7. Using the Protein Data Bank | Bioinformatics with Python Cookbook

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Bioinformatics with Python Cookbook

By : Tiago R Antao, Tiago Antao

4.7 (6)

Bioinformatics with Python Cookbook

4.7 (6)

By: Tiago R Antao, Tiago Antao

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

1. Python and the Surrounding Software Ecology

1. Python and the Surrounding Software Ecology

Introduction

Installing the required software with Anaconda

Installing the required software with Docker

Interfacing with R via rpy2

Performing R magic with IPython

2. Next-generation Sequencing

2. Next-generation Sequencing

Introduction

Accessing GenBank and moving around NCBI databases

Performing basic sequence analysis

Working with modern sequence formats

Working with alignment data

Analyzing data in the variant call format

Studying genome accessibility and filtering SNP data

3. Working with Genomes

3. Working with Genomes

Introduction

Working with high-quality reference genomes

Dealing with low-quality genome references

Traversing genome annotations

Extracting genes from a reference using annotations

Finding orthologues with the Ensembl REST API

Retrieving gene ontology information from Ensembl

4. Population Genetics

4. Population Genetics

Introduction

Managing datasets with PLINK

Introducing the Genepop format

Exploring a dataset with Bio.PopGen

Computing F-statistics

Performing Principal Components Analysis

Investigating population structure with Admixture

5. Population Genetics Simulation

5. Population Genetics Simulation

Introduction

Introducing forward-time simulations

Simulating selection

Simulating population structure using island and stepping-stone models

Modeling complex demographic scenarios

Simulating the coalescent with Biopython and fastsimcoal

6. Phylogenetics

6. Phylogenetics

Introduction

Preparing the Ebola dataset

Aligning genetic and genomic data

Comparing sequences

Reconstructing phylogenetic trees

Playing recursively with trees

Visualizing phylogenetic data

7. Using the Protein Data Bank

7. Using the Protein Data Bank

Introduction

Finding a protein in multiple databases

Introducing Bio.PDB

Extracting more information from a PDB file

Computing molecular distances on a PDB file

Performing geometric operations

Implementing a basic PDB parser

Animating with PyMol

Parsing mmCIF files using Biopython

8. Other Topics in Bioinformatics

8. Other Topics in Bioinformatics

Introduction

Accessing the Global Biodiversity Information Facility

Geo-referencing GBIF datasets

Accessing molecular-interaction databases with PSIQUIC

Plotting protein interactions with Cytoscape the hard way

9. Python for Big Genomics Datasets

9. Python for Big Genomics Datasets

Introduction

Setting the stage for high-performance computing

Designing a poor human concurrent executor

Performing parallel computing with IPython

Computing the median in a large dataset

Optimizing code with Cython and Numba

Programming with laziness

Thinking with generators

Index

Index

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Implementing a basic PDB parser

As you know, by now the Bio.PDB parser is not complete. Here, we will develop a framework that allows you to parse other records on PDB files. Although we can expect a migration from PDB to the mmCIF format in the future, this is still useful in many situations.

Getting ready

In order to parse a format, we need its specification. You can find this at http://www.wwpdb.org/documentation/file-format.php. We will mostly be concerned with secondary structure records (HELIX and SHEET), but you will find more records in your scaffold parser. You can extend this scaffold to other records that you may need.

You can find this content in the 06_Prot/Parser.ipynb notebook.

How to do it...

Take a look at the following steps:

First, let's retrieve a file to work with. We will only retrieve, not parse as follows:

from __future__ import print_function
from Bio import PDB
repository = PDB.PDBList()
repository.retrieve_pdb_file('1TUP', pdir='.')

We will now devise a basic parsing framework...

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