Previous versions of this book recognized this, to some extent, with an Online Resource Centre supplementing the text. With this 4th edition, the online material assumes a full partnership. To learn bioinformatics means to understand basic concepts and principles, and to develop a set of skills. An icon in the text indicates the appearance in the Online Resource Centre of material related to the current discussion. The data of bioinformatics are accessible on the web. Programs to analyze them are available on the web.
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It is my opinion that you can end your search here for an entry point to the modern field of bioinformatics. As best I can tell, the target audience is undergraduate biology students who have basic familiarity with computer programming. Virtually no mathematical sophistication is required -- there is not a proof in sight, and complex mathematical topics like Hidden Markov Models and Monte Carlo algorithms are explained in an unintimidating, intuitive manner.
Computer science knowledge such as graph theory, dynamic programming, and computational complexity are introduced minimally and only when they are needed. Biological concepts are also sufficiently explained, except for perhaps a term here and there, and as a computer scientist I found the book a cinch to read. Therefore, the book is self-contained and is excellent for self-study.
The first half of the book Chapter are a high-level overview, and a practical summary of existing databases of genetic and proteomic data. This serves an excellent guide for those who A need to become familiar with the websites that "everybody" in the field knows about, or B are eager to get their paws on sequence data and start playing!
Chapter 3 even gives a very brief introduction to data mining and natural language processing for extracting information from the literature. Chapters are the meat of the matter.
Sequence alignment chapter 5 is "THE basic tool of bioinformatics" p. Dotplots, single and multiple sequence alignment, profiling, BLAST, PSI-BLAST, Hidden Markov Models, and phylogenetic trees are all discussed and situated so that the read knows the advantages and disadvantages of each tool, and their limitations used to motivated future chapters on protein structure. Chapter 6 covers protein folding, structure prediction, classification, and function prediction, as well as applications to drug discovery.
Chapter 7 ends the book with a more theoretical, big-picture discussion of systems biology, information theory, and regulatory networks. Overall, I think this book is great. The book is full of practical tips like "Visual examination of multiple sequence alignment tables is one of the most profitable activities that a molecular biologist can undertake away from the lab bench.
He also is careful to emphasize difficulties in, for instance, inferring homology from sequence similarity, and in making assumptions about mutation rate. That said, as a computer scientist with a math degree under my belt, I did miss the presence of rigorous mathematics.
Introduction to Bioinformatics
He received his doctoral degree from Princeton University in Lesk and Chothia also studied the conformations of antigen-binding sites of immunoglobulins. The discovery and analysis of these mechanisms was the key to understanding conformation changes in serine protease inhibitors, also known as serpins, mutations of which are an important cause of several diseases, including emphysema and certain types of inherited mental illness. Lesk used a systematic analysis of protein-folding patterns to develop a mathematical representation that aids in the recognition and classification of these patterns. He also wrote the first computer program to generate schematic diagrams of proteins using molecular graphics, and he developed many algorithms now used by other researchers to analyze the structures of proteins.
Introduction to Bioinformatics (PDF)
Arthur M. Lesk