public class SmithWaterman
extends PairwiseAlignmentAlgorithm
This class implement the classic local alignment algorithm (with linear gap penalty function) due to T.F.Smith and M.S.Waterman (1981).
This algorithm is very similar to the class NeedlemanWunsch algorithm for global alignment. The idea here also consists of building an (n+1 x m+1) matrix M given two sequences A and B of sizes n and m, respectively. However, unlike in the global alignment case, every position M[i,j] in the matrix contains the similarity score of suffixes of A[1..i] and B[1..j].
Starting from row 0, column 0, the net.maizegenetics.analysis.gbs.neobio.SmithWaterman$computeMatrix()
M[0,0] = M[0,j] = M[i,0] = 0
M[i,j] = max { M[i,j-1] + scoreInsertion (B[j]),
M[i-1,j-1] + scoreSubstitution (A[i], B[j]),
M[i-1,j] + scoreDeletion(A[i]) }
Note that, here, all cells in the first row and column are set to zero. The best local alignment score is the highest value found anywhere in the matrix.
Just like in global alignment case, this algorithm has quadratic space complexity because it needs to keep an (n+1 x m+1) matrix in memory. And since the work of computing each cell is constant, it also has quadratic time complexity.
After the matrix has been computed, the alignment can be retrieved by tracing a path back in the matrix from the position of the highest score until a cell of value zero is reached. This step is performed by the net.maizegenetics.analysis.gbs.neobio.SmithWaterman$buildOptimalAlignment()
If the similarity value only is needed (and not the alignment itself), it is easy to reduce the space requirement to O(n) by keeping just the last row or column in memory. This is precisely what is done by the net.maizegenetics.analysis.gbs.neobio.SmithWaterman$computeScore()
For a more efficient approach to the local alignment problem, see the class CrochemoreLandauZivUkelson algorithm. For global alignment, see the class NeedlemanWunsch algorithm.
class NeedlemanWunsch,
net.maizegenetics.analysis.gbs.neobio.SmithWaterman$computeMatrix(),
net.maizegenetics.analysis.gbs.neobio.SmithWaterman$buildOptimalAlignment(),
net.maizegenetics.analysis.gbs.neobio.SmithWaterman$computeScore(),
class CrochemoreLandauZivUkelson,
class NeedlemanWunsch,
class NeedlemanWunsch,
class CrochemoreLandauZivUkelson,
class CrochemoreLandauZivUkelsonLocalAlignment,
class CrochemoreLandauZivUkelsonGlobalAlignmentprotected CharSequence seq1
The first sequence of an alignment.
protected CharSequence seq2
The second sequence of an alignment.
protected kotlin.Array[] matrix
The dynamic programming matrix. Each position (i, j) represents the best score between a suffic of the firsts i characters of seq1 and a suffix of the first j characters of seq2.
protected int max_row
Indicate the row of where an optimal local alignment can be found in the matrix..
protected int max_col
Indicate the column of where an optimal local alignment can be found in the matrix.
protected void loadSequencesInternal(java.io.Reader input1,
java.io.Reader input2)
Loads sequences into class CharSequence instances. In case of any error, an exception is raised by the constructor of CharSequence (please check the specification of that class for specific requirements).
input1 - Input for first sequenceinput2 - Input for second sequenceIOException - If an I/O error occurs when reading the sequencesInvalidSequenceException - If the sequences are not validclass CharSequence,
class CharSequenceprotected void unloadSequencesInternal()
Frees pointers to loaded sequences and the dynamic programming matrix so that their data can be garbage collected.
protected PairwiseAlignment computePairwiseAlignment()
Builds an optimal local alignment between the loaded sequences after computing the dynamic programming matrix. It calls the buildOptimalAlignment method after the computeMatrix method computes the dynamic programming matrix.
IncompatibleScoringSchemeException - If the scoring scheme is not compatible with the loaded sequences.#computeMatrix,
#buildOptimalAlignmentprotected void computeMatrix()
Computes the dynamic programming matrix.
IncompatibleScoringSchemeException - If the scoring scheme is not compatible with the loaded sequences.protected PairwiseAlignment buildOptimalAlignment()
Builds an optimal local alignment between the loaded sequences. Before it is executed, the dynamic programming matrix must already have been computed by the computeMatrix method.
IncompatibleScoringSchemeException - If the scoring scheme is not compatible with the loaded sequences.#computeMatrixprotected int computeScore()
Computes the score of the best local alignment between the two sequences using the scoring scheme previously set. This method calculates the similarity value only (doesn't build the whole matrix so the alignment cannot be recovered, however it has the advantage of requiring O(n) space only).
IncompatibleScoringSchemeException - If the scoring scheme is not compatible with the loaded sequences.