Assignment
Overview
The
purpose of this assigment is to have you implement the
Needleman-Wunsch global pairwise sequence alignment algorithm
(dynamic programming).
See
this [video](https://www.youtube.com/watch?v=NqYY0PJbD3s) for a walk
through of the algorithm implementation
Assignment
Tasks
Coding
Assessment
**Note:
All modules you need have already been imported.**
*
[TODO] Complete the `NeedlemanWunsch.align` method found in the
align/align.py
*
Finish the method for filling in the alignment and gap matrices for
Needleman-Wunsch.
*
[TODO] Complete the `NeedlemanWunsch._backtrace` method found in
align/align.py
*
Use the heuristic you have chosen to implement in the the
`NeedlemanWunsch.align` method to implement the backtracing
procedure.
*
[TODO] Complete the `main` function in main.py to
1.
align all provided species BRD2 sequences to the human BRD2 sequence
and print the species in order of most similar to least similar with
respect to human BRD2.
2.
print the alignment scores corresponding to each species alignment to
the human BRD2 sequence.
Software
Development Assessment
Unit
Tests
*
[TODO] Complete the `test_nw_alignment` function in
test/test_align.py to test for proper matrix filling in your
`NeedlemanWunsch.align` method.
*
[TODO] Complete the `test_nw_backtrace` function in
test/test_align.py to test for proper backtracing in your
`NeedlemanWunsch._backtrace` method.
Note:
To check that you have correclty implemented your algorithm the
following information is provided
*
test_seq3.fa and test_seq4.fa should have an alignment score of
**17** and an alignment of:
MAVHQLIRRP
M---QLIRHP
[TODO]
Ensure that it runs pytest
Pip
Installable
*
[TODO] make .toml file with flit and ensure that your package can be
installed with pip
Getting
Started
To
get started you will need to fork this repository onto your own
Github account. Work on the codebase from your own repo and commit
changes.
The
following packages will be needed:
*
numpy
*
pytest
Completing
the assignment
Make
sure to push all your code to Github, ensure that your unit tests are
correct, and submit a link to your Github through the Google
classroom assignment.
Grading
Code
(6 points)
*
Pairwise global alignment works properly (6)
*
Correct implementation of Needleman-Wunsch algorithm (4)
*
Produces correct order of species in main.py (1)
*
Produces correct NW alignment scores in main.py (1)
Unit
tests (3 points)
*
`test_nw_alignment` function properly checks that matrices are filled
in correctly for alignment of test_seq1.fa and test_seq2.fa (1)
*
`test_nw_backtrace` function properly checks that backtrace works
correctly (1)
*
Ensure functionality with pytest (1)
Style
(1 points)
*
Readable code with clear comments and method descriptions (1)
Extra
credit (0.5)
*
Github actions/workflow (0.5)
