Book Image

Bioinformatics with Python Cookbook - Second Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Second Edition

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data. This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries. This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark. By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data.
Table of Contents (16 chapters)
Title Page
About Packt
Contributors
Preface
Index

Introducing forward-time simulations


We will start with a simple recipe to code the bare minimum with simuPOP. simuPOP is probably the most flexible and powerful forward-time simulator available, and it's Python-based. You will be able to simulate almost anything in terms of demography and genomics, save for complex genome structural variation (for example, inversions or translocations).

Getting ready

simuPOP programming may appear difficult, but it will make sense if you understand its event-oriented model. As you might expect, there is a meta-population composed of individuals with a predefined genomic structure. Starting with an initial population that you prepare, a set of initial operators is applied. Then, every time a generation ticks, a set of pre-operators are applied, followed by a mating step that generates the new population for the next cycle. This is followed by a final set of post-operators that are applied again. This cycle (pre-operations, mating, and post-operations) repeats...