Modern bioinformatics analysis is normally performed on a Linux server. Most of our recipes will also work on macOS. 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/macOS computer. An alternative that we explore in the book is to use Docker as a container, which can be used on Windows and macOS.

As modern bioinformatics is a big data discipline, you will need a reasonable amount of memory; at least 8 GB on a native Linux machine, probably 16 GB on a macOS/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.

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Bioinformatics-with-Python-Cookbook-Second-Edition. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789344691_ColorImages.pdf.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We will read the data from our file using R's read.delim function."

A block of code is set as follows:

import os
from IPython.display import Image
import rpy2.robjects as robjects
import pandas as pd
from rpy2.robjects import pandas2ri

Any command-line input or output is written as follows:

conda install r-essentials r-gridextra

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "On the top menu choose User, inside choose Preferences."