Foreword
If you can't do bioinformatics, you
can't do biology, and Perl is the
biologist's favorite language for doing
bioinformatics. The genomics revolution has so altered the landscape
of biology that almost anyone who works at the bench now spends much
of his time at the computer as well, browsing through the large
online databases of genes, proteins, interactions and published
papers. For example, the availability of an (almost) complete catalog
of all the genes in human has fundamentally changed how anyone
involved in genetic research works. Traditionally, a biologist would
spend days thinking out the strategy for identifying a gene and
months working in the lab cloning and screening to get his hands on
it. Now he spends days thinking out the appropriate strategy for
mining the gene from a genome database, seconds executing the query,
and another few minutes ordering the appropriate clone from the
resource center. The availability of genomes from many species and
phyla makes it possible to apply comparative genomics techniques to
the problems of identifying functionally significant portions of
proteins or finding the genes responsible for a
species' or strains distinguishing traits.Parallel revolutions are occurring in neurobiology, in which new
imaging techniques allow functional changes in the nervous systems of
higher organisms to be observed in situ; in clinical research, where
the computer database is rapidly replacing the paper chart; and even
in botany, where herbaria are being digitized and cataloged for
online access.Biology is undergoing a sea change, evolving into an
information-driven science in which the acquisition of large-scale
data sets followed by pattern recognition and data mining plays just
as prominent a role as traditional hypothesis testing. The two
approaches are complementary: the patterns discovered in large-scale
data sets suggest hypotheses to test, while hypotheses can be tested
directly on the data sets stored in online databases.To take advantage of the new biology, biologists must be as
comfortable with the computer as they now are with thermocyclers and
electrophoresis units. Web-based access to biological databases and
the various collections of prepackaged data analysis tools are
wonderful, but often they are not quite enough. To really make the
most of the information revolution in biology, biologists must be
able to manage and analyze large amounts of data obtained from many
different sources. This means writing software. The ability to create
a Perl script to automate information management is a great
advantage: whether the task is as simple as checking a remote web
page for updates or as complex as knitting together a large number of
third-party software packages into an analytic pipeline.In his first bioinformatics book, Beginning Perl for
Bioinformatics, Jim introduced the fundamentals of
programming in the language most widely used in the field. This book
goes the next step, showing how Perl can be used to create large
software projects that are scalable and reusable. If you are
programming in Perl now and have experienced that wave of panic when
you go back to some code you wrote six months ago and
can't understand how the code works, then you know
why you need this book. If you are an accomplished programmer who has
heard about bioinformatics and wants to learn more, this book is also
for you. Finally, if you are a biologist who wants to ride the crest
of the information wave rather than being washed underneath it, then
buy both this book along with Beginning Perl for
Bioinformatics. I promise you won't be
disappointed.Lincoln SteinCold Spring Harbor, NYSeptember 2003 |
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