Preface
Whether you are reading this book as a computational biologist or a Python programmer, you will probably relate to the "explosive growth, exciting times" expression. The recent growth of Python is strongly connected with its status as the main programming language for big data. On the other hand, the deluge of data in biology, mostly from genomics and proteomics makes bioinformatics one of the forefront applications of data science. There is a massive need for bioinformaticians to analyze all this data; of course, one of the main tools is Python. We will not only talk about the programming language, but also the whole community and software ecology behind it. When you choose Python to analyze your data, you will also get an extensive set of libraries, ranging from statistical analysis to plotting, parallel programming, machine learning, and bioinformatics. However, when you choose Python, you expect more than this; the community has a tradition of providing good documentation, reliable libraries, and frameworks. It is also friendly and supportive of all its participants.
In this book, we will present practical solutions to modern bioinformatics problems using Python. Our approach will be hands-on, where we will address important topics, such as next-generation sequencing, genomics, population genetics, phylogenetics, and proteomics among others. At this stage, you probably know the language reasonably well and are aware of the basic analysis methods in your field of research. You will dive directly into relevant complex computational biology problems and learn how to tackle them with Python. This is not your first Python book or your first biology lesson; this is where you will find reliable and pragmatic solutions to realistic and complex problems.
What this book covers
Chapter 1, Python and the Surrounding Software Ecology, tells you how to set up a modern bioinformatics environment with Python. This chapter discusses how to deploy software using Docker, interface with R, and interact with the IPython Notebook.
Chapter 2, Next-generation Sequencing, provides concrete solutions to deal with next-generation sequencing data. This chapter teaches you how to deal with large FASTQ, BAM, and VCF files. It also discusses data filtering.
Chapter 3, Working with Genomes, not only deals with high-quality references—such as the human genome—but also discusses how to analyze other low-quality references typical in non-model species. It introduces GFF processing, teaches you how to analyze genomic feature information, and discusses how to use gene ontologies.
Chapter 4, Population Genetics, describes how to perform population genetics analysis of empirical datasets. For example, on Python, we will perform Principal Components Analysis, compute FST, or Structure/Admixture plots.
Chapter 5, Population Genetics Simulation, covers simuPOP, an extremely powerful Python-based forward-time population genetics simulator. This chapter shows you how to simulate different selection and demographic regimes. It also briefly discusses the coalescent simulation.
Chapter 6, Phylogenetics, uses complete sequences of recently sequenced Ebola viruses to perform real phylogenetic analysis, which includes tree reconstruction and sequence comparisons. This chapter discusses recursive algorithms to process tree-like structures.
Chapter 7, Using the Protein Data Bank, focuses on processing PDB files, for example, performing the geometric analysis of proteins. This chapter takes a look at protein visualization.
Chapter 8, Other Topics in Bioinformatics, talks about how to analyze data made available by the Global Biodiversity Information Facility (GBIF) and how to use Cytoscape, a powerful platform to visualize complex networks. This chapter also looks at how to work with geo-referenced data and map-based services.
Chapter 9, Python for Big Genomics Datasets, discusses high-performance programming techniques necessary to handle big datasets. It briefly discusses cluster usage and code optimization platforms (such as Numba or Cython).
What you need for this book
Modern bioinformatics analysis is normally performed on a Linux server. Most of our recipes will also work on Mac OS X. It will also work on Windows in theory, but this is not recommended. If you do not have a Linux server, you can use a free virtual machine emulator such as VirtualBox to run it on a Windows/Mac computer. An alternative that we explore in the book is to use Docker as a container, which can be used on Windows and Mac via boot2docker.
As modern bioinformatics is a big data discipline, you will need a reasonable amount of memory; at least 4 GB on a native Linux machine, probably 8 GB on a Mac/Windows system, but more would be better. A broadband Internet connection will also be necessary to download the real and hands-on datasets used in the book.
Python is a requirement. All the code will work with version 2, although you are highly encouraged to use version 3 whenever possible. Many free Python libraries will also be required and these will be presented in the book. Biopython, NumPy, SciPy, and Matplotlib are used in almost all chapters. Although the IPython Notebook is not strictly required, it's highly encouraged. Different chapters will also require various bioinformatics tools. All the tools used in the book are freely available and thorough instructions are provided in the relevant chapters of this book.
Who this book is for
If you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.
Sections
In this book, you will find several headings that appear frequently. To give clear instructions on how to complete a recipe, we use these sections as follows:
Getting ready
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
How to do it…
This section contains the steps required to follow the recipe.
There's more…
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, filenames, file extensions, URLs, user input, are shown as follows: "If you
are using notebooks, open the 00_Intro/Interfacing_R notebook.ipynb and just
execute the wget command on top."
A block of code is set as follows:
from collections import OrderedDict import simuPOP as sp pop_size = 100 pops = sp.Population(pop_size, loci=[1] * num_loci) num_loci = 10 init_ops['Freq'] = sp.InitGenotype(freq=[0.5, 0.5])
Any command-line input or output is written as follows:
conda create -n bioinformatics biopython=1.65 python=3.4
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The top line is without migration, the middle line with 0.005 migration and the bottom line with 0.1."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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