Biology Reference
In-Depth Information
Summarizing data in matrix form provided a visual solution to a data
analysis problem. In 1970, Needleman and Wunsch developed a more
sophisticated matrix-based method based on dynamic programming
techniques.
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This ubiquitous algorithm is almost always explained, and
understood, visually: a way of fi nding an optimum path through a grid
and then “tracing back” to fi nd the best alignment of the sequences.
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In
pictorial terms, the algorithm lines up sequences on a grid or matrix—
much like the dot matrix method—and then scores various possible
matches between nucleotides; the fi nal alignment is found by drawing
a line through the boxes with the highest scores.
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Dynamic program-
ming is not just a way of picturing a protein sequence, but also of ana-
lyzing its relationship to another sequence by seeing the alignment as
a path through a two-dimensional space. These matrix methods were
visual solutions to data- and calculation-intensive problems in early
bioinformatics.
The late 1970s and early 1980s saw the development of the personal
computer. Rather than having to log into central computing facilities,
biologists could now run and program computers in their own labora-
tories. Computers became more powerful and more oriented toward
graphical rather than textual display of information. The Apple Mac-
intosh (fi rst available in 1984), with its graphical user interface, was
especially important in this respect: biologists became more accustomed
to being able to use spreadsheet programs to rapidly create charts and
other images to quickly inspect and analyze their data. Moreover, as
more and more biologists connected to various telephonic and elec-
tronic networks, the potential for sharing and gathering large amounts
of biological data grew rapidly. Visualization and exchange of data be-
came understood as the primary uses for computers in biology. A report
produced by the National Academy of Sciences in 1989 noted on the
fi rst page of the executive summary that “analytic capabilities have im-
proved signifi cantly, along with the capacity to present results as visual
images.”
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A report to the National Science Foundation, published in
the journal
Computer Graphics
in 1987, saw the overwhelming growth
of data as the immediate problem. The solution was visualization: “Sci-
entists need an alternative to numbers. A technical reality today and a
cognitive imperative tomorrow are the use of images. The ability of sci-
entists to visualize complex computations and simulations is absolutely
essential to ensure the integrity of analyses, to provoke insights, and
to communicate those insights with others.”
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Some went so far as to
hail a “second computer revolution,” in which “the ultimate impact of
visual computing will eventually match or exceed the tremendous soci-
