package picard.analysis.directed;

import htsjdk.samtools.*;
import htsjdk.samtools.metrics.MetricsFile;
import htsjdk.samtools.util.CoordMath;
import htsjdk.samtools.util.Histogram;
import htsjdk.samtools.util.Interval;
import htsjdk.samtools.util.IntervalList;
import htsjdk.samtools.util.Log;
import htsjdk.samtools.util.OverlapDetector;
import htsjdk.samtools.util.SequenceUtil;
import picard.PicardException;
import picard.analysis.MetricAccumulationLevel;
import picard.analysis.RnaSeqMetrics;
import picard.annotation.Gene;
import picard.annotation.LocusFunction;
import picard.metrics.PerUnitMetricCollector;
import picard.metrics.SAMRecordMultiLevelCollector;
import picard.util.MathUtil;

import java.io.File;
import java.util.Arrays;
import java.util.Collection;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;

public class RnaSeqMetricsCollector extends SAMRecordMultiLevelCollector<RnaSeqMetrics, Integer> {
    public enum StrandSpecificity {NONE, FIRST_READ_TRANSCRIPTION_STRAND, SECOND_READ_TRANSCRIPTION_STRAND}

    private final int minimumLength;
    private final StrandSpecificity strandSpecificity;
    private final double rrnaFragmentPercentage;
    protected final Long ribosomalInitialValue;
    private final int endBiasBases;
    public static final int defaultEndBiasBases = 100;

    final private Set<Integer> ignoredSequenceIndices;

    private final OverlapDetector<Gene> geneOverlapDetector;
    private final OverlapDetector<Interval> ribosomalSequenceOverlapDetector;
    private final boolean collectCoverageStatistics;

    public RnaSeqMetricsCollector(final Set<MetricAccumulationLevel> accumulationLevels, final List<SAMReadGroupRecord> samRgRecords,
                                  final Long ribosomalBasesInitialValue, OverlapDetector<Gene> geneOverlapDetector, OverlapDetector<Interval> ribosomalSequenceOverlapDetector,
                                  final HashSet<Integer> ignoredSequenceIndices, final int minimumLength, final StrandSpecificity strandSpecificity,
                                  final double rrnaFragmentPercentage, boolean collectCoverageStatistics, final int endBiasBases) {
        this.ribosomalInitialValue  = ribosomalBasesInitialValue;
        this.ignoredSequenceIndices = ignoredSequenceIndices;
        this.geneOverlapDetector    = geneOverlapDetector;
        this.ribosomalSequenceOverlapDetector = ribosomalSequenceOverlapDetector;
        this.minimumLength          = minimumLength;
        this.strandSpecificity      = strandSpecificity;
        this.rrnaFragmentPercentage = rrnaFragmentPercentage;
        this.collectCoverageStatistics = collectCoverageStatistics;
        this.endBiasBases        = endBiasBases;
        setup(accumulationLevels, samRgRecords);
    }

    public RnaSeqMetricsCollector(final Set<MetricAccumulationLevel> accumulationLevels, final List<SAMReadGroupRecord> samRgRecords,
                                  final Long ribosomalBasesInitialValue, OverlapDetector<Gene> geneOverlapDetector, OverlapDetector<Interval> ribosomalSequenceOverlapDetector,
                                  final HashSet<Integer> ignoredSequenceIndices, final int minimumLength, final StrandSpecificity strandSpecificity,
                                  final double rrnaFragmentPercentage, boolean collectCoverageStatistics) {
        this(accumulationLevels, samRgRecords, ribosomalBasesInitialValue, geneOverlapDetector, ribosomalSequenceOverlapDetector, ignoredSequenceIndices, minimumLength, strandSpecificity, rrnaFragmentPercentage, collectCoverageStatistics, defaultEndBiasBases);
    }

    @Override
    protected PerUnitMetricCollector<RnaSeqMetrics, Integer, SAMRecord> makeChildCollector(final String sample, final String library, final String readGroup) {
        return new PerUnitRnaSeqMetricsCollector(sample, library, readGroup, ribosomalInitialValue);
    }

    public static OverlapDetector<Interval> makeOverlapDetector(final File samFile, final SAMFileHeader header, final File ribosomalIntervalsFile, final Log log) {

        final OverlapDetector<Interval> ribosomalSequenceOverlapDetector = new OverlapDetector<Interval>(0, 0);
        if (ribosomalIntervalsFile != null) {

            final IntervalList ribosomalIntervals = IntervalList.fromFile(ribosomalIntervalsFile);
            if (ribosomalIntervals.size() == 0) {
                log.warn("The RIBOSOMAL_INTERVALS file, " + ribosomalIntervalsFile.getAbsolutePath() + " does not contain intervals");
            }
            try {
                SequenceUtil.assertSequenceDictionariesEqual(header.getSequenceDictionary(), ribosomalIntervals.getHeader().getSequenceDictionary());
            } catch (SequenceUtil.SequenceListsDifferException e) {
                throw new PicardException("Sequence dictionaries differ in " + samFile.getAbsolutePath() + " and " + ribosomalIntervalsFile.getAbsolutePath(),
                        e);
            }
            final IntervalList uniquedRibosomalIntervals = ribosomalIntervals.uniqued();
            final List<Interval> intervals = uniquedRibosomalIntervals.getIntervals();
            ribosomalSequenceOverlapDetector.addAll(intervals, intervals);
        }
        return ribosomalSequenceOverlapDetector;
    }

    public static HashSet<Integer> makeIgnoredSequenceIndicesSet(final SAMFileHeader header, final Set<String> ignoredSequence) {
        final HashSet<Integer> ignoredSequenceIndices = new HashSet<Integer>();
        for (final String sequenceName: ignoredSequence) {
            final SAMSequenceRecord sequenceRecord = header.getSequence(sequenceName);
            if (sequenceRecord == null) {
                throw new PicardException("Unrecognized sequence " + sequenceName + " passed as argument to IGNORE_SEQUENCE");
            }
            ignoredSequenceIndices.add(sequenceRecord.getSequenceIndex());
        }
        return ignoredSequenceIndices;
    }

    protected class PerUnitRnaSeqMetricsCollector implements PerUnitMetricCollector<RnaSeqMetrics, Integer, SAMRecord> {

        protected final RnaSeqMetrics metrics;

        private final Map<Gene.Transcript, int[]> coverageByTranscript = new HashMap<Gene.Transcript, int[]>();

        /**
         * Derived classes that need to capture some additional metrics can use this ctor to supply a metrics instance
         * that is a subclass of RnaSeqMetrics.
         */
        protected PerUnitRnaSeqMetricsCollector(final RnaSeqMetrics metrics,
                                                final String sample,
                                                final String library,
                                                final String readGroup,
                                                final Long ribosomalBasesInitialValue) {
            this.metrics = metrics;
            this.metrics.SAMPLE = sample;
            this.metrics.LIBRARY = library;
            this.metrics.READ_GROUP = readGroup;
            this.metrics.RIBOSOMAL_BASES = ribosomalBasesInitialValue;

        }

        public PerUnitRnaSeqMetricsCollector(final String sample,
                                             final String library,
                                             final String readGroup,
                                             final Long ribosomalBasesInitialValue) {
            this(new RnaSeqMetrics(), sample, library, readGroup, ribosomalBasesInitialValue);
        }

        public void acceptRecord(SAMRecord rec) {
            // NB: for read count metrics, do not include supplementary records, but for base count metrics, do include supplementary records.

            // Filter out some reads, and collect the total number of PF bases
            if (rec.getReadFailsVendorQualityCheckFlag()) return;

            // NB: we count unmapped reads here
            if (!rec.getNotPrimaryAlignmentFlag()) this.metrics.PF_BASES += rec.getReadLength();

            // NB: we count secondary mapped reads here
            if (!rec.getReadUnmappedFlag() && !rec.getNotPrimaryAlignmentFlag() && ignoredSequenceIndices.contains(rec.getReferenceIndex())) {
                ++this.metrics.IGNORED_READS;
                return;
            }

            // We can now ignore secondary or unmapped reads
            if (rec.getNotPrimaryAlignmentFlag() || rec.getReadUnmappedFlag()) return;

            // Grab information about the alignment and overlapping genes etc.
            final Interval readInterval = new Interval(rec.getReferenceName(), rec.getAlignmentStart(), rec.getAlignmentEnd());

            // Attempt to get an interval for the entire fragment (if paired read) else just use the read itself.
            // If paired read is chimeric or has one end unmapped, don't create an interval.
            final Interval fragmentInterval;
            if (!rec.getReadPairedFlag()) {
                fragmentInterval = readInterval;
            } else if (rec.getMateUnmappedFlag() || !rec.getReferenceIndex().equals(rec.getMateReferenceIndex())) {
                fragmentInterval = null;
            } else {
                final int fragmentStart = Math.min(rec.getAlignmentStart(), rec.getMateAlignmentStart());
                final int fragmentEnd = CoordMath.getEnd(fragmentStart, Math.abs(rec.getInferredInsertSize()));
                fragmentInterval = new Interval(rec.getReferenceName(), fragmentStart, fragmentEnd);
            }
            if (fragmentInterval != null) {
                final Collection<Interval> overlappingRibosomalIntervals = ribosomalSequenceOverlapDetector.getOverlaps(fragmentInterval);
                int intersectionLength = 0;
                for (final Interval overlappingInterval : overlappingRibosomalIntervals) {
                    final int thisIntersectionLength = overlappingInterval.getIntersectionLength(fragmentInterval);
                    intersectionLength = Math.max(intersectionLength, thisIntersectionLength);
                }
                if (intersectionLength/(double)fragmentInterval.length() >= rrnaFragmentPercentage) {
                    // Assume entire read is ribosomal.
                    metrics.RIBOSOMAL_BASES += getNumAlignedBases(rec);
                    metrics.PF_ALIGNED_BASES += getNumAlignedBases(rec);
                    return;
                }
            }

            final Collection<Gene> overlappingGenes                  = geneOverlapDetector.getOverlaps(readInterval);
            final List<AlignmentBlock> alignmentBlocks               = rec.getAlignmentBlocks();
            boolean overlapsExon = false;

            for (final AlignmentBlock alignmentBlock : alignmentBlocks) {
                // Get functional class for each position in the alignment block.
                final LocusFunction[] locusFunctions = new LocusFunction[alignmentBlock.getLength()];

                // By default, if base does not overlap with rRNA or gene, it is intergenic.
                Arrays.fill(locusFunctions, 0, locusFunctions.length, LocusFunction.INTERGENIC);

                for (final Gene gene : overlappingGenes) {
                    for (final Gene.Transcript transcript : gene) {
                        transcript.assignLocusFunctionForRange(alignmentBlock.getReferenceStart(), locusFunctions);
                        // if you want to gather coverage statistics, this variable should be true.
                        // added for cases with many units [samples/read groups] which overwhelm memory.                
                        // Add coverage to our coverage counter for this transcript
                        if (collectCoverageStatistics) {
                            int[] coverage = this.coverageByTranscript.get(transcript);
                            if (coverage == null) {
                                coverage = new int[transcript.length()];
                                this.coverageByTranscript.put(transcript, coverage);
                            }
                            transcript.addCoverageCounts(alignmentBlock.getReferenceStart(),
                                    CoordMath.getEnd(alignmentBlock.getReferenceStart(), alignmentBlock.getLength()),
                                    coverage);
                        }

                    }
                }

                // Tally the function of each base in the alignment block.
                for (final LocusFunction locusFunction : locusFunctions) {
                    ++metrics.PF_ALIGNED_BASES;
                    switch (locusFunction) {
                        case INTERGENIC:
                            ++metrics.INTERGENIC_BASES;
                            break;
                        case INTRONIC:
                            ++metrics.INTRONIC_BASES;
                            break;
                        case UTR:
                            ++metrics.UTR_BASES;
                            overlapsExon = true;
                            break;
                        case CODING:
                            ++metrics.CODING_BASES;
                            overlapsExon = true;
                            break;
                        case RIBOSOMAL:
                            ++metrics.RIBOSOMAL_BASES;
                            break;
                    }
                }
            }

            // Strand-specificity is tallied on read basis rather than base at a time.  A read that aligns to more than one
            // gene is not counted.
            if (!rec.getSupplementaryAlignmentFlag() && overlapsExon && overlappingGenes.size() == 1) {
                final Gene gene                           = overlappingGenes.iterator().next();
                final boolean negativeTranscriptionStrand = gene.isNegativeStrand();
                final boolean readOneOrUnpaired           = !rec.getReadPairedFlag() || rec.getFirstOfPairFlag();
                final boolean negativeReadStrand          = rec.getReadNegativeStrandFlag();
                if (strandSpecificity != StrandSpecificity.NONE) {
                    final boolean readAndTranscriptStrandsAgree = negativeReadStrand == negativeTranscriptionStrand;
                    final boolean firstReadExpectedToAgree      = strandSpecificity == StrandSpecificity.FIRST_READ_TRANSCRIPTION_STRAND;
                    final boolean thisReadExpectedToAgree       = readOneOrUnpaired == firstReadExpectedToAgree;
                    // If the read strand is the same as the strand of the transcript, and the end is the one that is supposed to agree,
                    // then the strand specificity for this read is correct.
                    // -- OR --
                    // If the read strand is not the same as the strand of the transcript, and the end is not the one that is supposed
                    // to agree, then the strand specificity for this read is correct.
                    if (readAndTranscriptStrandsAgree == thisReadExpectedToAgree) {
                        ++metrics.CORRECT_STRAND_READS;
                    } else {
                        ++metrics.INCORRECT_STRAND_READS;
                    }
                }

                // Count templates only once rather than individual reads/records
                if (readOneOrUnpaired) {
                    // Check that for paired end reads they are in the proper orientation (FR) and that the entire
                    // template is enclosed in the gene.
                    final boolean properOrientation;
                    final int leftMostAlignedBase, rightMostAlignedBase;
                    if (rec.getReadPairedFlag()) {
                        if (rec.getMateUnmappedFlag()) {
                            properOrientation   = false; // mate is unmapped!
                            leftMostAlignedBase = rightMostAlignedBase = 0;
                        } else {
                            // Get the alignment length of the mate.  Approximate it with the current read length if no
                            // mate cigar is found.
                            final Cigar mateCigar         = SAMUtils.getMateCigar(rec);
                            final int mateReferenceLength = (mateCigar == null) ? rec.getReadLength() : mateCigar.getReferenceLength();
                            final int mateAlignmentEnd    =  CoordMath.getEnd(rec.getMateAlignmentStart(), mateReferenceLength);
                            properOrientation    = rec.getReferenceName().equals(rec.getMateReferenceName()) && 
                                    SamPairUtil.getPairOrientation(rec) == SamPairUtil.PairOrientation.FR;
                            leftMostAlignedBase  = Math.min(rec.getAlignmentStart(), rec.getMateAlignmentStart());
                            rightMostAlignedBase = Math.max(rec.getAlignmentEnd(), mateAlignmentEnd);
                        }
                    } else {
                        properOrientation    = true; // can only be false for paired end reads
                        leftMostAlignedBase  = rec.getAlignmentStart();
                        rightMostAlignedBase = rec.getAlignmentEnd();
                    }

                    if (properOrientation && CoordMath.encloses(gene.getStart(), gene.getEnd(), leftMostAlignedBase, rightMostAlignedBase)) {
                        if (negativeReadStrand == negativeTranscriptionStrand) {
                            ++metrics.NUM_R1_TRANSCRIPT_STRAND_READS;
                        } else {
                            ++metrics.NUM_R2_TRANSCRIPT_STRAND_READS;
                        }
                    } else {
                        ++metrics.NUM_UNEXPLAINED_READS;
                    }
                }
            }
        }

        protected int getNumAlignedBases(SAMRecord rec) {
            int numAlignedBases = 0;
            for (final AlignmentBlock alignmentBlock : rec.getAlignmentBlocks()) {
                numAlignedBases += alignmentBlock.getLength();
            }
            return numAlignedBases;
        }

        public void finish() {
            if (metrics.PF_ALIGNED_BASES > 0) {
                if (metrics.RIBOSOMAL_BASES != null) {
                    metrics.PCT_RIBOSOMAL_BASES =  metrics.RIBOSOMAL_BASES  / (double) metrics.PF_ALIGNED_BASES;
                }
                metrics.PCT_CODING_BASES =     metrics.CODING_BASES     / (double) metrics.PF_ALIGNED_BASES;
                metrics.PCT_UTR_BASES =        metrics.UTR_BASES        / (double) metrics.PF_ALIGNED_BASES;
                metrics.PCT_INTRONIC_BASES =   metrics.INTRONIC_BASES   / (double) metrics.PF_ALIGNED_BASES;
                metrics.PCT_INTERGENIC_BASES = metrics.INTERGENIC_BASES / (double) metrics.PF_ALIGNED_BASES;
                metrics.PCT_MRNA_BASES =       metrics.PCT_CODING_BASES + metrics.PCT_UTR_BASES;
                metrics.PCT_USABLE_BASES =     (metrics.CODING_BASES + metrics.UTR_BASES) / (double) metrics.PF_BASES;
            }

            if (metrics.CORRECT_STRAND_READS > 0 || metrics.INCORRECT_STRAND_READS > 0) {
                metrics.PCT_CORRECT_STRAND_READS = metrics.CORRECT_STRAND_READS/(double)(metrics.CORRECT_STRAND_READS + metrics.INCORRECT_STRAND_READS);
            }

            final long readsExamined = metrics.NUM_R1_TRANSCRIPT_STRAND_READS + metrics.NUM_R2_TRANSCRIPT_STRAND_READS;
            if (0 < readsExamined) {
                metrics.PCT_R1_TRANSCRIPT_STRAND_READS = metrics.NUM_R1_TRANSCRIPT_STRAND_READS / (double) readsExamined;
                metrics.PCT_R2_TRANSCRIPT_STRAND_READS = metrics.NUM_R2_TRANSCRIPT_STRAND_READS / (double) readsExamined;
            }
        }

        @Override
        public void addMetricsToFile(final MetricsFile<RnaSeqMetrics, Integer> file) {
            // Compute metrics based on coverage of top 1000 genes
            final Histogram<Integer> normalizedCovByPos = computeCoverageMetrics();
            file.addMetric(metrics);
            file.addHistogram(normalizedCovByPos);
        }

        /**
         * Computes a set of coverage based metrics on the mostly highly expressed genes' most highly
         * expressed transcripts.
         */
        private Histogram<Integer> computeCoverageMetrics() {
            final Histogram<Double> cvs = new Histogram<Double>();
            final Histogram<Double> fivePrimeSkews = new Histogram<Double>();
            final Histogram<Double> threePrimeSkews = new Histogram<Double>();
            final Histogram<Double> gapBasesPerKb = new Histogram<Double>();
            final Histogram<Double> fiveToThreeSkews = new Histogram<Double>();
            String prefix = null;
            if (this.metrics.READ_GROUP != null) {
                prefix = this.metrics.READ_GROUP + ".";
            }
            else if (this.metrics.LIBRARY != null) {
                prefix = this.metrics.LIBRARY + ".";
            }
            else if (this.metrics.SAMPLE != null) {
                prefix = this.metrics.SAMPLE + ".";
            }
            else {
                prefix = "All_Reads.";
            }

            final Histogram<Integer> normalizedCoverageByNormalizedPosition = new Histogram<Integer>("normalized_position", prefix + "normalized_coverage");

            final Map<Gene.Transcript,int[]> transcripts = pickTranscripts(coverageByTranscript);
            final double transcriptCount = transcripts.size();

            for (final Map.Entry<Gene.Transcript,int[]> entry : transcripts.entrySet()) {
                final Gene.Transcript tx = entry.getKey();
                final double[] coverage;
                {
                    final double[] tmp = MathUtil.promote(entry.getValue());
                    if (tx.getGene().isPositiveStrand())  coverage = tmp;
                    else coverage = copyAndReverse(tmp);
                }
                final double mean = MathUtil.mean(coverage);

                // Calculate the CV of coverage for this tx
                final double stdev = MathUtil.stddev(coverage, mean);
                final double cv    = stdev / mean;
                cvs.increment(cv);

                // Calculate the 5' and 3' biases
                {
                    final double fivePrimeCoverage = MathUtil.mean(coverage, 0, endBiasBases);
                    final double threePrimeCoverage = MathUtil.mean(coverage, coverage.length - endBiasBases, coverage.length);

                    fivePrimeSkews.increment(fivePrimeCoverage / mean);
                    threePrimeSkews.increment(threePrimeCoverage / mean);
                    fiveToThreeSkews.increment(MathUtil.divide(fivePrimeCoverage, threePrimeCoverage));
                }

                // Calculate normalized coverage vs. normalized position
                {
                    final int lastIndex = coverage.length - 1;

                    for (int percent=0; percent<=100; ++percent) {
                        final double p = percent / 100d;
                        final int start  = (int) Math.max(0,         lastIndex * (p-0.005));
                        final int end    = (int) Math.min(lastIndex, lastIndex * (p+0.005));
                        final int length = end - start + 1;

                        double sum = 0;
                        for (int i=start; i<=end; ++i) sum += coverage[i];
                        final double normalized = (sum / length) / mean;
                        normalizedCoverageByNormalizedPosition.increment(percent, normalized / transcriptCount);
                    }
                }

                // Calculate gap bases per kilobase
                //            {
                //                int gapBases = 0;
                //                final double minCoverage = mean * 0.1;
                //                for (int i=0; i<coverage.length; ++i) {
                //                    if (coverage[i] < minCoverage) ++gapBases;
                //                }
                //                gapBasesPerKb.increment(gapBases / (coverage.length / 1000d));
                //            }
            }

            this.metrics.MEDIAN_CV_COVERAGE = cvs.getMedian();
            this.metrics.MEDIAN_5PRIME_BIAS = fivePrimeSkews.getMedian();
            this.metrics.MEDIAN_3PRIME_BIAS = threePrimeSkews.getMedian();
            this.metrics.MEDIAN_5PRIME_TO_3PRIME_BIAS = fiveToThreeSkews.getMedian();

            return normalizedCoverageByNormalizedPosition;
        }

        /** Little method to copy an array and reverse it at the same time. */
        private double[] copyAndReverse(final double[] in) {
            final double[] out = new double[in.length];
            for (int i=0, j=in.length-1; i<in.length; ++i, --j) out[j] = in[i];
            return out;
        }

        /**
         * Derived class may override this method in order to return a different set of genes for
         * picking transcripts for computing coverage, or to cache the value returned by geneOverlapDetector.getAll(),
         * because it can be expensive to compute repeatedly.
         */
        protected Set<Gene> getGenesForPickTranscripts() {
            return geneOverlapDetector.getAll();
        }

        /** Picks the set of transcripts on which the coverage metrics are to be calculated. */
        public Map<Gene.Transcript, int[]> pickTranscripts(final Map<Gene.Transcript, int[]> transcriptCoverage) {
            final Set<Gene> genesWithTranscripts = getGenesWithTranscripts(transcriptCoverage.keySet());

            // Make a map of the best transcript per gene to it's mean coverage
            final Map<Gene.Transcript, Double> bestPerGene = new HashMap<Gene.Transcript, Double>();
            for (final Gene gene : genesWithTranscripts) {
                Gene.Transcript best = null;
                double bestMean = 0;

                for (final Gene.Transcript tx : gene) {
                    final int[] cov = transcriptCoverage.get(tx);

                    if (tx.length() < Math.max(minimumLength, endBiasBases)) continue;

                    final double mean = MathUtil.mean(MathUtil.promote(cov), 0, cov.length);
                    if (mean < 1d) continue;
                    if (best == null || mean > bestMean) {
                        best = tx;
                        bestMean = mean;
                    }
                }

                if (best != null) bestPerGene.put(best, bestMean);
            }

            // Find the 1000th best coverage value
            final double[] coverages = new double[bestPerGene.size()];
            int i=0;
            for (final double d : bestPerGene.values()) coverages[i++] = d;
            Arrays.sort(coverages);
            final double min = coverages.length == 0 ? 0 : coverages[Math.max(0, coverages.length - 1001)];

            // And finally build the output map
            final Map<Gene.Transcript, int[]> retval = new HashMap<Gene.Transcript, int[]>();
            for (final Map.Entry<Gene.Transcript,Double> entry : bestPerGene.entrySet()) {
                final Gene.Transcript tx = entry.getKey();
                final double coverage = entry.getValue();

                if (coverage >= min) {
                    retval.put(tx, transcriptCoverage.get(tx));
                }
            }

            return retval;
        }

        /**
         * @param transcripts
         * @return The set of genes for which the given transcripts come from, intersected with genesForPickTranscripts
         */
        private Set<Gene> getGenesWithTranscripts(Collection<Gene.Transcript> transcripts) {
            // Note that the size of transcriptCoverage may be << than size of genesForPickTranscripts, so iterating
            // over that set should be much faster.  If the size is not much smaller, it shouldn't be significantly
            // larger.
            final Set<Gene> genesWithTranscripts = new HashSet<>();
            final Set<Gene> genesForPickTranscripts = getGenesForPickTranscripts();
            for (final Gene.Transcript transcript: transcripts) {
                if (genesForPickTranscripts.contains(transcript.getGene())) {
                    genesWithTranscripts.add(transcript.getGene());
                }
            }
            return genesWithTranscripts;
        }

    }
}
