Python for Bioinformatics

These are the notebooks for the Python with Bioinformatics Cookbook.

Datasets

Click here for the datasets used in the book. You only need this if you do not use the notebooks (as the notebooks will take care of the data)

Python 2 or 3?

Depending on your Python version, some content might not be available. Lets test that:

The new version of DendroPy supports Python 3. You are thus encouraged to consider Python 3

Python and the surrounding software ecology

• Interfacing with R
• R Magic

Next Generation Sequencing

• Accessing Databases
• Basic Sequence Processing
• Working with FASTQ files
• Working with BAM files
• Working with VCF files
• Filtering SNPs

Genomics

• Working with high-quality reference genomes
• Dealing with low-quality Reference Genomes
• Traversing Genome Annotations
• Extracting Genes from a reference using annotations
• Finding orthologues with the Ensembl REST API
• Retrieving Gene Ontology information from Ensembl

Population Genetics

• Data Formats with PLINK
• The Genepop Format
• Exploratory Analysis
• F statistics
• Principal Components Analysis (PCA)
• Admixture/Structure

Simulation in Population Genetics

• Introducing Forward-time simulations
• Simulating selection
• Doing population structure with island and stepping-stone models
• Modeling complex demographic scenarios
• Simulating the coalescent with Biopython and fastsimcoal

Phylogenetics

• Preparing the Ebola dataset
• Aligning genetic and genomic data
• Comparing sequences
• Playing recursively with trees
• Reconstructing Phylogenetic trees
• Visualizing Phylogenetic data

Proteomics

• Finding a protein in multiple databases
• Introducing Bio.PDB
• Extracting more information from a PDB file
• Computing distances on a PDB file
• Doing geometric operations
• Implementing a basic PDB parser
• Parsing mmCIF files with Biopython

The code for the PyMol recipe can be found on the pymol directory of the github project

Other topics

• Accessing the Global Biodiversity Information Facility (GBIF) via REST
• Geo-referencing GBIF datasets
• Accessing molecular-interaction databases with PSIQUIC
• Plotting protein interactions with Cytoscape the hard way

Advanced Python for Bioinformatics

• Setting the stage for high performance computing
• Designing a poor-human concurrent executor
• Doing parallel computing with IPython
• Approximating the median in a large dataset
• Optimizing code with Cython and Numba
• Programming with lazyness
• Thinking with generators