Description

General Instructions Submission. You have two and a half weeks to complete this assessed coursework. You should upload your code to Moodle by 5pm on Monday 18th November. The main thing I want is your code – I am expecting one or more .py files If you want to provide a commentary or additional explanation, you may submit one additional .txt file. Note: all submitted files should be simple text files without any formatting. Do not submit large files, such as the input files I have provided you with. Directories. Please ensure you do your coursework in a new subdirectory (e.g. mkdir cw) and that the directory is not readable by anyone else (e.g. using the following command chmod go-rwx cw). Plagiarism and adapting code. You may use any Internet or written resources you wish, but you must not get help from any individual and must not copy from each other. Plagiarism will be treated seriously. If you use or adapt code that you find on the Internet or in a book, it is vital that you fully acknowledge the source. You may subsequently be asked questioned about the code you have written, so it is vital that you fully understand the code you submit. Depending on the amount of code you have taken from other sources, and how easy/challenging it was to adapt to the task, your mark may be reduced – the preferred option is that you write all the code yourself (except when using a standard library). General marking guidance. Note that credit will be given for partial answers (including incomplete code, or a written description of the approach you would adopt). Some marks will be reserved for the quality of the written code, including code layout, variable names, comments and docstrings. Overview Note that here I have broken down the problem into sections for clarity. However, these are not separate questions, i.e. the task should be addressed as a whole. The aim of this coursework is for you to write code, including your own functions and/or classes, that compares the amino acid usage observed within the three branches of life – Bacteria, Archaea and Eukaryota – using, as examples, the proteomes of Bacillus anthracis (5,490 proteins), Methanosarcina acetivorans (4,468 proteins) and Saccharomyces cerevisiae (6,049 proteins) respectively. (According to Wikipedia, M. acetivorans is “a versatile methane producing microbe which is found in such diverse environments as oil wells, trash dumps, deep-sea hydrothermal vents, and oxygen-depleted sediments beneath kelp beds”.) Comparisons should be made using two standard correlation coefficients and visually using a barchart (details below).