feat: add Smith-Waterman algorithm for local sequence alignment

DIRECTORY.md

@@ -103,10 +103,11 @@
     * [Minimum Cost Path](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/minimum_cost_path.rs)
     * [Optimal Bst](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/optimal_bst.rs)
     * [Rod Cutting](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/rod_cutting.rs)
+    * [Smith-Waterman](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/smith_waterman.rs)
     * [Snail](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/snail.rs)
     * [Subset Generation](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/subset_generation.rs)
-    * [Task Assignment](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/task_assignment.rs)
     * [Subset Sum](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/subset_sum.rs)
+    * [Task Assignment](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/task_assignment.rs)
     * [Trapped Rainwater](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/trapped_rainwater.rs)
     * [Word Break](https://github.com/TheAlgorithms/Rust/blob/master/src/dynamic_programming/word_break.rs)
   * Financial

src/dynamic_programming/mod.rs

@@ -14,6 +14,7 @@ mod maximum_subarray;
 mod minimum_cost_path;
 mod optimal_bst;
 mod rod_cutting;
+mod smith_waterman;
 mod snail;
 mod subset_generation;
 mod subset_sum;
@@ -45,6 +46,7 @@ pub use self::maximum_subarray::maximum_subarray;
 pub use self::minimum_cost_path::minimum_cost_path;
 pub use self::optimal_bst::optimal_search_tree;
 pub use self::rod_cutting::rod_cut;
+pub use self::smith_waterman::{score_function, smith_waterman, traceback};
 pub use self::snail::snail;
 pub use self::subset_generation::list_subset;
 pub use self::subset_sum::is_sum_subset;

src/dynamic_programming/smith_waterman.rs

@@ -0,0 +1,346 @@
+//! This module contains the Smith-Waterman algorithm implementation for local sequence alignment.
+//!
+//! The Smith-Waterman algorithm is a dynamic programming algorithm used for determining
+//! similar regions between two sequences (nucleotide or protein sequences). It is particularly
+//! useful in bioinformatics for identifying optimal local alignments.
+//!
+//! # Algorithm Overview
+//!
+//! The algorithm works by:
+//! 1. Creating a scoring matrix where each cell represents the maximum alignment score
+//!    ending at that position
+//! 2. Using match, mismatch, and gap penalties to calculate scores
+//! 3. Allowing scores to reset to 0 (ensuring local rather than global alignment)
+//! 4. Tracing back from the highest scoring position to reconstruct the alignment
+//!
+//! # Time Complexity
+//!
+//! O(m * n) where m and n are the lengths of the two sequences
+//!
+//! # Space Complexity
+//!
+//! O(m * n) for the scoring matrix
+//!
+//! # References
+//!
+//! - [Smith, T.F., Waterman, M.S. (1981). "Identification of Common Molecular Subsequences"](https://doi.org/10.1016/0022-2836(81)90087-5)
+//! - [Wikipedia: Smith-Waterman algorithm](https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm)
+
+use std::cmp::max;
+
+/// Calculates the score for a character pair based on match, mismatch, or gap scoring.
+///
+/// # Arguments
+///
+/// * `source_char` - Character from the source sequence
+/// * `target_char` - Character from the target sequence
+/// * `match_score` - Score awarded for matching characters (typically positive)
+/// * `mismatch_score` - Score penalty for mismatching characters (typically negative)
+/// * `gap_score` - Score penalty for gaps (typically negative)
+///
+/// # Returns
+///
+/// The calculated score for the character pair
+///
+/// # Examples
+///
+/// ```
+/// use the_algorithms_rust::dynamic_programming::score_function;
+///
+/// let score = score_function('A', 'A', 1, -1, -2);
+/// assert_eq!(score, 1); // Match
+///
+/// let score = score_function('A', 'C', 1, -1, -2);
+/// assert_eq!(score, -1); // Mismatch
+///
+/// let score = score_function('-', 'A', 1, -1, -2);
+/// assert_eq!(score, -2); // Gap
+/// ```
+pub fn score_function(
+    source_char: char,
+    target_char: char,
+    match_score: i32,
+    mismatch_score: i32,
+    gap_score: i32,
+) -> i32 {
+    if source_char == '-' || target_char == '-' {
+        gap_score
+    } else if source_char == target_char {
+        match_score
+    } else {
+        mismatch_score
+    }
+}
+
+/// Performs the Smith-Waterman local sequence alignment algorithm.
+///
+/// This function creates a scoring matrix using dynamic programming to find the
+/// optimal local alignment between two sequences. The algorithm is case-insensitive.
+///
+/// # Arguments
+///
+/// * `query` - The query sequence (e.g., DNA, protein)
+/// * `subject` - The subject sequence to align against
+/// * `match_score` - Score for matching characters (default: 1)
+/// * `mismatch_score` - Penalty for mismatching characters (default: -1)
+/// * `gap_score` - Penalty for gaps/indels (default: -2)
+///
+/// # Returns
+///
+/// A 2D vector representing the dynamic programming scoring matrix
+///
+/// # Examples
+///
+/// ```
+/// use the_algorithms_rust::dynamic_programming::smith_waterman;
+///
+/// let score_matrix = smith_waterman("ACAC", "CA", 1, -1, -2);
+/// assert_eq!(score_matrix.len(), 5); // query length + 1
+/// assert_eq!(score_matrix[0].len(), 3); // subject length + 1
+/// ```
+pub fn smith_waterman(
+    query: &str,
+    subject: &str,
+    match_score: i32,
+    mismatch_score: i32,
+    gap_score: i32,
+) -> Vec<Vec<i32>> {
+    let query_upper: Vec<char> = query.to_uppercase().chars().collect();
+    let subject_upper: Vec<char> = subject.to_uppercase().chars().collect();
+
+    let m = query_upper.len();
+    let n = subject_upper.len();
+
+    // Initialize scoring matrix with zeros
+    let mut score = vec![vec![0; n + 1]; m + 1];
+
+    // Fill the scoring matrix using dynamic programming
+    for i in 1..=m {
+        for j in 1..=n {
+            // Calculate score for match/mismatch
+            let match_or_mismatch = score[i - 1][j - 1]
+                + score_function(
+                    query_upper[i - 1],
+                    subject_upper[j - 1],
+                    match_score,
+                    mismatch_score,
+                    gap_score,
+                );
+
+            // Calculate score for deletion (gap in subject)
+            let delete = score[i - 1][j] + gap_score;
+
+            // Calculate score for insertion (gap in query)
+            let insert = score[i][j - 1] + gap_score;
+
+            // Take maximum of all options, but never go below 0 (local alignment)
+            score[i][j] = max(0, max(match_or_mismatch, max(delete, insert)));
+        }
+    }
+
+    score
+}
+
+/// Performs traceback on the Smith-Waterman score matrix to reconstruct the optimal alignment.
+///
+/// This function starts from the highest scoring cell and traces back through the matrix
+/// to reconstruct the aligned sequences. The traceback stops when a cell with score 0
+/// is encountered.
+///
+/// # Arguments
+///
+/// * `score` - The score matrix from the Smith-Waterman algorithm
+/// * `query` - Original query sequence used in alignment
+/// * `subject` - Original subject sequence used in alignment
+///
+/// # Returns
+///
+/// A String containing the two aligned sequences separated by a newline,
+/// or an empty string if no significant alignment is found
+///
+/// # Examples
+///
+/// ```
+/// use the_algorithms_rust::dynamic_programming::{smith_waterman, traceback};
+///
+/// let score_matrix = smith_waterman("ACAC", "CA", 1, -1, -2);
+/// let alignment = traceback(&score_matrix, "ACAC", "CA");
+/// assert_eq!(alignment, "CA\nCA");
+/// ```
+pub fn traceback(score: &[Vec<i32>], query: &str, subject: &str) -> String {
+    let query_upper: Vec<char> = query.to_uppercase().chars().collect();
+    let subject_upper: Vec<char> = subject.to_uppercase().chars().collect();
+
+    // Find the cell with maximum score
+    let mut max_value = i32::MIN;
+    let (mut i_max, mut j_max) = (0, 0);
+
+    for (i, row) in score.iter().enumerate() {
+        for (j, &value) in row.iter().enumerate() {
+            if value > max_value {
+                max_value = value;
+                i_max = i;
+                j_max = j;
+            }
+        }
+    }
+
+    // If no significant alignment found, return empty string
+    if i_max == 0 || j_max == 0 {
+        return String::new();
+    }
+
+    // Traceback from the maximum scoring cell
+    let (mut i, mut j) = (i_max, j_max);
+    let mut align1 = String::new();
+    let mut align2 = String::new();
+
+    // Continue tracing back until we hit a cell with score 0
+    while i > 0 && j > 0 && score[i][j] > 0 {
+        let current_score = score[i][j];
+
+        // Check if we came from diagonal (match/mismatch)
+        if current_score
+            == score[i - 1][j - 1]
+                + score_function(query_upper[i - 1], subject_upper[j - 1], 1, -1, -2)
+        {
+            align1.insert(0, query_upper[i - 1]);
+            align2.insert(0, subject_upper[j - 1]);
+            i -= 1;
+            j -= 1;
+        }
+        // Check if we came from above (deletion/gap in subject)
+        else if current_score == score[i - 1][j] - 2 {
+            align1.insert(0, query_upper[i - 1]);
+            align2.insert(0, '-');
+            i -= 1;
+        }
+        // Otherwise we came from left (insertion/gap in query)
+        else {
+            align1.insert(0, '-');
+            align2.insert(0, subject_upper[j - 1]);
+            j -= 1;
+        }
+    }
+
+    format!("{align1}\n{align2}")
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    macro_rules! smith_waterman_tests {
+        ($($name:ident: $test_cases:expr,)*) => {
+            $(
+                #[test]
+                fn $name() {
+                    let (query, subject, match_score, mismatch_score, gap_score, expected) = $test_cases;
+                    assert_eq!(smith_waterman(query, subject, match_score, mismatch_score, gap_score), expected);
+                }
+            )*
+        }
+    }
+
+    macro_rules! traceback_tests {
+        ($($name:ident: $test_cases:expr,)*) => {
+            $(
+                #[test]
+                fn $name() {
+                    let (score, query, subject, expected) = $test_cases;
+                    assert_eq!(traceback(&score, query, subject), expected);
+                }
+            )*
+        }
+    }
+
+    smith_waterman_tests! {
+        test_acac_ca: ("ACAC", "CA", 1, -1, -2, vec![
+            vec![0, 0, 0],
+            vec![0, 0, 1],
+            vec![0, 1, 0],
+            vec![0, 0, 2],
+            vec![0, 1, 0],
+        ]),
+        test_agt_agt: ("AGT", "AGT", 1, -1, -2, vec![
+            vec![0, 0, 0, 0],
+            vec![0, 1, 0, 0],
+            vec![0, 0, 2, 0],
+            vec![0, 0, 0, 3],
+        ]),
+        test_agt_gta: ("AGT", "GTA", 1, -1, -2, vec![
+            vec![0, 0, 0, 0],
+            vec![0, 0, 0, 1],
+            vec![0, 1, 0, 0],
+            vec![0, 0, 2, 0],
+        ]),
+        test_agt_g: ("AGT", "G", 1, -1, -2, vec![
+            vec![0, 0],
+            vec![0, 0],
+            vec![0, 1],
+            vec![0, 0],
+        ]),
+        test_g_agt: ("G", "AGT", 1, -1, -2, vec![
+            vec![0, 0, 0, 0],
+            vec![0, 0, 1, 0],
+        ]),
+        test_empty_query: ("", "CA", 1, -1, -2, vec![vec![0, 0, 0]]),
+        test_empty_subject: ("ACAC", "", 1, -1, -2, vec![vec![0], vec![0], vec![0], vec![0], vec![0]]),
+        test_both_empty: ("", "", 1, -1, -2, vec![vec![0]]),
+    }
+
+    traceback_tests! {
+        test_traceback_acac_ca: (
+            vec![
+                vec![0, 0, 0],
+                vec![0, 0, 1],
+                vec![0, 1, 0],
+                vec![0, 0, 2],
+                vec![0, 1, 0],
+            ],
+            "ACAC",
+            "CA",
+            "CA\nCA",
+        ),
+        test_traceback_agt_agt: (
+            vec![
+                vec![0, 0, 0, 0],
+                vec![0, 1, 0, 0],
+                vec![0, 0, 2, 0],
+                vec![0, 0, 0, 3],
+            ],
+            "AGT",
+            "AGT",
+            "AGT\nAGT",
+        ),
+        test_traceback_empty: (vec![vec![0, 0, 0]], "ACAC", "", ""),
+    }
+
+    #[test]
+    fn test_score_function_match() {
+        assert_eq!(score_function('A', 'A', 1, -1, -2), 1);
+        assert_eq!(score_function('G', 'G', 2, -1, -1), 2);
+    }
+
+    #[test]
+    fn test_score_function_mismatch() {
+        assert_eq!(score_function('A', 'C', 1, -1, -2), -1);
+        assert_eq!(score_function('G', 'T', 1, -2, -1), -2);
+    }
+
+    #[test]
+    fn test_score_function_gap() {
+        assert_eq!(score_function('-', 'A', 1, -1, -2), -2);
+        assert_eq!(score_function('A', '-', 1, -1, -2), -2);
+    }
+
+    #[test]
+    fn test_case_insensitive() {
+        let result1 = smith_waterman("acac", "CA", 1, -1, -2);
+        let result2 = smith_waterman("ACAC", "ca", 1, -1, -2);
+        let result3 = smith_waterman("AcAc", "Ca", 1, -1, -2);
+
+        assert_eq!(result1, result2);
+        assert_eq!(result2, result3);
+    }
+}