| Title: | Scien. Prog. for Biologists |
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| Long Title: | Scientific Programming for Biologists |
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| Field of Study: |
Computer Science
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| Valid From: |
Semester 1 - 2021/22 ( September 2021 ) |
| Next Review Date: |
April 2021 |
| Module Coordinator: |
Brigid Lucey |
| Module Author: |
Mohammed Hasanuzzaman |
| Module Description: |
Computational and experimental biologists as well as biostatisticians often develop a 'workflow' that involves several steps such as data collection, analysis, model building and testing hypothesis. This module aims at learning to program for scientific computation. The learners will be equipped with the skills to effectively use a collection of non-commercial tools and libraries for computational biology and bioinformatics. |
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| LO1 |
Analyse fundamental components of high-level programming language. |
| LO2 |
Develop programs for solving specific tasks using standard programming concepts. |
| LO3 |
Compare programming techniques to clean, transform and automate biological data analysis. |
| LO4 |
Integrate off-the-shelf libraries and their associated functionalities to address biological questions. |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list. |
| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
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| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Introduction
Importance of programming in today's world, categories of programming language, their typical application in scientific computing, basic concepts in Python programming: source code, text editors, syntax, semantics, statements, Python versions and installation.
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String Manipulation
Learn to write and execute simple python programs. Works in the context of biological sequence manipulation-concepts of terminals, standard output, variables and naming, strings and characters, special characters, comments, and error handling.
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Flow control
Importance of flow control- conditional statements, loops. Learn truth and falsehood, Boolean logic, identity and equality, evaluation of statements, branching, iteration, and ranges.
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Data Structures
Various data structures such as arrays, lists, tuples, dictionaries, sets and how to use them.
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Functions
Learn how to write functions in Python. Concepts of argument passing, encapsulation, data flow through a program.
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File handling
Introduce the importance of files in biological pipelines and workflows, and explore the Python interfaces for reading from and writing to files. Concepts: paths and folders, relationships between variables and values, text and binary files, and newlines.
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Python library
How to find and install packages, create data objects defined in the package, and write programs that use these objects. Introduction BioPython (or maybe SciKit Bio)- tools for computational biology and bioinformatics.
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| Assessment Breakdown | % |
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| Course Work | 100.00% |
| Course Work |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Reflective Journal |
Compendium of labs into a report ensuring that the student is engaging in practical laboratory sessions. |
1,2,3 |
50.0 |
Week 7 |
| Project |
Develop a workflow to address specific biological problems using Python libraries and techniques. Produce a report documenting findings and critically analyzing results. |
2,3,4 |
50.0 |
Sem End |
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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The institute reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Delivers the concepts and theories underpinning the learning outcomes. |
2.0 |
Every Week |
2.00 |
| Lab |
Application of learning to case studies and project work. |
2.0 |
Every Week |
2.00 |
| Independent Learning |
Student reads recommended books and practices implementation. |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Delivers the concepts and theories underpinning the learning outcomes. |
2.0 |
Every Week |
2.00 |
| Lab |
Application of learning to case studies and project work. |
2.0 |
Every Week |
2.00 |
| Independent Learning |
Student reads recommended books and practices implementation. |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
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- Eric Matthes 2019, Python Crash Course: A Hands-On, Project-Based Introduction to Programming, 2nd Edition Ed., No Starch Press [ISBN: 1593279280]
- Al Sweigart 2019, Automate the Boring Stuff with Python, 2nd Edition Ed., No Starch Press [ISBN: 1593279922]
- Dr Martin Jones 2020, Biological data exploration with Python, pandas and seaborn: Clean, filter, reshape and visualize complex biological datasets using the scientific Python stack, Independently published (3 Jun. 2020) [ISBN: 9798612757238]
- Tiago Antao 2018, Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology, 2nd Ed., Packt Publishing [ISBN: 1789344697]
| | Recommended Article/Paper Resources |
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- Valentin Zulkower, Susan Rosser 2020, DNA Features Viewer, a sequence annotations formatting and plotting library for Python, Bioinformatics, Volume 36, Issue 15, 1 August 2020
- Sam M Ireland, Andrew C R Martin 2020, atomium—a Python structure parser, Bioinformatics, Volume 36, Issue 9, 1 May 2020
- David J Wooten, Réka Albert 2020, synergy - A Python library for calculating, analyzing, and visualizing drug combination synergy, Bioinformatics, 22 September 2020
| | Other Resources |
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- Website: Biopython
- Website: SciPy library
- Website: NumPy
- Website: Pandas - Python Data Analysis Library
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Module Delivered in
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