Many recent bioinformatics books cater to a protocol-centric pragmatic approach to bioinformatics. They focus on parameter settings, application-specific features, and other details without revealing the computational ideas behind the algorithms. This trend often follows the tradition of biology books to present material as a collection of facts and discoveries. In contrast, introductory books in algorithms and mathematics usually focus on ideas rather than on the details of computational recipes. In principle, one can imagine a calculus book teaching physicists and engineers how to take integrals without any attempt to explain what is integral. Although such a book is not that difficult to write, physicists and engineers somehow escaped this curse, probably because they understand that the recipe-based approach to science is doomed to fail. Biologists are less lucky and many biology departments now offer recipe-based bioinformatics courses without sending their students first to Algorithms 101 and Statistics 101. Some of students who take these classes get excited about bioinformatics and try to pursue a research career in bioinformatics. Many of them do not understand that, with a few exceptions, such courses prepare bioinformatics technicians rather than bioinformatics scientists.

Bioinformatics is often defined as “applications of computers in biology”. In recent decades biology has raised fascinating mathematical problems and reducing bioinformatics to “applications of computers in biology” diminishes the rich intellectual content of bioinformatics. Bioinformatics has become a part of modern biology and often dictates new fashions, enables new approaches, and drives further biological developments. Simply using bioinformatics as a toolkit without understanding of the main computational ideas is not very different than using a PCR kit without knowing how PCR works.

So much true. Simply using bioinformatics tools or protocols as part of a collection of cookbook style recipes without understanding how they work does not make anyone bioinformatics scientist. As Pevzner writes

Users that do not know how BLAST works might misapply the algorithm or misinterpret the results it returns . Biologists sometimes use bioinformatics tools simply as computational protocols in quite the same way that an uninformed mathematician might use experimental protocols without any background in biochemistry or molecular biology. In either case, important observations might be missed or incorrect conclusions drawn. Besides, intellectually interesting work can quickly become mere drudgery if one does not really understand it.

I could not agree more, Bioinformatics is perfect example how intellectually interesting work can quickly become mere drudgery if one does not really understand it.