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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
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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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Studying genome accessibility and filtering SNP data

While the previous recipes were focused on giving an overview of Python libraries to deal with alignment and variant call data, in this recipe, we will concentrate on actually using them with a clear purpose in mind.

If you are using NGS data, chances are that your most important file to analyze is a VCF file, which is produced by a genotype caller such as SAMtools, mpileup, or GATK. The quality of your VCF calls may need to be assessed and filtered. Here, we will put in place a framework to filter SNP data. Rather than giving you filtering rules (an impossible task to be performed in a general way), we will give you procedures to assess the quality of your data. With this, you can devise your own filters.

Getting ready

In the best-case scenario, you have a VCF file with proper filters applied. If this is the case, you can just go ahead and use your file. Note that all VCF files will have a FILTER column, but this might...

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