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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago Antao
4 (8)
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4 (8)
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, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)
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Advanced NGS Data Processing

If you work with next-generation sequencing (NGS) data, you know that quality analysis and processing are two of the great time-sinks in getting results. In the first part of this chapter, we will delve deeper into NGS analysis by using a dataset that includes information about relatives – in our case, a mother, a father, and around 20 offspring. This is a common technique for performing quality analysis, as pedigree information will allow us to make inferences on the number of errors that our filtering rules might produce. We will also take the opportunity to use the same dataset to find genomic features based on existing annotations.

The last recipe of this chapter will delve into another advanced topic using NGS data: metagenomics. We will QIIME2, a Python package for metagenomics, to analyze data.

If you are using Docker, please use the tiagoantao/bioinformatics_base image. The QIIME2 content has a special setup process that will be discussed...

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