forked from bigdatagenomics/adam
/
SmithWatermanGapScoringFromFn.scala
65 lines (53 loc) · 1.75 KB
/
SmithWatermanGapScoringFromFn.scala
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/*
* Copyright (c) 2013. Regents of the University of California
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.bdgenomics.adam.algorithms.smithwaterman
abstract class SmithWatermanGapScoringFromFn(xSequence: String,
ySequence: String,
scoreFn: (Int, Int, Char, Char) => Double)
extends SmithWaterman(xSequence, ySequence) {
def buildScoringMatrix(): Array[Array[Double]] = {
val y = ySequence.length + 1
val x = xSequence.length + 1
var matrix = new Array[Array[Double]](x)
for (i <- 0 until x) {
matrix(i) = new Array[Double](y)
}
// set row/col 0 to 0
for (i <- 0 until x) {
matrix(i)(0) = 0.0
}
for (j <- 0 until y) {
matrix(0)(j) = 0.0
}
// score matrix
for (i <- 1 until x) {
for (j <- i until y) {
val m = matrix(i - 1)(j - 1) + scoreFn(i, j, xSequence(i), ySequence(j))
val d = matrix(i - 1)(j) + scoreFn(i, j, xSequence(i), '_')
val in = matrix(i)(j - 1) + scoreFn(i, j, '_', ySequence(j))
val update = (d max in) max (m max 0)
matrix(i)(j) = update
// check if new max and update
if (update > max) {
maxX = i
maxY = j
max = update
}
}
}
matrix
}
}