Combating the Life Science
Bioinformatics helps with big data problems.
Big data has become a growing issue in science, as these data sets are so large and complex that traditional data
processing applications are inadequate. This
is especially true for the life science industry,
where the growing size of data hasn’t been
met with tools for analyzing and interpreting
this data at the same rate, leading to what
many call a “data avalanche.”
Life science researchers are seeing more
next-generation sequencing data generation,
more samples and deeper sequencing,
and this data is increasing in complexity
as researchers move from targeted panels
to whole-exome and whole-genome
sequencing. However, the tools appearing in
the market often fail to incorporate system-
level interpretation, leading to further issues.
In enters bioinformatics, an interdisciplinary
field—including computer science, statistics,
mathematics and engineering—which develops
methods and software tools for understanding
As a result, to address the growing size
of data, there’s an expansion of tools to take
advantage of Cloud computing and storage.
Many vendors are developing their own
Cloud-enabled software platforms capable
of hosting a variety of analysis applications.
And while life science researchers want to
leverage the Cloud, they only want to do
so for the additional functionality it brings,
such as access to large data sets, common
annotation sources, data sharing and scalable
computing—they don’t want to overcome the
hassle of uploading unless there’s a real benefit.
The other downside to open source
solutions, like Cloud computing, is “tool
overload”. “Tool overload occurs as open
source tools rapidly evolve and create
downstream problems with version control,
validation, security, governance, reporting
and data reproducibility,” says Narges Bani
Asadi, PhD, Founder and CEO for Bina
However, with the
onset of bioinformatics,
multi-omic data sets are
increasingly common and tools are being
developed to integrate this data, “sometimes
across extremely different file types and
software applications,” says Antoni Wandycz,
Director, Bioinformatics Solutions, Software
& Informatics Div., Agilent Technologies.
There has also been an increase in the
use of tools for modeling and simulation.
As predictive analytics is a standard in
research, it’s finding its way into regulatory
submissions. “This shift toward more in
silico experimentation has provided several
benefits to the life science industry,” says
Tim Moran, Director of Life Science
Research Marketing, BIOVIA.
The benefits of bioinformatics
In life science, whether for basic research
or applications—bioinformatics offers major
advantages, including measuring hidden
values and seeing invisible patterns—at a
low cost. However, the truth still remains
that the best technology on the market
can’t measure everything of interest to this
field. “An example of this is the fluctuating
concentration of any given metabolite in a
live cell,” says Wandycz.
However, good bioinformatics models
allow researchers to interpret those
values based on what they can measure.
“Furthermore, a researcher may not see the
pattern in a large, complex model of data
sets, whereas bioinformatics algorithms
excel as such a task,” says Wandycz.
With bioinformatics tools on the market,
thousands, and sometimes millions, of
virtual experiments can be done faster and
cheaper than a single experiment at the
lab bench. Yet, bioinformatics technology
doesn’t replace reality, “and therefore doesn’t
complemented in turn,” says Wandycz.
In the past, bioinformatics has been a
barrier to translational research, and a known
bottleneck between laboratory scientists
who do sequencing and clinical researchers
who do annotation and interpretation.
“Empowering bioinformatics with a Genomic
Management Solution (GMS) to more
efficiently process and manage large volumes
of genomic data, removes this bottleneck,”
says Bani Asadi. “With GMS we also enable
researchers with better access to the data
through collaborative user interfaces, to more
effectively leverage the data across research
By providing collaborative access to
genomic data, clinical and translational
research can progress towards a deeper
understanding of human disease,
mechanisms, treatment and diagnosis.
Bioinformatics tools also enable modeling
and simulation that can reduce the number
of experiments that need to be performed.
This meets the repeatability standards
of life science experiments. “These same
capabilities allow researchers to identify
undesirable pharmacological and/or
biological development issues early in
discovery, before progress to development—
essentially failing poor candidates faster
and providing higher-quality candidates
downstream,” says Moran.
The changing landscape of life
The changing landscape of research today
is forcing the bioinformatics community
to seek a new level of data sharing and
collaboration only made possible with new
platforms. Data volumes are growing due to
larger population studies, deeper sequencing
coverage to detect variants with greater
What if we can use 3-D computational modeling to explore individual hearts
and guide the creation of personalized therapies? Image: BIOVIA