Added Bustard vs. Four-prob percent bases consistent output.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@710 348d0f76-0448-11de-a6fe-93d51630548a
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@ -2,6 +2,7 @@ package org.broadinstitute.sting.secondarybase;
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import org.broadinstitute.sting.utils.cmdLine.CommandLineProgram;
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import org.broadinstitute.sting.utils.cmdLine.CommandLineProgram;
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import org.broadinstitute.sting.utils.cmdLine.Argument;
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import org.broadinstitute.sting.utils.cmdLine.Argument;
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import org.broadinstitute.sting.utils.BaseUtils;
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import org.broadinstitute.sting.secondarybase.BasecallingReadModel;
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import org.broadinstitute.sting.secondarybase.BasecallingReadModel;
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import java.io.File;
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import java.io.File;
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@ -38,36 +39,59 @@ public class AnnotateSecondaryBase extends CommandLineProgram {
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if (SAM_IN == null || !SAM_IN.exists()) {
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if (SAM_IN == null || !SAM_IN.exists()) {
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// Iterate through raw Firecrest data and store the first N reads up to TRAINING_LIMIT
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// Iterate through raw Firecrest data and store the first N reads up to TRAINING_LIMIT
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System.out.println("Training from the first " + TRAINING_LIMIT + " reads in the raw data.");
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System.out.println("Loading training set from the first " + TRAINING_LIMIT + " reads in the raw data...");
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trainingSet.loadFirstNUnambiguousReadsTrainingSet();
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trainingSet.loadFirstNUnambiguousReadsTrainingSet();
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} else {
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} else {
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// Find alignments with zero mismatches and store them until we've picked up TRAINING_LIMIT alignments
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// Find alignments with zero mismatches and store them until we've picked up TRAINING_LIMIT alignments
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System.out.println("Training from the first " + TRAINING_LIMIT + " perfect reads in the aligned data.");
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System.out.println("Loading training set from the first " + TRAINING_LIMIT + " perfect reads in the aligned data...");
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trainingSet.loadPreAlignedTrainingSet(SAM_IN, REFERENCE);
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trainingSet.loadPreAlignedTrainingSet(SAM_IN, REFERENCE);
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}
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}
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// Iterate through the stored training data and add the info to the BasecallingReadModel
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// Iterate through the stored training data and add the info to the BasecallingReadModel
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System.out.println("Applying training set...");
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BasecallingReadModel model = new BasecallingReadModel(CYCLE_END - CYCLE_BEGIN + 1, CONTEXT);
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BasecallingReadModel model = new BasecallingReadModel(CYCLE_END - CYCLE_BEGIN + 1, CONTEXT);
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model.train(trainingSet);
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model.train(trainingSet);
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// Call bases and write results
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// Call bases and write results
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System.out.println("Calling bases and writing SAM file...");
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SAMFileHeader sfh = new SAMFileHeader();
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SAMFileHeader sfh = new SAMFileHeader();
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SAMFileWriter sfw = new SAMFileWriterFactory().makeSAMOrBAMWriter(sfh, false, SAM_OUT);
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SAMFileWriter sfw = new SAMFileWriterFactory().makeSAMOrBAMWriter(sfh, false, SAM_OUT);
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IlluminaParser iparser = new IlluminaParser(BUSTARD_DIR, LANE, CYCLE_BEGIN, CYCLE_END);
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IlluminaParser iparser = new IlluminaParser(BUSTARD_DIR, LANE, CYCLE_BEGIN, CYCLE_END);
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int basesConsistent = 0, basesTotal = 0;
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RawRead rr;
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RawRead rr;
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while ((rr = iparser.next()) != null) {
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while ((rr = iparser.next()) != null) {
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FourProbRead fpr = model.call(rr);
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FourProbRead fpr = model.call(rr);
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SAMRecord sr = constructSAMRecord(rr, fpr, sfh, RUN_BARCODE);
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SAMRecord sr = constructSAMRecord(rr, fpr, sfh, RUN_BARCODE);
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sfw.addAlignment(sr);
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sfw.addAlignment(sr);
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for (int cycle = 0; cycle < fpr.size(); cycle++) {
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int rawBaseIndex = BaseUtils.simpleBaseToBaseIndex((char) rr.getSequence()[cycle]);
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int fpBaseIndex = fpr.get(cycle).indexAtRank(0);
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if (rawBaseIndex >= 0 && fpBaseIndex >= 0) {
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basesTotal++;
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if (rawBaseIndex == fpBaseIndex) {
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basesConsistent++;
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}
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}
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}
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if (basesTotal % 10000 == 0 && basesTotal > 0) {
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System.out.printf("%% bases consistent: %d/%d (%4.4f)\r", basesConsistent, basesTotal, ((double) basesConsistent)/((double) basesTotal));
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}
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}
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}
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iparser.close();
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iparser.close();
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sfw.close();
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sfw.close();
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System.out.println("Done.");
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return 0;
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return 0;
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}
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}
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