We live in astonishing times, not least when we consider the recent rate of progress in science. We are experiencing a revolution, underpinned and empowered by Moore’

Galbraith, David William

Frontiers in Genetics

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David William Galbraith

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Frontiers in Genomic Assay Technologies: The Grand Challenges in Enabling Data-Intensive Biological Research

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www.frontiersin.org June 2011 | Volume 2 | Article 26 | 1
Specialty Grand challenGe article
published: 08 June 2011
doi: 10.3389/fgene.2011.00026
Frontiers in genomic assay technologies: the grand challenges 
in enabling data-intensive biological research
David William Galbraith*
BIO5 Institute, University of Arizona, Tucson, AZ, USA
*Correspondence: galbraith@arizona.edu
IntroductIon
We live in astonishing times, not least when 
we consider the recent rate of progress in 
science. We are experiencing a revolution, 
underpinned and empowered by Moore’s 
famous law and the computational power 
enabled by that law (Mollick, 2006), that 
has largely eliminated the traditional sci-
entific bottle-neck associated with data 
acquisition. Biologists now find themselves 
a situation familiar to any of us that has, 
over the last decade or so, replaced their 
conventional film camera with its digital 
counterpart. The biologist now has the 
ability to take technological “snapshots” of 
almost any attribute of a living organism, 
whether it be DNA sequence, protein iden-
tity, 3-, 4-, and 5-dimensional distributions 
of macromolecules, composition of small 
molecules, and ions, identity of mutations, 
the mapping of phenotypes to genotypes, 
and so-on, and this has provided an unprec-
edented amount of data for analysis. This 
situation, which extends across all fields of 
experimental science, has been canonized 
as the emergence of a “Fourth Paradigm” 
in scientific research, termed Data-Intensive 
Scientific Discovery, the first, second, and 
third paradigms representing conven-
tional empirical observation, abstraction 
and theory, and computational simula-
tion, respectively (Gray, 2009). As you will 
see below, biologists now face much of the 
same grand challenges as experienced by the 
recreational photographer.
The purpose of this article is to sum-
marize grand challenges confronted at the 
frontier of genomic assay technology, and 
to provide some talking points as to the 
purpose of this new journal. Several grand 
challenges are relatively easy to define. At 
least three are directly associated with our 
recent and unprecedented ability to gener-
ate biological data in digital form. Given 
this ability, immediate challenges are: (1) 
How does one design the most  meaningful 
experiments to generate the most useful 
data? (2) How does one handle the resultant 
data stream? and (3) What is the best way 
to archive data, and to harvest and integrate 
this archived data?
A fourth grand challenge naturally arises 
from the process of technology innova-
tion, being associated with the identifi-
cation of new ways to analyze biological 
organisms and systems, and the develop-
ment of the associated technologies and 
experimental platforms.
A final grand challenge arises from the 
concept of stepping back and focusing on 
the source materials used for generating 
biological knowledge, the families, genera, 
and species, discovered and undiscovered, 
representing life on this planet, and asking 
the question as to whether we know all pos-
sible ways in which evolution can give rise 
to successful life-forms based on detailed 
understanding and study of only a very lim-
ited subset of these life-forms.
detaIls of the Grand challenGes
desIGnInG meanInGful experIments In 
the context of facIle data productIon
Changing the rate-limiting step on biological 
experiments, away from the process of data 
production, has unexpected consequences. 
From a practical viewpoint, although it pro-
vides a greatly expanded range of possible 
experiments, it also has the effect of decreas-
ing the focus both of the experiments and 
of the experimenter. At the same time, 
the commoditizing of experiments, and 
their ever-decreasing cost, runs the risk of 
decreased rigor in experimental design. This 
is particularly important from the point of 
view of statistics, since any experimental 
design should not only provide data that 
most directly test the questions of greatest 
interest, but also avoid confounding vari-
ables when it comes to the point of analysis. 
The context of the experiment can also be a 
factor: within the public sector, any dataset 
can be harvested to provide information for 
follow-up, which when published can then 
be defined as valuable. From this viewpoint, 
dealing with false-positives, such as those 
generated when insufficient power is associ-
ated with the experimental design, may not 
be a serious issue. In contrast, within the 
targeted research programs of the private 
sector, such as those aimed at the discovery 
of new drugs, false discovery is particularly 
problematic, since the downstream test-
ing, modification, and validation of lead 
chemical structures requires significant 
investments of both time and resources. 
The grand challenge of optimizing both 
the choice of experiments and the associ-
ated experimental design will remain per-
sistent, given that our technical abilities to 
perform these experiments, and therefore 
their scope, will continue to increase expo-
nentially, with corresponding decreases in 
the associated costs, at least for the immedi-
ate future, and most probably for a further 
extended period.
An additional problem is associated with 
the fact that most genomic assay technolo-
gies now produce datasets of extremely high 
dimensionality. This means that the data-
sets provide greater insight into the biologi-
cal system than can be accommodated in 
the summary descriptions (the associated 
metadata) of the experiments that are being 
performed. From this viewpoint, the con-
cept of replicating an experiment, central 
to the scientific method as implemented in 
biology, comes into question. If we cannot 
describe the source material and the experi-
mental treatment(s) with the same level of 
detail as the resultant datasets, we cannot 
be sure that we have identified all variables 
that might affect both the datasets and our 
understanding of their meaning (Galbraith, 
2006). Many examples of this problem are 
found in the historical record: for example, 
before 1998 or thereabouts, no attention 
was paid to the role of small RNA species 
in gene expression, since these were widely 
considered to simply represent break-
down products of the larger, more  relevant 
Frontiers in Genetics | Genomic Assay Technology  June 2011 | Volume 2 | Article 26 | 2
Galbraith Enabling data-intensive biological research
the evolving organism. It appears likely, 
therefore, that we will uncover physical and 
chemical mechanisms whose involvement 
in the activities of living systems was pre-
viously unsuspected. The associated grand 
challenge will involve the development 
of accurate technologies and platforms 
to detect and chart these mechanisms. A 
recent example includes observations that 
implicate the vibrational modes of mole-
cules, rather than simply their space-filling 
structures, as involved in biological detec-
tion and function (Franco et al., 2011). If 
this is true, the development of instrumen-
tation to readily measure these vibrational 
modes will become essential.
explorInG Global bIodIversIty
Implicit in the previous grand challenge is 
the assumption that our sampling of global 
biodiversity may have been insufficient to 
identify examples of organisms that repre-
sent all possible modes of biological evolu-
tion. Certainly, our funding mechanisms 
focus heavily on few species, and these spe-
cies have, in many cases, been subjected to 
stringent selection for “appropriate behav-
ior.” For example, of the ∼500,000 species 
of angiosperms (flowering plants), agri-
cultural crops represent a very small sub-
set (about 2,500 species) and, of these, 103 
supply over 90% of the calories for human 
consumption. These crops have, without 
exception, been engineered to be amena-
ble to crop improvement, requiring predict-
able behavior in crossing, in production of 
seeds, in cultivation under controlled con-
ditions, and so-on. The implication is that 
unusual mechanisms of organismal devel-
opment and reproduction would not sur-
vive this process of selection. The fact that 
descriptions do exist of organisms having 
unconventional genetic and biosynthetic 
mechanisms, with particularly good exam-
ples coming from marine biota (cf. diatoms 
and dinoflagellates), gives us confidence 
that additional, peculiar biodiversity exists 
and is yet to be described. Of course, this can 
be controversial, as indicated by the recent 
vigorous debate as to whether or not arsenic 
can replace phosphorus in living organisms 
(Wolfe-Simon et al., 2010). As a final note, 
urgency in pursuing this grand challenge is 
particularly emphasized by the increasing 
rate of anthropogenic change, since we risk 
losing species before they are characterized 
(Barnosky et al., 2011).
in N, are much less easy to implement, and 
in many cases impossible. Part of this grand 
challenge therefore calls for the design 
and implementation of new, more-rapid 
algorithms, which necessarily will provide 
approximate answers, but which will nev-
ertheless be very useful.
data archIvInG
An immediate question, given our tech-
nical inability to store all of our current 
data streams, is as to which data should 
be stored for future analysis. This question 
is complicated by the observation that we 
cannot know what methods of data analysis 
and what novel insights will emerge in the 
future that would guide the types of data 
analysis that should then be done on pre-
viously archived data. Further challenges 
in data storage concern maintenance of 
the data in digital archives in a state that is 
both readable and comprehensible; Curry 
(2011) provides a cautionary tale from high 
energy physics.
It will also be a grand challenge to deter-
mine which currently stored data should 
remain in storage and which discarded. 
Certainly, improvements in accuracy 
already render much stored data obsolete, 
sequence information in GenBank being a 
prime example. The cost of careful cura-
tion of the stored data may well exceed the 
cost of simply replacing it. This conundrum 
will affect all high throughput data streams, 
and will be a persistent problem to be con-
fronted, given the fact that data replacement 
will generally be associated with exponen-
tially decreasing costs.
new technoloGIes
We can confidently expect increases in 
accuracy, throughput, and scale, and 
decreases in cost, for existing platforms 
and technologies. A grand challenge will 
be to push these technologies to their tech-
nical limits. However, one the most intel-
lectually exciting aspects of the frontier is 
the development of new and transforma-
tive technologies for examining biological 
organisms, their genomes, and the ways 
that these genomes function. From a philo-
sophical viewpoint, evolution, acting over 
millennia, simply serves to codify the ways 
in which living organisms can survive and 
reproduce. This codification occurs within, 
and employs, all physical and chemical laws 
and mechanisms that are experienced by 
mRNA species. The discovery of the cru-
cial importance of small RNA molecules 
in multiple cellular regulatory processes 
dramatically shifted our understanding of 
the process of gene expression. In another 
example, increasing evidence is emerging of 
transgenerational inheritance of epigenetic 
states (see, for example, Ng et al., 2010). This 
observation, which tints Mendel’s laws with 
shades of ambiguity, implies that experi-
ments involving analysis of individuals can-
not provide a complete description without 
the availability of a history of their prov-
enance over generations, and it becomes 
unclear as to what limits (if any) should be 
applied to this retrospection, since it rap-
idly becomes cost-prohibitive and is usually 
impractical. Bioinformaticians increasingly 
worry that the imposition of prescriptive 
structures (such as standards, schemas, 
controlled vocabularies, and ontologies) 
simply defines a shared consensus of the 
world, which therefore cannot represent 
the frontier of research (Barga et al., 2011). 
Solving these issues represents a grand chal-
lenge that again will not be easy.
handlInG Increased data streams
In 2007, for the first time, global digital data 
production, about 260 EB, exceeded our 
ability to store this data (Gantz et al., 2008). 
The size of the digital universe estimated 
for that year is larger than the number of 
stars in the universe and, given conserva-
tive predictions of growth rate, in 15 years 
will surpass Avogadro’s number. Another 
context for viewing this problem involves 
the 2007 prediction that by 2011 (i.e., the 
present day), almost 50% of digital infor-
mation that we create can and will have 
no permanent home. The grand challenge 
here involves addressing the question as to 
how best to analyze data streams with the 
understanding that much will not be per-
manently stored.
In terms of analysis of the data streams, 
increases in global computation power have 
followed an exponential course (Hilbert and 
Lopez, 2011), although this may not be suf-
ficient for adequate analysis. Gray (2009) 
notes that data analysis typically can be clas-
sified either as a search for specific anoma-
lies, which can be readily implemented on 
grids of computer clusters, or as exercises in 
correlation analysis, to identify global pat-
terns underlying the data. The latter, which 
involves algorithms that are typically cubic 
www.frontiersin.org June 2011 | Volume 2 | Article 26 | 3
Galbraith Enabling data-intensive biological research
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D., Schlichting, W., and Toncheva, A. (2008). The 
diverse and exploding digital universe: an updated 
forecast of worldwide information growth through 
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diverse-exploding-digital-universe.pdf
Gray, J. (2009). “Jim Gray on eScience: a transformed 
scientific method,” in The Fourth Paradigm: Data-
Intensive Scientific Discovery, eds T. Hey, S. Tansley, 
and K. Tolle (Redmond, WA: Microsoft Research), 
xvii–xxxi.
Hilbert, M., and López, P. (2011). The world’s techno-
logical capacity to store, communicate, and compute 
information. Science 332, 60–65.
Mollick, E. (2006). Establishing Moore’s law. IEEE Ann. 
Hist. Comput. 28, 62–75.
Ng, S.-F., Lin, R. C. Y., Laybutt, D. R., Barres, R., Owens, 
J. A., and Morris, M. J. (2010). Chronic high-fat diet 
in fathers programs β-cell dysfunction in female rat 
offspring. Nature 467, 963–966.
Wolfe-Simon, F., Switzer Blum, J., Kulp, T. R., Gordon, 
G. W., Hoeft, S. E., Pett-Ridge, J., Stolz, J. F., Webb, S. 
M., Weber, P. K., Davies, P. C. W., Anbar, A. D., and 
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10.1126/science.1197258
Received: 04 May 2011; accepted: 20 May 2011; published 
online: 08 June 2011.
Citation: Galbraith DW (2011) Frontiers in genomic assay 
technologies: the grand challenges in enabling data-inten-
sive biological research. Front. Gene. 2:26. doi: 10.3389/
fgene.2011.00026
This article was submitted to Frontiers in Genomic Assay 
Technology, a specialty of Frontiers in Genetics.
Copyright © 2011 Galbraith. This is an open-access article 
subject to a non-exclusive license between the authors and 
Frontiers Media SA, which permits use, distribution and 
reproduction in other forums, provided the original authors 
and source are credited and other Frontiers conditions are 
complied with.
record, one wonders if the time is right for 
a critical reevaluation of the predominance 
of hypothesis-testing within biological 
research. In other words, it may now be time 
for biologists to think more like physicists. 
The irony of this statement has not escaped 
our notice!
In summary, therefore, the role to be 
played by frontiers in genomic assay tech-
nology will include showcasing of new 
methods, technologies and platforms for 
probing the functions of living organisms, 
the critical analysis of methods for data 
acquisition, analysis and archiving, the 
exploration of new and uncharted organ-
isms and life-forms, and an encouragement 
of healthy debate as to the philosophy of 
science underpinning advances in biologi-
cal research. We live in exciting times, and 
I look forward to being astonished by these 
advances.
references
Barga, R., Howe, B., Beck, D., Bowers, S., Dobyns, 
W., Haynes, W., Higdon, R., Howard, C., Roth, 
C., Stewart, E., Welch, D., and Kolker, E. (2011). 
Bioinformatics and data-intensive scientific discov-
ery in the beginning of the 21st century. OMICS 
15, 199–201.
Barnosky, A. D., Matzke, N., Tomiya, S., Wogan, G. O. U., 
Swartz, B., Quental, T. B., Marshall, C., McGuire, J. L., 
Lindsey, E. L., Maguire, K. C., Mersey, B., and Ferrer, 
E. A. (2011). Has the Earth’s sixth mass extinction 
already arrived? Nature 471, 51–57.
Curry, A. (2011). Rescue of old data offers lesson for 
particle physicists. Science 331, 694–695.
Franco, M. I., Turin, L., Mershin, A., and Skoulakis, E. M. 
C. (2011). Molecular vibration-sensing component in 
Drosophila melanogaster olfaction. Proc. Natl. Acad. 
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conclusIon
A few words in conclusion are worth on 
the subject of the philosophy of science. A 
colleague recently commented to me about 
differences between the way in which sci-
entific discovery is pursued in the physi-
cal and biological sciences. In the physical 
sciences, astrophysics and particle physics 
being good examples, the accepted scientific 
approach involves the design and construc-
tion of instrumentation, to the highest pos-
sible design tolerances and measurement 
accuracies, to provide datasets that are as 
complete as can be achieved concerning 
the objects of interest; these objects range 
from galaxies at the limits of detection, to 
more hypothetical objects, such as the Higgs 
boson, other novel elementary particles, 
dark matter, and dark energy. The result-
ant datasets are then analyzed in a more-
or-less hypothesis neutral fashion, in order 
to detect the increasingly subtle indications 
of interactions, representing underlying 
natural order from which speculation as to 
general mechanisms, rules, and laws can be 
based. If these speculations have predictive 
value, which can subsequently be tested, so 
much the better.
In contrast, particularly in the period 
covering the latter part of the twentieth 
century, biologists have concentrated on 
hypothesis-driven scientific testing. Given 
that all hypotheses proposed for  biological 
systems likely are incomplete, since we 
know little about the systems under study, 
and furthermore most probably will turn 
out to be incorrect, based on the historical 


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