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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

Finding genomic features from sequencing annotations


We will finalize this chapter and this book with a simple recipe that suggests that sometimes you can learn important things from simple unexpected results, and that apparent quality issues might mask important biological questions.

We will plot read depth (DP) across chromosome arm 2L for all the parents on our crosses. The recipe can be found on Chapter11/2L.ipynb.

 

 

How to do it…

  1. Let's start with the usual imports:
%matplotlib inline

from collections import defaultdict
import gzip

import numpy as np
import matplotlib.pylab as plt
  1. Let's load the data that we saved on the first recipe:
num_parents = 8
dp_2L = np.load(gzip.open('DP_2L.npy.gz', 'rb'))
print(dp_2L.shape)
  1. And let's print median DP for the whole chromosome arm, and a part of it in the middle for all parents:
for i in range(num_parents):
    print(np.median(dp_2L[:,i]), np.median(dp_2L[50000:150000,i]))

Interestingly, the median for the whole chromosome sometimes does not hold for...

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