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Implementation of Smith-Waterman local alignment model- find closest local alignments in two given amino acid sequences. BLOSUM was used as the scoring matrix.

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Implementation of Smith-Waterman local alignment

This is a repository for a CompBio assignment for CSEP527 course assignment page: https://courses.cs.washington.edu/courses/csep527/20au/hw/hw2.html The data was scraped from FASTA, and are located in amino-acid-sequences directory.

How to use

  • For string inputs, an example command usage is shown below
python src/smith_waterman.py --str-input -A "AKA" -B "ak" -o output/output.txt

Note: the input is not case sensitive.

  • For file inputs, an example command usage is shown below
python src/smith_waterman.py --file-input -af amino-acid-sequences/P15172.fasta -bf amino-acid-sequences/Q10574.fasta -p -o output/output.txt

Caution: -p flag calculates p-value from Fisher Yates shuffles, so for fasta file inputs or long string sequences, it may take up to 20 minutes

  • The shell script organizes the runs to generate HW2 report. In case you'd like to see more examples of the runs, please take a look at the shell script.

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Implementation of Smith-Waterman local alignment model- find closest local alignments in two given amino acid sequences. BLOSUM was used as the scoring matrix.

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