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Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789344691
Length 360 pages
Edition 2nd Edition
Languages
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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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Table of Contents (12) Chapters Close

Preface 1. Python and the Surrounding Software Ecology FREE CHAPTER 2. Next-Generation Sequencing 3. Working with Genomes 4. Population Genetics 5. Population Genetics Simulation 6. Phylogenetics 7. Using the Protein Data Bank 8. Bioinformatics Pipelines 9. Python for Big Genomics Datasets 10. Other Topics in Bioinformatics 11. Advanced NGS Processing

Preparing the dataset for analysis


Our starting point will be a VCF file (or equivalent), with calls made by a genotyper (Genome Analysis Toolkit (GATK) in our case), including the annotations. As we will be filtering NGS data, we need reliable decision criteria to call a site. So, how do we get that information? Generally, we can't, but if we need to do it, there are three basic approaches:

  • Using a more robust sequencing technology for comparison; for example, using Sanger sequencing to verify NGS datasets. This is cost-prohibitive and can only be done for a few loci.
  • Sequencing closely related individuals, for example, two parents and their offspring. In this case, we use Mendelian inheritance rules to devise if a certain call is acceptable or not. This was the strategy used by both the human and Anopheles 1,000 Genomes Projects.
  • Finally, we can use simulations. This setup is not only quite complex, but also of dubious reliability. It's more of a theoretical option.

In this chapter, we will...

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