Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Bioinformatics with Python Cookbook
  • Table Of Contents Toc
  • Feedback & Rating feedback
Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

By : Tiago Antao
4 (8)
Buy this Book
close
close
Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4 (8)
By: Tiago Antao
Buy this Book

Overview of this book

If you have intermediate-level knowledge of Python and are well aware of the main research and vocabulary in your bioinformatics topic of interest, this book will help you develop your knowledge further.
Table of Contents (15 chapters)
close
close
close
Preface
Icon
Preface
Icon
Who this book is for
Icon
What this book covers
Icon
To get the most out of this book
Icon
Download the color images
Icon
Conventions used
Icon
Sections
Icon
Get in touch
Icon
Reviews
Icon
Share Your Thoughts
Lock Free Chapter
1
Chapter 1: Python and the Surrounding Software Ecology
Icon
Chapter 1: Python and the Surrounding Software Ecology
Icon
Installing the required basic software with Anaconda
Icon
Installing the required software with Docker
Icon
Interfacing with R via rpy2
Icon
Performing R magic with Jupyter
2
Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib
Icon
Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib
Icon
Using pandas to process vaccine-adverse events
Icon
Dealing with the pitfalls of joining pandas DataFrames
Icon
Reducing the memory usage of pandas DataFrames
Icon
Accelerating pandas processing with 
Apache Arrow
Icon
Understanding NumPy as the engine behind Python data science and bioinformatics
Icon
Introducing Matplotlib for chart generation
3
Chapter 3: Next-Generation Sequencing
Icon
Chapter 3: Next-Generation Sequencing
Icon
Accessing GenBank and moving around NCBI databases
Icon
Performing basic sequence analysis
Icon
Working with modern sequence formats
Icon
Working with alignment data
Icon
Extracting data from VCF files
Icon
Studying genome accessibility and filtering SNP data
Icon
Processing NGS data with HTSeq
4
Chapter 4: Advanced NGS Data Processing
chevron up
Icon
Chapter 4: Advanced NGS Data Processing
Icon
Preparing a dataset for analysis
Icon
Using Mendelian error information for quality control
Icon
Exploring the data with standard statistics
Icon
Finding genomic features from sequencing annotations
Icon
Doing metagenomics with QIIME 2 Python API
5
Chapter 5: Working with Genomes
Icon
Chapter 5: Working with Genomes
Icon
Technical requirements
Icon
Working with high-quality reference genomes
Icon
Dealing with low-quality genome references
Icon
Traversing genome annotations
Icon
Extracting genes from a reference using annotations
Icon
Finding orthologues with the Ensembl REST API
Icon
Retrieving gene ontology information from Ensembl
6
Chapter 6: Population Genetics
Icon
Chapter 6: Population Genetics
Icon
Managing datasets with PLINK
Icon
Using sgkit for population genetics analysis with xarray
Icon
Exploring a dataset with sgkit
Icon
Analyzing population structure
Icon
Performing a PCA
Icon
Investigating population structure with admixture
7
Chapter 7: Phylogenetics
Icon
Chapter 7: Phylogenetics
Icon
Preparing a dataset for phylogenetic analysis
Icon
Aligning genetic and genomic data
Icon
Comparing sequences
Icon
Reconstructing phylogenetic trees
Icon
Playing recursively with trees
Icon
Visualizing phylogenetic data
8
Chapter 8: Using the Protein Data Bank
Icon
Chapter 8: Using the Protein Data Bank
Icon
Finding a protein in multiple databases
Icon
Introducing Bio.PDB
Icon
Extracting more information from a PDB file
Icon
Computing molecular distances on a PDB file
Icon
Performing geometric operations
Icon
Animating with PyMOL
Icon
Parsing mmCIF files using Biopython
9
Chapter 9: Bioinformatics Pipelines
Icon
Chapter 9: Bioinformatics Pipelines
Icon
Introducing Galaxy servers
Icon
Accessing Galaxy using the API
Icon
Deploying a variant analysis pipeline with Snakemake
Icon
Deploying a variant analysis pipeline with Nextflow
10
Chapter 10: Machine Learning for Bioinformatics
Icon
Chapter 10: Machine Learning for Bioinformatics
Icon
Introducing scikit-learn with a PCA example
Icon
Using clustering over PCA to classify samples
Icon
Exploring breast cancer traits using Decision Trees
Icon
Predicting breast cancer outcomes using Random Forests
11
Chapter 11: Parallel Processing with Dask and Zarr
Icon
Chapter 11: Parallel Processing with Dask and Zarr
Icon
Reading genomics data with Zarr
Icon
Parallel processing of data using Python multiprocessing
Icon
Using Dask to process genomic data based on NumPy arrays
Icon
Scheduling tasks with dask.distributed
12
Chapter 12: Functional Programming for Bioinformatics
Icon
Chapter 12: Functional Programming for Bioinformatics
Icon
Understanding pure functions
Icon
Understanding immutability
Icon
Avoiding mutability as a robust development pattern
Icon
Using lazy programming for pipelining
Icon
The limits of recursion with Python
Icon
A showcase of Python’s functools module
13
Index
Icon
Index
Icon
Why subscribe?
14
Other Books You May Enjoy
Icon
Other Books You May Enjoy
Icon
Packt is searching for authors like you
Icon
Share Your Thoughts

Introducing the Genepop format

The Genepop format is used in many conservation genetics studies. It's the format of the Genepop application and is the de facto format for many population genetics analysis. If you come from other fields (for example, have a lot of sequencing experience), you may not have heard of it, but this format is widely used (as its citation record proves) and is worth a look. Here, we will convert some datasets from previous recipes to this format and introduce the Genepop parser from Biopython.

Getting ready

You will need to run the previous recipe because its output will be required for this one.

If you are not using Docker, you might not be using some of the code that I produced earlier (mostly to deal with the bread and butter of data conversion). You can find this code at https://github.com/tiagoantao/pygenomics and install it from

pip install pygenomics

Note that at this stage, we will not use the Genepop application (this will change in the next recipe), so...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Bioinformatics with Python Cookbook
End of Chapter 4
Next Chapter
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon