Genomics With Cloud Computing

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 04, APRIL 2015

ISSN 2277-8616

Genomics with Cloud Computing
Sukhamrit Kaur, Sandeep Kaur
Abstract: Genomics is study of genome which provides large amount of data for which large storage and computation power is needed. These issues
are solved by cloud computing that provides various cloud platforms for genomics. These platforms provides many services to user like easy access to
data, easy sharing and transfer, providing storage in hundreds of terabytes, more computational power. Some cloud platforms are Google genomics,
DNAnexus and Globus genomics. Various features of cloud computing to genomics are like easy access and sharing of data, security of data, less cost
to pay for resources but still there are some demerits like large time needed to transfer data, less network bandwidth.
Index Terms: Cloud Computing, DNA, DNAnexus, EC2, Genomics.
————————————————————

1 INTRODUCTION
GENOMICS is study of all the genes that collectively make up
an organism. All genes collectively are known as genome. So
genomics is study of genome which provides information of
features of organisms genomics can be divided into three
categories: Structural, Functional and Comparative Genomics.
Structural genomics means determining the three dimensional
structures all proteins in genome known as proteome and
understanding the biological meaning of proteome. To
determine structure of protein, some methods like nuclear
magnetic resonance NMR are used and its main application is
drug design to treat some diseases. Functional genomics is to
determine functions of genes and proteins. Comparative
genomics involves comparing genomes of different organisms
i.e. genes, genomes and proteins. It is used to see that how
much two species are closely related to each other that mean
to study similarity and differences of various organisms. [1]
Some genome analysis tools are BLAST and PSI-BLAST,
SSEARCH, CUSHAW, FASTA, CASAVA, SAM, IMPALA, and
HMMER. Genome analysis includes the detection of similarity
in two or more sequences which is important in research and
diagnostic work. [2] With genome sequencing some
challenges in the biomedical field have arose like, large
amounts of data produced from sequencing, data transfer,
access control and management which is difficult for
researchers with limited computational power and storage
space. [3] Human DNA is made of approximately 3 billion base
pairs which represents nearly 100 gigabytes (GB) of data, the
equivalent of 102,400 photos. By the end of 2011, the global
annual sequencing capacity was estimated to be 13 quadrillion
bases and counting, enough data to fill a stack of DVDs two
miles high. To store, process and analyse this big data is very
expensive which leads to the use of cloud computing where
users can hire infrastructure on a „„pay as you go‟‟ basis,
thereby avoiding large capital infrastructure and maintenance
costs.

__________________________
 Sukhamrit Kaur is currently pursuing masters degree
program in computer science in Guru Nanak Dev
University, India, PH-+919872455529.
E-mail: [email protected]
 Sandeep Kaur is currently working as assistant professor
in Guru Nanak DevUniversity Regional Campus
Gurdaspur, India

These services of hardware and computational power can be
provided using user friendly web interfaces. [4] A solution for
this problem is cloud computing which is number of networks
of computers equipped together by the Internet to work on a
specific computing problem. Microsoft, Google and Amazon
are providing cloud computing services for genomics which is
a cost effective solution for researchers. [5] Currently, the
leading cloud service provider is Amazon Web Services. They
offer many resources that help to store and analyze the data
produced by whole genome sequencing. Amazon S3 used to
store the data in a secure, encrypted, redundant environment
and EC2 provides a flexible, scalable and stable computational
environment. Users can create virtual machines of different
sizes like machine with 60 GB of RAM and 88 cores parallel
workflow Elastic Map Reduce provides a framework for
parallelizing jobs, so that tasks that may have taken days
before can now be performed in a matter of hours. All these
services collectively provide research institutions with the
necessary capacity to store and analyze sequencing data. [3]
In recent years, Google and Amazon have struggled to tackle
the problem of big data which are handled by their specific
cloud applications [4] like the Amazon S3 cloud computing
service provides is used for storing and retrieving amount of
genome data. Cloud computing is a rising technological
paradigm enabling researcher to dynamically virtual machine
that will be better than large computational tools in
bioinformatics. It offers fast scaling, less management, pay-asyou-go pricing, code reproducibility and the potential for 100%
utilization. [6]

2 PROBLEMS

IN

GENOMICS

SOLVED

BY

CLOUD

COMPUTING

2.1 Challenge of Big Data
To understand the living system, large amount is biological
information is used. For example, data produced from large
projects like 1000 Genomes will give information in Petabytes.
Some other challenges are: data transfer, access control and
management; standardization of data formats; and accurate
modelling of biological systems by integrating data from
multiple dimensions.
2.2 Data Transfer, Sharing, Access control and
management
Analysis can increase the size of the raw data, and results of
analysis can be a part of information needed and stored in one
place. So, it is important to efficiently move these big data over
the internet, for providing access control if the data is stored
centrally to reduce storage costs and to properly manage large
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 04, APRIL 2015

amount of data. Current solution is to store data on hard drives
and ship it physically to customers, or upload the data onto
local, temporary servers and until the hard drives arrive at lab,
analysis cannot begin and data are only delivered to a single
location that may be needed by many researchers at many.
Therefore, data transfer, storage and sharing are time
consuming and effort consuming processes.

2.3 Standardizing Data Format
Different centres generate data in different formats, and some
analysis tools require data to be in particular formats or require
different types of data to be linked together. Reformatting and
re-integrating data can waste time. So for sequence analyses
across different platforms requires tools to be adapted to
specific platforms.
2.4 Expensive and Inflexible Access to Computing
Power
To get more computing power, money needs to be spent on
hardware.

3 CLOUD PLATFORMS FOR GENOMICS
3.1 Google Genomics
Google genomics is a cloud platform for storing, processing,
exploring and sharing data produced by genomics. It allows
you to store alignments for one or many genomes, process
data produced by genomics in minutes or hours by using
parallel computing like MapReduce of Amazon cloud services,
explore data, share genomic data between research groups.
Sources of data include dataset (grouping of genomic data
and analysis), reads (nucleotide sequence produced by
sequencer with quality score and metadata), read group
set(collection of reads and metadata), variants(positions of
genetic differences), jobs(collections of large data). This
platform provides managing datasets, sharing datasets,
importing reads, searching for reads and exporting of reads, to
work with genomics. [9]
3.2 DNAnexus
DNAnexus provides for solution for dna sequencing and
researchers who work on data produced by sequencing. This
platform solves the problem of analysis and management of
this produced data. It runs on Amazon Web Services.
Thousands of CPUs and 100 of Petabytes is available through
DNAnexus, by accessing only those resources that are
required according to our need. Its various data management
features are given below:
 Infrastructure is provided on demand that means only that
amount needed of computing power and storage is provided
to user.
 It ensures the quality of sequence data before providing it to
user.
 It gives access to various bioinformatics tools for
sequencing.
 It is designed for the large growing sequence data.
Some features that benefits researchers are given below:
 Infrastructure is provided on demand that means only that
amount needed of computing power and storage is
provided to user.
 It debugs sequencing problems and provides high quality
data.

ISSN 2277-8616

 Long term storage of sequence data and provides fast or
instant access to that data.
 Mapping of sequence data to genome of your choice.
 Provides fast visualization of genomic data.
 RNA-seq(method to measure gene expression) and ChIPseq(study interaction of DNA with other molecules)
applications can be accessed though this platform with one
click.
DNAnexus provide security to sequencing data
 To manage and monitor security of data, it uses ISO 27002
international security standard.
 By allowing customer control i.e. user decides that how
data should be processed, who should access it, and what
data should be deleted
 Data centres and offices are protected by firewalls
[10]

3.3 Globus Genomics
It is combination of algorithms, data management tools,
graphical workflow environment, and good computing
infrastructure. It provides us with features of fast, reliable and
easy to use data movement capabilities. Globus Genomics
also uses Amazon Web Services for computational analysis
for adjusting the resources according to research
requirements, provide almost infinite pool of available
resources and charges only for the resources used. [8]

7 MERITS AND DEMERITS
COMPUTING IN GENOMICS

OF

CLOUD

7.1 Merits
 Cloud computing provides appropriate platforms for
computational problems in today‟s genomics research
that is very difficult with previous or available methods.
 Pay only according to your usage i.e. of only those
resources that you need and use. Like if you use 10 GB
of storage then pay only of that but not of full hard drive
price. Backup and recovery are included in this cost.
 It reduces computation time like from one day to one
hour with very less cost.
 It also solves the problem of transferring and sharing
data with other genomic researchers by placing data on
cloud that can be accessed by many researchers from
any place. [7]
7.2 Demerits
 Reduced control over distribution of computation and
resources.
 Large time needed to transfer large data to and from
cloud.
 It provides easy access to resources but data transfer
problems still remains.
 Problem of network bandwidth makes transfer of large
data difficult [7]

4

CONCLUSION

Genomics produces large amount of sequence data known as
big data that is not easy to process and manage on normally
using machines. So, cloud computing becomes the solution for
these problem by providing appropriate platform more
computational power for processing and more storage for
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 04, APRIL 2015

ISSN 2277-8616

data, easy and less costly access to resources required for
processing and storing data.

Acknowledgment
Nothing concrete can be achieved without an optimum
combination of inspection and perspiration. Our sincere
appreciation and gratitude goes to GOD who HAS always
helped us in good and bad.

REFERENCES
[1] Amanda Knowles et.al. Available: wiki.brwon.edu.
[2] M. Femminella et.al. (2014). “The ARES project: cloud
services for medical genomics”, pp. 15-22.
[3] (2015). Cloud Computing. Available: icbi.georgetown.edu.
[4] A. D. Driscoll et.al. (2013). „Big data‟, hadoop and cloud
computing in genomics. Vol. 46, pp 774-781.
[5] (2013). Answers to Genome analysis may be in the
clouds. Available: www.genome.gov.
[6] P. Kudtarkar (2010). Cost Effective cloud computing: A
case study using the comparative genomic tool, Roundup.
pp 197-203.
[7] E.E.Schadt et.al. (2010) Computational solutions to large
scale data management and analysis. 11(9), pp 647-657.
[8] Pediatric brain research laboratory uses Globus genomics
to overcome its hurdles. Available at ww.globus.org.
[9] Dr. G. Mirzaa (2013). Globus genomics a solution as
cutting edge as our research. Available: www. Globus.org.
[10] (2010). Available: classic.dnanexus.com.

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