A Brief Introduction to Scientific
Programming with Python
TCD, 26/08/2015
Karsten Hokamp, PhD
TCD Bioinformatics Support Team
Trinity College Dublin, The University of Dublin
Overview
• Programming
• First Python script/program
• Why Python?
• Bioinformatics examples
• Additional resources
• Outlook
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What is programming and why bother?
 Data processing
 Automation
 Combination of programs for analysis pipelines
 More control and flexibility
 Better understanding of how programs work
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Programming Concepts
 Turn into a very meticulous problem solver
 Break problems into small details
 Keep it variable
 Give very precise instructions
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Programming Concepts
"human" recipe
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Programming Concepts
"computerised" recipe
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Mac for Windows users
The main differences:
 cmd instead of ctrl (e.g. cmd-C for copying)
 right-click mouse: ctrl-click
 # character: alt-3
 switch between applications: cmd-tab
 Spotlight (top right) for finding files/programs
 Apple symbol (top left) for logging out
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IDLE: Integrated DeveLopment Environment
open through Spotlight
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IDLE: Integrated DeveLopment Environment
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IDLE: Integrated DeveLopment Environment
Alternatively: open through Finder
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IDLE: Integrated DeveLopment Environment
interactive Python console
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IDLE: Integrated DeveLopment Environment
simple Python statement
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IDLE: Integrated DeveLopment Environment
user input
output
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IDLE: Integrated DeveLopment Environment
user input
output
try a few simple
numeric operations
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IDLE: Integrated DeveLopment Environment
repeat/combine
previous commands
by clicking into
them and hitting return
(use left/right arrows
and delete to edit them)
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IDLE: Integrated DeveLopment Environment
Console vs Editor
Console
Editor
interactive
requires extra click for running
great for trying out code
additional IDLE functionality
not suited for long scripts
suited for long scripts
no saving of code
allows to save code
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IDLE: Writing Python Scripts
open a new file
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IDLE: Writing Python Scripts
write some code
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IDLE: Writing Python Scripts
run your code
shortcut: F5
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IDLE: Writing Python Scripts
save file first
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IDLE: Writing Python Scripts
specify a file name
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IDLE: Writing Python Scripts
write more code
IDLE provides help
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IDLE: Writing Python Scripts
save and run:
cmd-S then F5
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IDLE: Writing Python Scripts
make it personal
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IDLE: Writing Python Scripts
keep going
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Python vs Perl
the equivalent
in Perl
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Python vs Perl
the equivalent
in Perl
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Python vs Perl
Python
• fewer special characters
• indentation enforced
• more user-friendly functions
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Perl
Why Python?
 easy to learn
 great for beginners
 enforces clean coding
 great for teachers
 comes with IDE
 avoids command-line usage
 object-orientated
 code reuse and recycling
 very popular
 many peers
 BioPython
 many bioinformatics modules
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Simple Bioinformatics Example
built-in function 'len'
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Simple Bioinformatics Example
built-in function 'set'
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Simple Bioinformatics Example
built-in functions 'sorted' and 'set'
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Simple Bioinformatics Example
string method 'count'
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Simple Bioinformatics Example
string method 'upper'
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 Basic sequence manipulation
 Fetch records from databases
 Multiple sequence alignment (Clustal, Muscle)
 Sequence similarity search (Blast)
 Working with motifs: MEME, Jaspar, Transfac
 Phylogenetics
 Clustering
 Visualisation
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 Parsing GenBank records:
from Bio import SeqIO
record = SeqIO.read("AE014613.1.gb", "genbank")
record.description
 'Salmonella enterica subsp. enterica serovar Typhi Ty2, complete genome.'
len(record.features)
 9086
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 Parsing sequence records:
from Bio import SeqIO
for entry in SeqIO.parse("tlr4_protein.fa", "fasta") :
print(entry.description)
print(len(entry), 'bp')
gi|765368240|gb|AJR32867.1| TLR4 [Gallus gallus]
843 bp
gi|111414439|gb|ABH09759.1| toll-like receptor 4 [Bos taurus]
841 bp
gi|6175873|gb|AAF05316.1|AF177765_1 toll-like receptor 4 [Homo sapiens]
839 bp
…
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 Graphics:
Chromosomes colour-coded by GC content (Bioinformatics with Python Cookbook)
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 Graphics:
Coloured phylogenetic tree from Ebola sequences (Bioinformatics with Python Cookbook)
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Additional Resources
https://store.continuum.io/cshop/anaconda/
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Visualisations with Matplotlib
http://matplotlib.org/gallery.html
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Examples
http://scikit-learn.org
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Scikit-learn – Machine Learning in Python
• Machine Learning: PCA of Iris data set
http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html
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Python Help
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Online courses
 http://biopython.org/DIST/docs/tutorial/Tutorial.html
 http://dowell.colorado.edu/education-python.html
 http://www.pasteur.fr/formation/infobio/python
 https://www.codecademy.com/tracks/python
 http://anh.cs.luc.edu/python/hands-on/
 https://www.coursera.org
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Books
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Conclusions
• You have been briefly introduced to Python and IDLE.
• You have learnt about programming concepts.
• You have seen examples of what can be accomplished through Python.
• Topics of an extensive Python course:
• Coding in Python – variables, scope, functions…
• Bioinformatics with BioPython
• Automated biological data analysis – your interests!
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Thank You!
http://bioinf.gen.tcd.ie/workshops/python
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Don't forget to log out!
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