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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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Performing basic sequence analysis

We will now do some basic analysis of DNA sequences. We will work with FASTA files and do some manipulation, such as reverse complementing or transcription. As with the previous recipe, we will use Biopython, which you installed in Chapter 1, Python and the Surrounding Software Ecology. These two recipes provide you with the necessary introductory building blocks with which we will perform all the modern NGS analysis and then genome processing in this chapter and Chapter 5, Working with Genomes.

Getting ready

The code for this recipe is available in Chapter03/Basic_Sequence_Processing.py. We will use the human lactase (LCT) gene as an example; you can get this using your knowledge from the previous recipe, by using the Entrez research interface:

from Bio import Entrez, SeqIO, SeqRecord
Entrez.email = "[email protected]"
hdl = Entrez.efetch(db='nucleotide', id=['NM_002299'], rettype='gb') # Lactase gene...

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