Reference no: EM132842759

Problem #1 - Implement Smith-Waterman algorithm

A. Write a function in C++ that would implement the Smith-Waterman alignment between two genomic sequences.
o The function must take a genomic similarity scoring matrix (+2 for match, -1 for mismatch) and a gap penalty (-3) as an input.
o The function must return:
• score for best alignment
• alignment in text format (hint: use a struct of 3 character arrays, 2 for sequences one for alignment codes {x, I , } - use whitespace for gaps). See below.
o Test your code using the SARS-COV2 viral genome found in appendix A at the bottom of this homework assignment and sequence fragments found in appendix B at the bottom of this homework.
o You will need to submit a screenshot of the output as part of the homework write up (it should look something like the text below)

B. Generate 1K, 10K, 100K, and 1M (million) completely random genomic sequences (50nt) to use as targets for alignment and use SARS-COV2 genome as subject. Perform alignment of the queries to the subject sequence and record time to completion (in seconds / minutes).
Special note: Depending on the speed of your alignment implementation, this homework may take hours or days to complete. The goal is to get a sense for how slow these ‘optimal' alignment algorithms are... for the explicit purpose of establishing a baseline to be able to compare the improved algorithms and data structures we will be discussing later in the course. Please be aware of this and ‘give up' on larger benchmarks as appropriate.

Problem #2 - Having a BLAST

A. Implement a seed-based Smith Waterman. This means:
o Use the genome in Appendix A and break I down into seeds with word size = 11.
o Load your seeds (created in part A) into memory
o For each read disassemble it into k-mers (of size 11). Compare your read k-mers to the SARS-COV2 seeds.
o If a seed-match is found, extend the seed by cutting out the appropriate segment of the subject (Genome of SARS-COV2) and running the Smith Waterman on the two sequences (original read and the segment from SARS-COV2)
• Beware of edge cases
• Ok to just expand one seed and be done (multiple seeds from a read can be found, typically necessitating multiple seed expansions and decision on what is the best alignment)
o Test your code on the 50-mers I have provided below in Appendix B. You must report alignment for the 50-mers I've provided as part of the homework solution.

B. Test your code on a set of 1K, 10K, 100K, and 1M (million) completely random 50-mers, aligning them to SARS-COV2 genome. How long did it take? Compare it to the results in problem 1B.

C. Test algorithm's exhaustiveness. Randomly select 100,000 fragments from the SARS-COV2 genome and use these fragments to query the SARS-COV2 genome using the seed-based SW you implemented in part A. How many fragments were you able to find? Now introduce random errors into your 100,000 fragments at a 5% per-base error rate (every character has a 5% change of being changed to some other random character). Use these error-filled 100,000 fragments to query the SARS-COV2 genome again. How many fragments were you able to find?

Attachment:- algorithm_Homework.rar