IEEE Spectrum July, 2013 - 33

IdentIfyIng mutatIons
Using the MapReduce software framework, the raw data from a sequencing machine is divided up and distributed
among many computers. In the "map" step, these computers try to match strings of nucleotides to locations in the
reference genome. Then they compare results in the "reduce" step to either confirm that a string perfectly corresponds
to the reference genome or to identify a mismatch-a mutation.

moving the code to the data rather than having to access data in a
comparatively slow file server. This structure also brings a reliability bonus, even on off-the-shelf servers and disks. Google created
MapReduce to run in data centers packed with cheap commodity computers, some of which were expected to fail every day, so
fault tolerance was built into the system. When a data set is loaded
into the program, it's split up into manageable chunks, and each
chunk is replicated and sent to several computer nodes. If one fails,
the others go on. This model also works well in a flexible setting
such as the Amazon Elastic Compute Cloud, where nodes can be
provisioned for an application as needed, on the fly, and leased
on a per-hour basis.
illustration by

L-Dopa

We're still a long way from having anything as powerful as a Web
search engine for sequencing data,
but our research groups are trying to
exploit what we already know about
cloud computing and text indexing to
make vast sequencing data archives
more usable. Right now, agencies
like the National Institutes of Health
maintain public archives containing
petabytes of genetic data. But without
easy search methods, such databases
are significantly underused, and all
that valuable data is essentially dead.
We need to develop tools that make
each archive a useful living entity the
way that Google makes the Web a useful living entity. If we can make these
archives more searchable, we will empower researchers to pose scientific
questions over much larger collections of data, enabling greater insights.

This year, genomics researchers
may reach a remarkable milestone:
the US $1000 genome. Experts have
long said that when the cost of sequencing a human genome falls to that
level, the technology can be used routinely in biological research and medical care. The high-capacity Illumina
systems are nearing this price point,
as is the Ion Proton machine from San
Diego-based Life Technologies.
Such sequencing capacity is already enabling projects that can reinvent major sectors of technology,
science, and medicine. For example, the U.S. Department of Energy
recently launched KBase, a knowledge base for biofuel research that
integrates hundreds of terabytes of
genomic and other biological data
inside its own compute cloud. KBase will use state-of-the-art machine learning and data-mining techniques to build predictive
models of how genome variations influence the growth of plants
and microbes in different environments. Researchers can then
select which plants and microbes should be bred or genetically engineered to become more robust, or to produce more usable oils.
This scenario is just a hint of what is to come if we can figure
out how to channel the data deluge in genomics. As sequencing
machines spew out floods of As, Ts, Cs, and Gs, software and hardware will determine how much we all benefit. n
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Table of Contents for the Digital Edition of IEEE Spectrum July, 2013