Biology Reference
In-Depth Information
quences have been reconceived as objects “out of sequence,” or “out of
order,” making way for techniques of randomness and stochasticity to
be applied to analyzing genes and genomes.
Understanding the connections between sequences, statistics, and
computers can help us make sense of recent pronouncements about bi-
ology, such as this one from Craig Venter in 2012:
We're at the point where we don't need one genome or just a
few genomes to interpret your genome. We need tens of thou-
sands of genomes as a starting point, coupled with everything
we can know about their physiology. It's only when we do that
giant computer search, putting all that DNA together, that we
will be able to make sense in a meaningful statistical manner of
what your DNA is telling you. We're just at the start of trying
to do that.
1
With enough sequence data and powerful enough computers, many
biologists believe, it will be possible to answer almost any question in
biology, including and especially big questions about whole organisms,
bodies, and diseases. What “your DNA is telling you” depends on its
data-based relationships to thousands or millions of other pieces of se-
quence. So the turn toward sequence is not just reductionism writ large,
or a scaling up and speeding up. It also signifi es a faith in particular
tools (computers), particular approaches (statistics), particular objects
(data), and particular types of questions (large scale) to move biological
knowledge forward.
More specifi cally, “what your DNA is telling you” doesn't just de-
pend on your genome or the thousands of other genomes out there.
Rather, it depends on how they can be related to one another; these
vast amounts of data can be made sense of only by using computers
to search for specifi c patterns and relationships. These relationships, in
turn, depend on the structures into which the data are fi tted: the ontolo-
gies, the databases, the fi le structures, and the visual representations into
which they are poured. It is these structures—written into hardware and
software—that order and reorder sequence data such that biologists can
i nd meaning in them. It is the rigidity of these forms that this topic has
sought to describe and understand. Databases, ontologies, and visual
representations tie informatic genomes to the specifi c practices of com-
puters, computational biology, and bioinformatics.
Our understanding of our own genomes also increasingly depends
on vast amounts of
other kinds
of data that are being rapidly accu-
