Compared to other primers such as "Developing Bioinformatics Computer Skills", this book contains less unnecessasary figures (e.g., central dogma, etc.), covers wider range of topics, tries to be less verbose.

A drawback is that there is little description at an algorithmic level (e.g., dynamic programming). However, the book does a pretty good job in conveying the main ideas about what such algorithms do and why they are needed. I like this book's concise and accurate presentation style much better than lengthy and confusing style found in many other books (e.g., Bioinformatics - David Mount). Another drawback is that font is small.

Overall, this book is not bad. I think this book's preface tells you what you can expect from this book, so below I excerpted a paragraph.

"We will tell you how to do things, but this is not a software manual for commonly used packages. They have their own manuals that are (mostly) much better than anything we could provide. Many of the methods we describe rely on quite complex mathematical, statistical or computational techniques. Often we choose not to describe these at all, but where we do we have aimed for a simple conceptual understanding."