At the end of this course you can:
- implement an integrative computational analysis to test a biological hypothesis using high-throughput (epi)genomic and proteomic data;
- write a simple Python program to read and process big, text-based data files;
- design and implement simple algorithms to gain biological insights from data;
- use Python to perform exploratory analysis of high-dimensional data;
- visualize complex biological data and analysis results at the genomic level to understand biological systems.
|
|
|
Technological advances in the fields of genomics and proteomics have accelerated the ease and speed of data collection. High-throughput instruments, such as DNA sequencers and mass spectrometers, generate large amounts of biological measurements. This has brought the goal of understanding gene regulation within a living cell at the systems levels much closer. However, to integrate and analyze these various big data sets, a quantitative approach to biology is needed.
In this course you will learn to apply the Python programming language in combination with the Pandas data analysis framework for data analysis. In this course, you will apply Python in a biological context, focusing mostly on (epi)genomic and proteomic data. The course will deal with a complete view of data analysis, from reading and processing raw data files to interpretation and visualization within the biological context.
Topics
- Programming in Python
- Jupyter notebooks
- Lists & dictionaries
- Control flow & loops
- Files
- Scripts
- Debugging
- Functions and modules
- The pandas module
- Visualization
Instructional Modes
- Lecture
- Self-study
- Computer practical
|
 |
|
|
| Knowledge of the principles of genome architecture and gene regulation (NWI-BB064B or Lodish 7th edition chpt 5-7 or equivalent)
Basic statistics (NWI-MOL028 or equivalent)
Practical knowledge of Linux and the Bash shell and the ability to use the command-line to manipulate common genomics data files (NWI-BB086 or the on-line edX course “Introduction to Linux” or equivalent). |
|
|
|