Python For Bioinformatics

Introduction to Python for Bioinformatics - available at https://github.com/kipkurui/Python4Bioinformatics.

Attribution

These tutorials are an adaptation of the Introduction to Python for Maths by Andreas Ernst, available from https://gitlab.erc.monash.edu.au/andrease/Python4Maths.git. The original version was written by Rajath Kumar and is available at https://github.com/rajathkumarmp/Python-Lectures.

These notes have been greatly amended and updated for the EANBiT Introduction to Python for Bioinformatics course facilitated Caleb Kibet, Audrey Mbogho and Anthony Etuk.

Quick Introduction to Jupyter Notebooks

Throughout this course, we will be using Jupyter Notebooks. Although the HPC you will be using will have Jupyter setup, these notes are provided for you want to set it up in your Computer.

Introduction

The Jupyter Notebook is an interactive computing environment that enables users to author notebooks, which contain a complete and self-contained record of a computation. These notebooks can be shared more efficiently. The notebooks may contain:

• Live code
• Interactive widgets
• Plots
• Narrative text
• Equations
• Images
• Video

It is good to note that "Jupyter" is a loose acronym meaning Julia, Python, and R; the primary languages supported by Jupyter.

The notebook can allow a computational researcher to create reproducible documentation of their research. As Bioinformatics is datacentric, use of Jupyter Notebooks increases research transparency, hence promoting open science.

First Steps

Installation

1. Download Miniconda for your specific OS to your home directory
   • Linux: wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
   • Mac: curl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
2. Run:
   • bash Miniconda3-latest-Linux-x86_64.sh
   • bash Miniconda3-latest-MacOSX-x86_64.sh
3. Follow all the prompts: if unsure, accept defaults
4. Close and re-open your terminal
5. If the installation is successful, you should see a list of installed packages with
   • conda list If the command cannot be found, you can add Anaconda bin to the path using: export PATH=~/miniconda3/bin:$PATH

For reproducible analysis, you can create a conda environment with all the Python packages you used.

`conda create --name bioinf python jupyter`

To activate the conda environment: source activate bioinf

Having set-up conda environment, you can install jupyter lab using pip.

conda install -c conda-forge jupyterlab

or by using pip

pip3 install jupyter

How to learn from this resource?

Download all the notebooks from [Python4Bioinformatics(https://github.com/kipkurui/Python4Bioinformatics). The easiest way to do that is to clone the GitHub repository to your working directory using any of the following commands:

git clone https://github.com/kipkurui/Python4Bioinformatics.git

or

wget https://github.com/kipkurui/Python4Bioinformatics/archive/master.zip

unzip master.zip

rm master.zip

cd Python4Bioinformatics-master

Then you can quickly launch jupyter lab using:

jupyter lab

NB: We will use a jupyter lab for training. A Jupyter notebook is made up of many cells. Each cell can contain Python code. You can execute a cell by clicking on it and pressing Shift-Enter or Ctrl-Enter (run without moving to the next line).

Login into the web server

The easiest way to run this and other notebooks for the EANBiT course participants is to log into the Jupyter server (Unfortunately, this is not currently working). The steps for running notebooks are:

• Log in using the username and password assigned to you. The first time you log in an empty account will automatically be set up for you.
• Press the start button (if prompted by the system)
• Use the menu of the jupyter system to upload a .ipynb python notebook file or to start a new notebook.

Further help

To learn more about Jupyter notebooks, check the official introduction and some useful Jupyter Tricks.

Book: http://www.ict.ru.ac.za/Resources/cspw/thinkcspy3/thinkcspy3.pdf

Python for Bioinformatics

Introduction

Python is a modern, robust, high-level programming language. It is straightforward to pick up even if you are entirely new to programming.

Python, similar to other languages like Matlab or R, is interpreted hence runs slowly compared to C++, Fortran or Java. However, writing programs in Python is very quick. Python has an extensive collection of libraries for everything from scientific computing to web services. It caters for object-oriented and functional programming with a module system that allows large and complex applications to be developed in Python.

These lectures are using Jupyter notebooks which mix Python code with documentation. The python notebooks can be run on a web server or stand-alone on a computer.

Contents

This course is broken up into a number of notebooks (lectures).

Session 1

• 00 This introduction with additional information below on how to get started in running python
• 01 Basic data types and operations (numbers, strings)

Session 2

• 02 String manipulation
• 03 Data structures: Lists and Tuples
• 04 Data structures (continued): dictionaries

Session 3

• 05 Control statements: if, for, while, try statements
• 06 Functions
• 07 Scripting with python
• 08 Data Analysis and plotting with Pandas
• 09 Reproducible Bioinformatics Research

This is a tutorial style introduction to Python. For a quick reminder/summary of Python syntax, the following Quick Reference Card may be useful. A longer and more detailed tutorial style introduction to python is available from the python site at: https://docs.python.org/3/tutorial/.

How to Contribute

To contribute, fork the repository, make some updates and send me a pull request.

Alternatively, you can open an issue.

License

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/.