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International School ofBiomedicalDiagnostics
In This Issue
Upcoming Features
Digital Lab ManagementóJuly 25
MetabolomicsóSeptember 19
GenomicsóOctober 3
Big BiologicalImpacts fromBig Data
LIFE SCIENCE TECHNOLOGIESBIOTECH AND PHARMA
Produced by the Science/AAAS Custom Publishing Office
In This Issue
Upcoming Features
Postdocs: Broadening Your Skills óAugust 22
Faculty: Professionalism 1óSeptember 12
Faculty: Professionalism 2óOctober 3
Produced by the Science/AAAS Custom Publishing Office
An Explosion ofBioinformatics
Big data is everywhere, and its in�uence and practical
omnipresence across multiple industries will just continue
to grow. For life scientists with expertise and an interest in
bioinformatics, computer science, statistics, and related
skill sets, the job outlook couldnít be rosier. Big pharma,
biotech, and software companies are clamoring to hire
professionals with experience in bioinformatics and the
identi�cation, compilation, analysis, and visualization of
huge amounts of biological and health care information.
With the rapid development of new tools to make sense of
life science research and outcomes, spurred by innovative
research in bioinformatics itself,
scientists who are entranced by
data can pursue more career op-
tions than ever before. See the
full story on page 1303.
FOCUS ON CAREERSBIG DATA
In the life sciences, data can
come in many forms, including
information about genomic se-
quences, molecular pathways,
and different populations of
people. Those data create a
potential bonanza, if scientists can overcome one stum-
bling block: how to handle the complexity of information.
Tools and techniques for analyzing big data promise to
mold massive mounds of information into a better under-
standing of the basic biological mechanisms and how the
results can be applied in, for example, health care. See
the full story on page 1298.
The meeting theme reflects on the information transformation
happening in science and technology brought about by
recent advances in organizing, visualizing, and analyzing data.
Save the Date: 12–16 February 2015
The AAAS Annual Meeting is interdisciplinary and inclusive.
Each year, thousands of leading scientists, engineers, educators,
policymakers, and journalists from around the world gather to
discuss recent developments in science and technology.
Registration opens in early August.
www.aaas.org/meetings
I N N O V A T I O N S , I N F O R M A T I O N , A N D I MAG I N G
1298 sciencemag.org/products SCIENCE
LIFE SCIENCE TECHNOLOGIES
BIG DATA
Produced by the Science/AAAS Custom Publishing Office
ìBig dataî is one of todayís hottest concepts, but
it can be misleading. The name itself suggests
mountains of data, but thatís just the start. Overall,
big data consists of three vís: volume of data, velocity of pro-
cessing the data, and variability of data sources. These are the
key features of information that require big-data tools.
Although biologists have spent decades struggling to collect
enough data, says Keith Crandall, director of the Computational
Biology Institute at George Washington University in Ashburn,
Virginia, ìthe new bottlenecks in biology are big-data issues.î
()*+�*��+�����*��*���)*��*�+*��*� )*���+�*������*
sequence, announced in April 2002, utilized the expertise, in-
frastructure, and people from 20 institutions and took 13 years
of work and about $3 billion to determine the order of approxi-
mately three billion nucleotides. Now, says Crandall, ìWe can
sequence a human genome for $1,000, and we can generate
more than 320 genomes per week!î
As life scientists explore more ways to deal with big dataís
volume, velocity, and variability, they are starting to develop
new approaches to analyzing information.
Ever-Expanding Volume
When it comes to collecting large volumes of information about
human biology, the pharmaceutical industry started battling
large data sets decades ago. As Jason Johnson, associate vice
� �)���*�� *)�����*��� �+�)*+*Merck Research Labs in
Boston, Massachusetts says, ìMerck has for many years had
clinical trials with thousands of patients, and the ability to query
�����)*��*���������*�+��* ��� �)�*+��*���*��*�+��*���*
generation genomic sequencing that can create a terabyte of
data per sample.î
To deal with so much data, even large pharmaceutical com-
�+��)*����*�����*�� *��+�����*� ��*���� )������+�*��+����*
������� +��)*+*Roche in Basel, Switzerland, says, ìA cen-
turyís worth of Roche R&D data were more than doubled in
�� !�� �*�*+*)������+ ���)�+������� ���*�*)�"���������-
� ��)*����+��� ���������)�#*���� )*+��*�)*�����+���)��+�*
���� ��*�� �*�+���*� ��*��)�*�++*)�)*+��*��� )*��������*
years ago. So they are collaborating with PointCross in Foster
$��*$+��� �+�*�*� �+�*+*�++*��+�� �*�+*+����)*%�����*
searching of data from the past 25 years of Roche studies,
including those outsourced to contract research organizations.
Those data, along with information about thousands of com-
pounds, will be mined to use the existing knowledge to develop
new drugs.
To generate large volumes of data, though, a biologist does
not need the infrastructure of a large pharmaceutical company.
�� *��+�����*���)�� *��*)�����+��)*��*��*&��*'� )��+�*
Genome Machine (PGM) System from Life Technologies in
Carlsbad, California (now a part of �������� �����������).
This next generation device can sequence up to two gigabases
in less than eight hours, and this is marketed as a ìpersonal
genome machineî that can go on a scientistís benchtop. Life
Technologiesí larger Ion Proton machine pumps out up to 10
gigabases in four hours or less.
In general, for academic and industrial life scientists, next
generation sequencing supplies a bonanza and a bottleneck.
As Crandall explains, ìWe cannot effectively study this volume
of genomes until our computational software scales up to these
big data needs.î So his team is working with W. Evan John-
son, an assistant professor of medicine at Boston University
School of Medicine, to develop software, PathoScope, that
can handle the data from todayís next generation sequencing
(NGS) platforms, which turn information on gigabases of DNA
into gigabytes of computer dataóthe exact ratio tends to be
about linear, depending on the NGS platform being used. This
software compares DNA samples to reference genomes in an
effort to identify a pathogen. Crandall says, ìOur data sets can
Big Biological Impacts from Big Data
Digital Lab ManagementóJuly 25 MetabolomicsóSeptember 19 GenomicsóOctober 3
PH
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In the life sciences, data can come in many forms, including
information about genomic sequences, molecular pathways,
and different populations of people. Those data create a
potential bonanza, if scientists can overcome one stumbling
block: how to handle the complexity of information. Tools
and techniques for analyzing big data promise to mold mas-
sive mounds of information into a better understanding of
the basic biological mechanisms and how the results can be
applied in, for example, health care. By Mike May
Upcoming Features
1299SCIENCE sciencemag.org/products
LIFE SCIENCE TECHNOLOGIES
BIG DATA
Produced by the Science/AAAS Custom Publishing Office
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samples with downstream
analyses generat ing
100ís of gigabytes of
data per sample.î
continued>
1300 sciencemag.org/products SCIENCE
LIFE SCIENCE TECHNOLOGIES
BIG DATA
Produced by the Science/AAAS Custom Publishing Office
Complexity from
Combinations
To the three vís of big data,
Stephen Cleaver, execu-
tive director of informatics
systems at the Novartis
Institutes for BioMedi-
cal Research (NIBR) in
Cambridge, Massachusetts
adds complexity. He says
that scientists in the phar-
maceutical industry ana-
lyze the data by ìpatients
individually and then as a
group, and then we inte-
grate everything we have.î
That gets complex.
In health care, the com-
plexity of big data analysis
also arises from combining
different types of informa-
tion, such as data from
genomics, proteomics,
cellular signaling, clinical research, and even environmental
studies. The results could reveal entirely new approaches to
treating diseases. But Iya Khalil, cofounder of GNS Healthcare
in Cambridge, Massachusetts, asks: ìHow do you make sense
of those data and get insights from those data that will advance
our understanding of the disease mechanism?î For Khalil and
her teammates, the answer comes from machine learning,
mathematics, computational algorithms, and supercomputersó
all combined to explore the underlying pathways of disease and
to follow a patientís likely response to a particular treatment.
At GNS Healthcare, such big-data analysis depends on a
computational platform called REFS, which stands for reverse
engineering and forward simulation. In short, the software ana-
lyzes data to construct possible molecular networks underlying
&'()����'�'��(�&(����&��('���'��(�')&��&��'����'��'�(�('��&�'
�� ��&����'���(����&������'��)&��'&')&�����&'���)����'�����'
have upon the pathwayóthe forward aspect of the process.
In addition to health care, REFS can be applied to basic
biology. For example, Khalil and her colleagues have used this
technology to make a molecular model of part of the cell repli-
cation cycle.
For Khalil and other scientists, the key is using big data in
ways that move science forward. At NIBR, for instance, Cleaver
and his colleagues want to make sure that the data is informa-
����'�'(�'&��' ����(��'����('��&�'��'��'&�&����'�&�&�������'
������(�'���'��'��(�'(����(�'���'����'(�������'�'��)����(�(��'��'
says. That way, todayís big data will change tomorrowís biology
and medicine.
track the characteristic
changes cells undergo
during development,
measure the impact that
environmental factors have
on an organism, or that
quantify the cellular effects
a medication may have on
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Structured data, such
as tables of numbers, do
not reveal everything that
is known about a medica-
tion or biological process.
Much of what we know
about living organisms ex-
ists in unstructured formats,
like journal article text. As
Johnson of Merck says,
ìThere are thousands of
ways to describe biological
processes,î and it is dif-
�'����'��'���&��'�&�&' ��'
the literature.
At IBMís Almaden Research Center in San Jose, California,
analytics expert and research staff member Ying Chen and her
colleagues have worked for years on creating technologies for
mining text, which they now use for their ìaccelerated drug
discovery solution.î Their platform aggregates patents, scien-
���'�'����&����'�&(��'�����(��'&��'�������'���������'�(���'&('
how chemicals and molecules interact), more than 16 million
unique chemical structures, and information about nearly 7,000
diseases. Using this system, researchers can search for com-
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Other companies also hope to mine existing resources to
learn more about the biology of diseases and how to treat
them. NuMedii, a big-data company in Silicon Valley, and
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drugs, known as drug repurposing. ìUsing genomic databases,
integrated knowledge sources, and bioinformatic approaches,
we can quickly discover novel uses for drugs,î says Craig
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safety data for the drugs in their original use to get to clinical
trials faster and cheaper.î NuMedii is contributing databases
and analytics to the project, while Thomas Reuters is supplying
in-depth knowledge on diseases and drugs.
One such project, Webb says, has researchers compiling
gene expression data from more than 2,500 ovarian tumor
samples and using several computer algorithms to predict
whether any existing drugs could potentially treat ovarian
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allows us to cast a wide net initially to identify leads, while ëbig
knowledgeí allows us to quickly select viable compounds to
test,î Webb says. DOI: 10.1126/science.opms.p1400086
ACD/Labswww.acdlabs.com
BioDatomicswww.biodatomics.com
Boston University School of Medicinewww.bumc.bu.edu/busm
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George Washington Universitywww.gwu.edu
GNS Healthcarewww.gnshealthcare.com
IBMwww.ibm.com
Life Technologieswww.lifetechnologies.com
Merckwww.merck.com
Novartis Institutes for BioMedical Researchwww.nibr.com
NuMediiwww.numedii.com
Rochewww.roche.com/index.htm
Rutgers, The State University of New Jerseywww.rutgers.edu
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University of Oxfordwww.ox.ac.uk
Wellcome Trust Centre for Human Geneticswww.well.ox.ac.uk
Mike May is a publishing consultant for science and technology.
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