Download PDF Bioinformatics with Python Cookbook
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Bioinformatics with Python Cookbook
Download PDF Bioinformatics with Python Cookbook
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About the Author
Tiago Antao Tiago Antao is a bioinformatician. He is currently studying the genomics of the mosquito Anopheles gambiae, the main vector of malaria. Tiago was originally a computer scientist who crossed over to computational biology with an MSc in bioinformatics from the Faculty of Sciences of the University of Porto, Portugal. He holds a PhD in the spread of drug resistant malaria from the Liverpool School of Tropical Medicine, UK. Tiago is one of the coauthors of Biopython―a major bioinformatics package―written on Python. He has also developed Lositan, a Jython-based selection detection workbench. In his postdoctoral career, he has worked with human datasets at the University of Cambridge, UK, and with the mosquito whole genome sequence data at the University of Oxford, UK. He is currently working as a Sir Henry Wellcome fellow at the Liverpool School of Tropical Medicine.
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Product details
Paperback: 310 pages
Publisher: Packt Publishing - ebooks Account (June 25, 2015)
Language: English
ISBN-10: 1782175113
ISBN-13: 978-1782175117
Product Dimensions:
7.5 x 0.7 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
4.4 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#680,880 in Books (See Top 100 in Books)
This book is a great addition to the library of texts to learn advanced Python applications in biomedical sciences. One big drawback of the Python environment in this field is the paucity of textbooks beyond the most basic level. In this sense, the book by Antao is a welcome contribution.Two things you need to know right away about the book: 1) This is not a bioinformatics book, as it covers only the Python implementation of the algorithms but not their theoretical background. Furthermore, the techniques covered are intermediate and advanced bioinformatics topics (e.g. next-generation sequencing, population genetics, protein structure visualization). 2) By the same token, is not an introductory Python book, knowledge about the language and its most common scientific packages (NumPy, SciPy, and Matplotlib) is assumed (on the other hand, Chapter 1 - covering the installation of the software - is a little wasted because of this).The materials in each chapter are presented clearly and in a concise manner. Is very easy to follow what Python packages are being used and the differences in syntax between Python 2 and 3. The selected examples are very relatable to the biomedical scientist, making it a nice change of pace from all the finance examples commonly encountered in most Python books. The code runs smoothly without any changes, and the explanations of the commands are good although a little short every now and then.In summary, this is a great book if you are looking to implement advanced bioinformatics techniques in Python. The writing style of the author makes it very user-friendly and if he decided to write another book covering how to implement basic bioinformatic techniques (probably a Biopython manual) I wouldn't hesitate to add it to my library.
This book is an excellent bioinformatics cookbook and it covers a vast range of topics, ranging from next generation sequencing to population genetics and phylogenetics. However to take fully advantage of the book is highly recommended to have both a medium-high knowledge of python and some experience in bioinformatics. Each chapter provides python code to reproduce the analyses the author performs. Even though the author explains the code in details, the code often is not trivial and a certain experience with python is required. Similarly, a previous knowledge of bioinformatic technologies is desirable as the book often uses a terminology (such as fastq, bam, vcf, etc.) that won’t be familiar to those that haven’t any experience in bioinformatics.The book is well organized and structured. The first chapter helps the reader to set up a python environment through Anaconda or Docker. In the following chapter the author starts to talk about bioinformatics and the first topic he touches is NGS (next generation sequencing). I found this chapter very interesting as it shows how to deal efficiently with the usually huge fastq files and how to perform simple but useful analyses such as quality control of the sequencing reads by position. In the same chapter the author talks about the variant call format (VCF) and how to work with it through libraries such as pyVCF. The next chapter is dedicated to whole genome sequences in fasta format and to genome annotations in GFF/GTF formats. The following chapters are about population genetics and phylogenetics. Even though I’m not an expert of these fields and I was not aware of the formats used (such as genepop) I could enjoy the reading and the code was straightforward enough to be easily understandable. Then the author explains how to use PDB (the protein data bank) and the software PyMol. A very interesting (and in my opinion important) chapter is the last one where the author discusses how to deal efficiently with big genomics datasets. Here the authors talks about concurrency and ipython parallel computing, and shows how it is possible to speed up your python code by using libraries such as Cython and Numba.Overall the book is well written and organized and is a good cookbook for bioinformaticians.
I've been in computational biology for a long time, and I find this to be one of the most impressive practical and comprehensive books I've come across (and I go through a lot!) It provides an incredibly wide survey of different domains of computational biology (e.g., protein structure, genomics, and much more), combined with lots of good Python programming advice (e.g., use of IPython/Jupyter notebooks, provided by the author). And it's about as up to date as imaginable -- quite a tour de force!
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