Now basecalls an entire read (both ends of the pair, barcode... everything) at once. After, RawRead and FourProbRead can be asked to return a specified subset (corresponding to the ranges specified for each end of the read.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@754 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
kiran
parent
e293d65ede
commit
6c5fbb988b
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@ -18,43 +18,37 @@ import java.util.ArrayList;
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*/
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public class BasecallingReadModel {
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private BasecallingBaseModel[] basemodels = null;
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private boolean correctForContext = false;
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private boolean correctForContext = true;
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/**
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* Constructor for BasecallingReadModel.
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*
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* @param readLength the length of the read that this model will support
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*/
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public BasecallingReadModel(int readLength, boolean correctForContext) {
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this.correctForContext = correctForContext;
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public BasecallingReadModel(int readLength) {
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initialize(readLength);
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}
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public BasecallingReadModel(ArrayList<RawRead> trainingData) {
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initialize(trainingData.get(0).getReadLength());
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train(trainingData);
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}
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public void initialize(int readLength) {
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basemodels = new BasecallingBaseModel[readLength];
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for (int cycle = 0; cycle < readLength; cycle++) {
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basemodels[cycle] = new BasecallingBaseModel(cycle == 0 ? false : correctForContext);
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basemodels[cycle] = new BasecallingBaseModel(cycle != 0 && correctForContext);
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}
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}
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public void train(BasecallingTrainingSet trainingSet) {
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ArrayList<RawRead> trainingData = trainingSet.getTrainingData();
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for (int readIndex = 0; readIndex < trainingData.size(); readIndex++) {
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RawRead read = trainingData.get(readIndex);
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public void train(ArrayList<RawRead> trainingData) {
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//for (int readIndex = 0; readIndex < trainingData.size(); readIndex++) {
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for ( RawRead read : trainingData ) {
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addMeanPoints(read);
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}
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for (int readIndex = 0; readIndex < trainingData.size(); readIndex++) {
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RawRead read = trainingData.get(readIndex);
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for ( RawRead read : trainingData ) {
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addCovariancePoints(read);
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}
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}
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/**
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* Add a single training point to the model means.
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*
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* @param cycle the cycle for which this point should be added
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* @param fourintensity the four intensities of the current base
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*/
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public void addMeanPoint(int cycle, double[][] probMatrix, double[] fourintensity) {
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basemodels[cycle].addMeanPoint(probMatrix, fourintensity);
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}
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@ -80,12 +74,6 @@ public class BasecallingReadModel {
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}
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}
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/**
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* Add a single training point to the model covariances.
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*
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* @param cycle the cycle for which this point should be added
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* @param fourintensity the four intensities of the current base
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*/
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public void addCovariancePoint(int cycle, double[][] probMatrix, double[] fourintensity) {
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basemodels[cycle].addCovariancePoint(probMatrix, fourintensity);
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}
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@ -111,25 +99,10 @@ public class BasecallingReadModel {
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}
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}
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/**
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* Compute the likelihood matrix for a given cycle.
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*
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* @param cycle the cycle number for the current base
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* @param fourintensity the four intensities for the current cycle's base
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* @return 4x4 matrix of likelihoods
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*/
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public double[][] computeLikelihoods(int cycle, double[] fourintensity) {
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return basemodels[cycle].computeLikelihoods(cycle, fourintensity);
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}
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/**
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* Compute the probability distribution for the base at a given cycle.
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*
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* @param cycle the cycle number for the current base
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* @param fourintensity the four intensities for the current cycle's base
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* @return an instance of FourProb, which encodes a base hypothesis, its probability,
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* and the ranking among the other hypotheses
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*/
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public FourProb computeProbabilities(int cycle, char basePrev, byte qualPrev, double[] fourintensity) {
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double[][] likes = computeLikelihoods(cycle, fourintensity);
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@ -157,9 +130,7 @@ public class BasecallingReadModel {
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}
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}
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FourProb fp = new FourProb(likes);
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return fp;
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return new FourProb(likes);
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}
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public FourProbRead call(RawRead read) {
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@ -174,7 +145,6 @@ public class BasecallingReadModel {
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fourIntensity[channel] = (double) read.getIntensities()[cycle][channel];
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}
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//fps[cycle] = computeProbabilities(cycle, basePrev, qualPrev, fourIntensity);
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fpr.add(cycle, computeProbabilities(cycle, basePrev, qualPrev, fourIntensity));
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}
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@ -182,15 +152,6 @@ public class BasecallingReadModel {
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return fpr;
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}
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/**
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* Returns the base probability matrix
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*
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* @param cycle the cycle of the base
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* @param basePrev the previous base
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* @param baseCur the current base
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* @param probCur the probability of the current base
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* @return the base probability matrix (1x4 if no contextual correction, 4x4 otherwise)
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*/
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public double[][] getBaseProbabilityMatrix(int cycle, char basePrev, char baseCur, double probCur) {
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double[][] dist = new double[(correctForContext && cycle > 0) ? 4 : 1][4];
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@ -208,11 +169,6 @@ public class BasecallingReadModel {
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return dist;
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}
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/**
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* Writes model parameters to a file per cycle.
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*
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* @param dir the directory where the parameters should be written
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*/
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public void write(File dir) {
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for (int cycle = 0; cycle < basemodels.length; cycle++) {
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File outparam = new File(dir.getPath() + "/param." + cycle + ".r");
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@ -18,6 +18,23 @@ public class FourProbRead extends ArrayList<FourProb> {
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super(initialCapacity);
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}
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/**
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* Returns a subset of the FourProbRead.
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*
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* @param cycleStart the starting cycle (0-based, inclusive)
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* @param cycleStop the ending cycle (0-based, inclusive)
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* @return
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*/
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public FourProbRead getSubset(int cycleStart, int cycleStop) {
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FourProbRead subset = new FourProbRead(cycleStop - cycleStart + 1);
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for (int cycle = cycleStart, offset = 0; cycle <= cycleStop; cycle++, offset++) {
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subset.add(offset, this.get(cycle));
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}
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return subset;
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}
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/**
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* Get the read sequence at a specified rank
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*
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@ -13,12 +13,10 @@ import java.util.Iterator;
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import java.util.regex.Matcher;
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import java.util.regex.Pattern;
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public class IlluminaParser implements Iterator<RawRead>, Iterable<RawRead>, Closeable {
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public class IlluminaParser implements Closeable {
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private File bustardDir;
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private File firecrestDir;
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private int lane;
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private int cycleBegin;
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private int cycleEnd;
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private File[] intfiles;
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private File[] seqfiles;
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@ -26,26 +24,21 @@ public class IlluminaParser implements Iterator<RawRead>, Iterable<RawRead>, Clo
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private int currentTileIndex;
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private PasteParser currentTileParser;
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private String[][] currentParseResult;
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private boolean iterating = false;
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public IlluminaParser(File bustardDir, int lane, int cycleBegin, int cycleEnd) {
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public IlluminaParser(File bustardDir, int lane) {
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this.bustardDir = bustardDir;
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this.firecrestDir = bustardDir.getParentFile();
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this.lane = lane;
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this.cycleBegin = cycleBegin;
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this.cycleEnd = cycleEnd;
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initializeParser();
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}
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public IlluminaParser(File bustardDir, File firecrestDir, int lane, int cycleBegin, int cycleEnd) {
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public IlluminaParser(File bustardDir, File firecrestDir, int lane) {
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this.bustardDir = bustardDir;
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this.firecrestDir = firecrestDir;
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this.lane = lane;
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this.cycleBegin = cycleBegin;
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this.cycleEnd = cycleEnd;
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initializeParser();
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}
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@ -67,10 +60,9 @@ public class IlluminaParser implements Iterator<RawRead>, Iterable<RawRead>, Clo
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Arrays.sort(seqfiles, getTileSortingComparator());
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Arrays.sort(prbfiles, getTileSortingComparator());
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iterator();
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seekToTile(1);
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// Todo: put some more consistency checks here
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}
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private FilenameFilter getFilenameFilter(final String suffix) {
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@ -129,30 +121,33 @@ public class IlluminaParser implements Iterator<RawRead>, Iterable<RawRead>, Clo
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return (currentTileParser.hasNext() || seekToTile(currentTileIndex + 1));
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}
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public RawRead next() {
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public boolean next() {
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if (hasNext()) {
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return new RawRead(currentTileParser.next(), cycleBegin, cycleEnd);
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currentParseResult = currentTileParser.next();
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return true;
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}
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return null;
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return false;
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}
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public String[][] getCurrentParseResult() {
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return currentParseResult;
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}
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public void remove() {
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throw new UnsupportedOperationException("IlluminaParser.remove() method is not supported.");
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}
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public Iterator<RawRead> iterator() {
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if (iterating) {
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throw new IllegalStateException("IlluminaParser.iterator() method can only be called once, before the first call to IlluminaParser.hasNext()");
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}
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seekToTile(1);
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iterating = true;
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return this;
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}
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public void close() {
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currentTileParser.close();
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}
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public RawRead getRawRead() {
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return getSubset(0, currentParseResult[1][4].length() - 1);
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}
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public RawRead getSubset(int cycleStart, int cycleStop) {
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return new RawRead(currentParseResult, cycleStart, cycleStop);
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}
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}
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@ -29,12 +29,12 @@ public class RawRead {
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x = Short.valueOf(pastedReadString[0][2]);
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y = Short.valueOf(pastedReadString[0][3]);
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sequence = pastedReadString[1][4].substring(cycleBegin, cycleEnd).getBytes();
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sequence = pastedReadString[1][4].substring(cycleBegin, cycleEnd + 1).getBytes();
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quals = new byte[sequence.length];
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intensities = new short[sequence.length][4];
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for (int cycle = 0; cycle < sequence.length; cycle++) {
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for (int cycle = cycleBegin, offset = 0; cycle <= cycleEnd; cycle++, offset++) {
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byte maxQual = -50;
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for (int fullReadIndex = 4*cycle; fullReadIndex < 4*cycle + 4; fullReadIndex++) {
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@ -43,13 +43,13 @@ public class RawRead {
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if (qual > maxQual) { maxQual = qual; }
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}
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quals[cycle] = maxQual >= 0 ? maxQual : 0;
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quals[offset] = maxQual >= 0 ? maxQual : 0;
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for (int fullReadIndex = 4*cycle + 4, channel = 0; fullReadIndex < 4*cycle + 8; fullReadIndex++, channel++) {
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double doubleChannelIntensity = Double.valueOf(pastedReadString[0][fullReadIndex]);
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short shortChannelIntensity = (short) doubleChannelIntensity;
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intensities[cycle][channel] = shortChannelIntensity;
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intensities[offset][channel] = shortChannelIntensity;
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}
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}
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}
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