Bioinformatics Algorithms: An Active Learning Approach

by Phillip Compeau, Pavel Pevzner

Bioinformatics Algorithms: An Active Learning Approach is one of the first textbooks to emerge from the recent Massive Open Online Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinforma...), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of biology and computer science students alike.

Each chapter begins with a central biological question, such as "Are There Fragile Regions in the Human Genome?" or "Which DNA Patterns Play the Role of Molecular Clocks?" and then steadily develops the algorithmic sophistication required to answer this question. Hundreds of exercises are incorporated directly into the text as soon as they are needed; readers can test their knowledge through automated coding challenges on Rosalind (http://rosalind.info), an online platform for learning bioinformatics.

The textbook website (http://bioinformaticsalgorithms.org) directs readers toward additional educational materials, including video lectures and PowerPoint slides.

Genres: Biology, Computer Science, Science, Textbooks, Reference, Algorithms, Nonfiction

392 pages, Paperback

First published June 1, 2014

About the author

Phillip Compeau is an Associate Teaching Professor and the Assistant Department Head for Education in the Computational Biology Department in Carnegie Mellon University's School of Computer Science. He directs the undergraduate program in computational biology, co-directs the Precollege Program in Computational Biology, and serves as Assistant Director of the Master's in Computational Biology program (http://www.cmu.edu/ms-compbio/). He is the co-author of Bioinformatics Algorithms: An Active Learning Approach (http://bioinformaticsalgorithms.org).

Phillip co-created the first massive open online course (MOOC) in bioinformatics, which has grown into the Bioinformatics Specialization on Coursera (https://www.coursera.org/specializati...). He also co-founded Rosalind (http://rosalind.info), an online platform for learning bioinformatics that has reached tens of thousands of learners around the world.

Phillip is also the founder of Programming for Lovers (http://compeau.cbd.cmu.edu/programmin...), a free online course in introductory programming motivated by fun scientific applications.

Reviews

[Naren · Rating 5 out of 5]

I read first chapter of this book.

Excellent introduction to, how to derive replication origin ie OriC. The authors explain complex topics using simple, easy to understand, language. They give ample examples, so that a reader is able to follow exact concepts. They build your knowledge, step by step. They also explain statistical terms with proper examples.

I would definitely recommend this book to anybody, who wants to know about what's happening in the world of Genome Sequencing.

[Radu Vunvulea · Rating 5 out of 5]

Great book for an introduction to Bioinformatics, from algorithms point of view. It is pretty complex and hard to read. I recommend to refresh the graph theory before jumping to this book. But a good and solid starting point.

[R Phantom · Rating 5 out of 5]

This book is really really good for introducing bioinformatics to a novice reader. As a student from CS background who had seen quiet a few algorithms of graphs, Dynamic Programming, etc being taught in university course without any application, this book will take a reader through all those algorithms (and their tougher real life variants) while introducing you about genomes. Must read!

[Miguel · Rating 5 out of 5]

Does exactly what the book sets out to do. Teaches all the basics. As someone who's been looking for a good introduction to bioinformatic algorithms this is perfect. Also Pavel and Phillips are masters in this domain and it really shows if you take the coursera course. Highly recommended

[Michiel · Rating 5 out of 5]

Clear and accessible introduction to bioinformatics algorithms. I especially enjoyed the effort to frame every chapter with a concrete biological problem which bioinformatics can solve.

[Irene · Rating 3 out of 5]

Está bueno, siento que está escrito más para la gente que viene de Informática que para biólogos, por lo que me costó un poco seguirlo. Da por obvias cosas que para mí no lo eran. Hace falta una base importante de programación y yo recién empecé a estudiar Python hace 3-4 meses, quizás lo relea más adelante ... Igual me copó mucho porque da respuestas a preguntas que ni se me habían ocurrido cuando cursé Molecular, Genética, Evolución, etc... Es como una forma distinta de ver las cosas, me parece re interesante

[Esben · Rating 5 out of 5]

Probably one of the best textbooks I've had my hands on for voluntary reading. It maintains a beautiful balance between mathematics, explanations, visualizations, and interesting bibliographic details about how genomics arose. Getting this unique and in-depth insight into bioinformatics and the characteristics of genomes, chromosomes, bacteria, and computational (graph) methods for analyzing them was highly enjoyable. 10/10.

[Samuel · Rating 2 out of 5]

While this might be a good introduction for someone just starting out in the field, I found it was too slow going for someone with some basics already and the analogies bored me out as they felt irrelevant and did not give me a feel for what applying the knowledge to real questions would look (or feel) like. I could not get me to read more than a few sections.

[Steve Haigh · Rating 5 out of 5]

Great book, just wish it had an index!

[Rachel B · Rating 4 out of 5]

Got a lil lost sometimes but overall interesting and a good intro book

[Rahulalishetty]

great start