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Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Use modern Python libraries and applications to solve real-world computational biology problems

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803236421
Length 360 pages
Edition 3rd Edition
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Author (1):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Python and the Surrounding Software Ecology 2. Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib FREE CHAPTER 3. Chapter 3: Next-Generation Sequencing 4. Chapter 4: Advanced NGS Data Processing 5. Chapter 5: Working with Genomes 6. Chapter 6: Population Genetics 7. Chapter 7: Phylogenetics 8. Chapter 8: Using the Protein Data Bank 9. Chapter 9: Bioinformatics Pipelines 10. Chapter 10: Machine Learning for Bioinformatics 11. Chapter 11: Parallel Processing with Dask and Zarr 12. Chapter 12: Functional Programming for Bioinformatics 13. Index 14. Other Books You May Enjoy

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 = "your@email.here"
hdl = Entrez.efetch(db='nucleotide', id=['NM_002299'], rettype='gb') # Lactase gene...
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