INTRODUCTION TO PYTHON FOR BIOLOGISTS
This course is being delivered by Dr Martin Jones, an expert in Python and
author of two text books,
Python for Biologists [http://www.amazon.co...gists-complete-
Advanced Python for Biologists [http://www.amazon.com/Advanced-Python-
Prices start at £475 and accommodation packages can be added for an
additional £260, includes all meals and accommodation on site for the week,
arrival Sunday before the course starts)
Course overview: Python is a dynamic, readable language that is a popular
platform for all types of bioinformatics work, from simple one-off scripts
to large, complex software projects. This workshop is aimed at complete
beginners and assumes no prior programming experience. It gives an overview
of the language with an emphasis on practical problem-solving, using
examples and exercises drawn from various aspects of bioinformatics work.
After completing the workshop, students should be in a position to (1)
apply the skills they have learned to tackle problems in their own research
and (2) continue their Python education in a self-directed way.
This workshop is aimed at all researchers and technical workers with a
background in biology who want to learn programming. The syllabus has been
planned with complete beginners in mind; people with previous programming
experience are welcome to attend as a refresher but may find the pace a bit
The workshop is delivered over ten half-day sessions (see the detailed
curriculum below). Each session consists of roughly a one hour lecture
followed by two hours of practical exercises, with breaks at the
organizer’s discretion. There will also be plenty of time for students to
discuss their own problems and data.
Students should have enough biological background to appreciate the
examples and exercise problems (i.e. they should know about DNA and protein
sequences, what translation is, and what introns and exons are). No
previous programming experience or computer skills (beyond the ability to
use a text editor) are necessary, but you'll need to have a laptop with
Module 1: Introduction.
We will start with a general introduction to Python and explain why it is
useful and how learning to program can benefit your research. Some time
will be taken to explain the format of the course. We will outline the edit-
run-fix cycle of software development and talk about how to avoid common
text editing errors. In this session, we also check that the computing
infrastructure for the rest of the course is in place. Core concepts
introduced: source code; text editors; whitespace; syntax and syntax error;
and Python versions.
Module 2: Output and text manipulation.
This session will show students how to write very simple programs that
produce output to the terminal and in doing so become comfortable with
editing and running Python code. This session also introduces many of the
technical terms that we’ll rely on in future sessions. We will run through
some examples of tools for working with text and show how they work in the
context of biological sequence manipulation. We also cover different types
of errors and error messages and learn how to go about fixing them
methodically. Core concepts introduced: terminals; standard output;
variables and naming; strings and characters; special characters; output
formatting; statements; functions; methods; arguments; comments.
Module 3: File IO and user interfaces.
We will discuss about the importance of files in bioinformatics pipelines
and workflows during this session, and we then explore the Python
interfaces for reading from and writing to files. This involves introducing
the idea of types and objects and a bit of discussion about how Python
interacts with the operating system. The practical session is spent
combining the techniques from session 2 with the file IO tools to create
basic file-processing scripts. Core concepts introduced: objects and
classes; paths and folders; relationships between variables and values;
text and binary files; newlines.
Module 4: Flow control 1: loops.
A discussion of the limitations of the techniques learned in session 3
quickly reveals that flow control is required to write more sophisticated
file-processing programs, at this point we will progress on to the concept
of loops. We look at the way in which Python loops work, and how they can
be used in a variety of contexts. We explore the use of loops and lists
together to tackle some more difficult problems. Core concepts introduced:
lists and arrays; blocks and indentation; variable scoping; iteration and
the iteration interface; ranges.
Module 5: Flow control 2: conditionals.
We will use the idea of decision-making in session 5 as a way to introduce
conditional tests and outline the different building-blocks of conditions
before showing how conditions can be combined in an expressive way. We look
at the different ways that we can use conditions to control program flow,
and how we can structure conditions to keep programs readable. Core
concepts introduced: Truth and falsehood; Boolean logic; identity and
equality; evaluation of statements; branching.
Module 6: Organizing and structuring code.
In session 6 we will discuss functions that we would like to see in Python
before considering how we can add to our computational toolbox by creating
our own. We examine the nuts and bolts of writing functions before looking
at best-practice ways of making them usable. We also look at a couple of
advanced features of Python – named arguments and defaults. Core concepts
introduced: argument passing; encapsulation; data flow through a program.
Module 7: Regular expressions.
A range of common problems in bioinformatics can be described in terms of
pattern matching; we will discuss these and give an overview of Python’s
regex tools. We look at the building blocks of regular expressions
themselves, and learn how they are a general solution to the problem of
describing patterns in strings, before practising writing some specific
examples of regular expressions. Core concepts introduced: domain-specific
languages; sessions and namespaces.
Module 8: Dictionaries.
We discuss a few examples of key-value data and see how the problem of
storing them is a common one across bioinformatics and programming in
general. We learn about the syntax for dictionary creation and manipulation
before talking about the situations in which dictionaries are a better fit
that the data structures we have learned about thus far. Core concepts
introduced: paired data types; hashing; key uniqueness; argument unpacking
Module 9: Interaction with the file system.
In the final session e discuss the role of Python in the context of a
bioinformatics workflow, and how it is often used as a language to “glue”
various other components together. We then look at the Python tools for
carrying out file and directory manipulation, and for running external
programs – two tasks that are often necessary in order to integrate our own
programs with existing ones. Core concepts introduced: processes and sub-
processes; the shell and shell utilities; program return values.
Please email any inquiries to email@example.com or visit our
Please feel free to distribute this material anywhere you feel is suitable
PR informatics other courses
1. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS #BIGB
3rd – 7th July 2017, Scotland, Dr. Nic Blouin, Dr. Ian Misner
2. INTRODUCTION TO BIOINFORMATICS USING LINUX #IBUL
16th – 20th October, Scotland, Dr. Martin Jones
3. INTRODUCTION TO PYTHON FOR BIOLOGISTS #IPYB
27th Nov – 1st Dec, Wales, Dr. Martin Jones
4. INTRODUCTION REMOTE SENSING AND GIS APPLICATIONS FOR ECOLOGISTS
27th Nov – 1st Dec, Wales, Dr Duccio Rocchini, Dr. Luca Delucchi
5. DATA VISUALISATION AND MANIPULATION USING PYTHON #DVMP
11th – 15th December 2017, Wales, Dr. Martin Jones
6. EUKARYOTIC METABARCODING
23rd – 27th July 2018, Scotland, Dr. Owen Wangensteen
7. CODING, DATA MANAGEMENT AND SHINY APPLICATIONS USING RSTUDIO FOR
EVOLUTIONARY BIOLOGISTS AND ECOLOGISTS #CDSR
Dr. Aline Quadros
PR stats other courses
1. META-ANALYSIS IN ECOLOGY, EVOLUTION AND ENVIRONMENTAL SCIENCES
24th – 28th July, Scotland, Prof. Julia Koricheva, Prof. Elena Kulinskaya
2. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R #SPAE
7th – 12th August 2017, Scotland, Prof. Jason Matthiopoulos, Dr. James
3. ECOLOGICAL NICHE MODELLING USING R #ENMR
16th – 20th October 2017, Scotland, Dr. Neftali Sillero
4. GENETIC DATA ANALYSIS AND EXPLORATION USING R #GDAR
23rd – 27th October, Wales, Dr. Thibaut Jombart, Zhian Kavar
5. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY
BIOLOGISTS USING R #SEMR
23rd – 27th October, Wales, Prof Jarrett Byrnes, Dr. Jon Lefcheck
6. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R #LNDG
6th – 10th November, Wales, Prof. Rodney Dyer
7. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS #ABME
20th - 25th November 2017, Scotland, Prof. Jason Matthiopoulos, Dr. Matt
8. ADVANCING IN STATISTICAL MODELLING USING R #ADVR
11th – 15th December 2017, Wales, Dr. Luc Bussiere, Dr. Tom Houslay, Dr.
Ane Timenes Laugen,
9. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING #IBHM
29th Jan – 2nd Feb 2018, Scotland, Dr. Andrew Parnell
10. PHYLOGENETIC DATA ANALYSIS USING R (TBC) #PHYL
28th Jan – Feb 2nd Dr. Emmanuel Paradis – Date and location to be confirmed
11. ANIMAL MOVEMENT ECOLOGY (February 2018) #ANME
19th – 23rd February 2018, Wales, Dr Luca Borger, Dr. John Fieberg
12. GEOMETRIC MORPHOMETRICS USING R #GMMR
5th – 9th June 2017, Scotland, Prof. Dean Adams, Prof. Michael Collyer, Dr.
13. FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND
5th – 9th March 2018, Scotland, Dr. Francesco de Bello, Dr. Lars
Götzenberger, Dr. Carlos Carmona
14. MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R #MBMV0
8th – 12th July 2018, Prof David Warton
15. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA USING
Prof. Pierre Legendre, Dr. Olivier Gauthier - Date and location to be
16. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR #SIMM
Dr. Andrew Parnell, Dr. Andrew Jackson – Date and location to be confirmed
17. NETWORK ANAYLSIS FOR ECOLOGISTS USING R #NTWA
Dr. Marco Scotti - Date and location to be confirmed
18. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA #MASE
Prof. Subhash Lele, Dr. Peter Solymos - Date and location to be confirmed
19. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 #TSME
Dr. Andrew Parnell - Date and location to be confirmed
Oliver Hooker PhD.
2017 publications -
Ecosystem size predicts eco-morphological variability in post-glacial
diversification. Ecology and Evolution. In press.
The physiological costs of prey switching reinforce foraging
specialization. Journal of animal ecology.
3/1, 128 Brunswick Street
+44 (0) 7966500340
Introduction to Python for Biologists Nov 27 - Dec 1
Posted 06 July 2017 - 08:01 AM
INTRODUCTION TO PYTHON FOR BIOLOGISTS
0 user(s) are reading this topic
0 members(s), 0 guests(s) and 0 anonymous member(s)