hiway : : c l i e n t
mgmtnodes :
size : 4
recipes :
− hops : : nn , hops : : rm , ndb : : mgmd, ndb : : mysqld
datanodes :
size : 2
recipes :
− ndb : : ndbd
workers :
s i z e : 100
recipes :
− hops : : dn , hops : : nm, hiway : : worker
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