Data Transformations

/**
 * Convert alignment records to coverage depth information
 * @param collapse - Whether to merge adjacent coverage records with same depth
 * @return CoverageRDD representing sequencing depth across genomic positions
 */
def toCoverage(collapse: Boolean = true): CoverageRDD

/**
 * Group paired-end reads into fragments representing complete DNA molecules
 * @return FragmentRDD containing paired reads as single fragments
 */
def toFragments(): FragmentRDD

/**
 * Mark duplicate reads based on genomic coordinates and orientation
 * Uses Picard's duplicate marking algorithm for compatibility
 * @return AlignmentRecordRDD with duplicate reads flagged
 */
def markDuplicates(): AlignmentRecordRDD

/**
 * Recalibrate base quality scores using known variant sites
 * Implements GATK-style base quality score recalibration
 * @param knownSnps - VariantRDD of known variant sites for recalibration
 * @return AlignmentRecordRDD with recalibrated quality scores
 */
def recalibrateBaseQualities(knownSnps: VariantRDD): AlignmentRecordRDD

/**
 * Realign reads around indels to improve alignment accuracy
 * @return AlignmentRecordRDD with improved alignments around indels
 */
def realignIndels(): AlignmentRecordRDD

/**
 * Sort reads by genomic coordinate for efficient processing
 * @return AlignmentRecordRDD sorted by reference position
 */
def sortReadsByReferencePosition(): AlignmentRecordRDD

/**
 * Count k-mers of specified length across all reads
 * @param kmerLength - Length of k-mers to count
 * @return RDD of k-mer sequences with their occurrence counts
 */
def countKmers(kmerLength: Int): RDD[(String, Long)]

import org.bdgenomics.adam.rdd.ADAMContext._

val alignments = sc.loadBam("input.bam")

// Generate coverage from alignments
val coverage = alignments.toCoverage(collapse = true)
coverage.saveAsWig("coverage.wig")

// Mark and remove duplicates
val deduped = alignments
  .markDuplicates()
  .transform(_.filter(!_.getDuplicateRead))

// Quality score recalibration workflow
val knownVariants = sc.loadVcf("known_sites.vcf").toVariants()
val recalibrated = alignments.recalibrateBaseQualities(knownVariants)

// K-mer analysis
val kmers21 = alignments.countKmers(21)
val topKmers = kmers21.top(100)(Ordering.by(_._2))

/**
 * Convert variants to genotype calls for population analysis
 * @return GenotypeRDD containing sample-specific genotype information
 */
def toGenotypes(): GenotypeRDD

/**
 * Convert variants to variant contexts with full VCF metadata
 * @return VariantContextRDD containing variants with header information
 */
def toVariantContexts(): VariantContextRDD

/**
 * Extract variants from genotype calls
 * @return VariantRDD containing unique variant sites
 */
def toVariants(): VariantRDD  // Available on GenotypeRDD and VariantContextRDD

// Load and transform variant data
val variantContexts = sc.loadVcf("population.vcf")

// Extract variants for analysis
val variants = variantContexts.toVariants()

// Extract genotypes for population genetics
val genotypes = variantContexts.toGenotypes()

// Filter variants by quality and convert back
val highQualityVariants = variants
  .transform(_.filter(_.getQuality > 30.0))
  .toVariantContexts()

/**
 * Collapse adjacent coverage records with identical depth values
 * @return CoverageRDD with consolidated coverage intervals
 */
def collapse(): CoverageRDD

/**
 * Convert coverage records to genomic features for analysis
 * @return FeatureRDD representing coverage intervals as features
 */
def toFeatures(): FeatureRDD

/**
 * Aggregate coverage across multiple samples at each position
 * @return CoverageRDD with combined coverage information
 */
def aggregateByPosition(): CoverageRDD

// Generate and analyze coverage
val alignments = sc.loadBam("sample.bam")
val coverage = alignments.toCoverage()

// Collapse adjacent regions with same coverage
val collapsed = coverage.collapse()

// Find regions with high coverage (>50x)
val highCoverage = coverage
  .transform(_.filter(_.count > 50.0))
  .toFeatures()

highCoverage.saveAsBed("high_coverage_regions.bed")

/**
 * Convert fragments back to individual alignment records
 * @return AlignmentRecordRDD containing separate read alignments
 */
def toAlignmentRecords(): AlignmentRecordRDD

/**
 * Mark duplicate fragments based on alignment coordinates
 * @return FragmentRDD with duplicate fragments flagged
 */
def markDuplicates(): FragmentRDD

// Work with paired-end fragments
val alignments = sc.loadBam("paired_end.bam")
val fragments = alignments.toFragments()

// Mark duplicate fragments
val dedupedFragments = fragments.markDuplicates()

// Convert back to reads for downstream processing
val cleanReads = dedupedFragments.toAlignmentRecords()

/**
 * Save alignment records as SAM/BAM/CRAM format
 * @param pathName - Output file path
 * @param asType - Output format (SAM, BAM, or CRAM)
 * @param asSingleFile - Whether to save as single file or partitioned
 */
def saveAsSam(pathName: String, 
             asType: SAMFormat = SAMFormat.SAM,
             asSingleFile: Boolean = false): Unit

/**
 * Save alignment records as FASTQ format
 * @param pathName - Output file path
 * @param outputOriginalBaseQualities - Whether to output original quality scores
 * @param asSingleFile - Whether to save as single file
 */
def saveAsFastq(pathName: String,
               outputOriginalBaseQualities: Boolean = false,
               asSingleFile: Boolean = false): Unit

/**
 * Save variants as VCF format
 * @param pathName - Output file path
 * @param stringency - Validation stringency for VCF compliance
 */
def saveAsVcf(pathName: String,
             stringency: ValidationStringency = ValidationStringency.STRICT): Unit

/**
 * Save features in various annotation formats
 */
def saveAsBed(pathName: String): Unit        // BED format
def saveAsGtf(pathName: String): Unit        // GTF format  
def saveAsGff3(pathName: String): Unit       // GFF3 format
def saveAsIntervalList(pathName: String): Unit // Picard interval list
def saveAsNarrowPeak(pathName: String): Unit  // ENCODE narrowPeak

/**
 * Save coverage as WIG format for genome browsers
 * @param pathName - Output file path
 */
def saveAsWig(pathName: String): Unit

/**
 * Save reference sequences as FASTA format
 * @param pathName - Output file path
 * @param lineWidth - Number of bases per line
 */
def saveAsFasta(pathName: String, lineWidth: Int = 60): Unit

/**
 * Generic transformation preserving genomic metadata
 * @param tFn - Function to transform the underlying RDD
 * @return Same GenomicRDD type with transformed data
 */
def transform(tFn: RDD[T] => RDD[T]): U

/**
 * Transform to different GenomicRDD type
 * @param tFn - Function to transform RDD to different type
 * @return New GenomicRDD type with appropriate metadata
 */
def transmute[X, Y <: GenomicRDD[X, Y]](tFn: RDD[T] => RDD[X]): Y

/**
 * Union multiple GenomicRDDs of the same type
 * @param rdds - Variable number of GenomicRDDs to union
 * @return Combined GenomicRDD containing all records
 */
def union(rdds: U*): U

// Complex transformation pipeline
val alignments = sc.loadBam("input.bam")

val processed = alignments
  .transform(_.filter(_.getReadMapped))           // Filter mapped reads
  .markDuplicates()                               // Mark duplicates
  .transform(_.filter(!_.getDuplicateRead))       // Remove duplicates
  .sortReadsByReferencePosition()                 // Sort by position

// Convert to different format
val coverage = processed.toCoverage()

// Union multiple samples
val sample1 = sc.loadBam("sample1.bam")
val sample2 = sc.loadBam("sample2.bam") 
val sample3 = sc.loadBam("sample3.bam")

val combined = sample1.union(sample2, sample3)

/**
 * Calculate alignment statistics for quality assessment
 * @return RDD of alignment statistics by reference contig
 */
def alignmentStatistics(): RDD[(String, AlignmentStatistics)]

/**
 * Filter reads based on quality metrics
 * @param minMappingQuality - Minimum mapping quality score
 * @param requireProperPair - Whether to require properly paired reads
 * @return AlignmentRecordRDD with filtered reads
 */
def filterByQuality(minMappingQuality: Int, requireProperPair: Boolean = false): AlignmentRecordRDD

/**
 * Calculate insert size distribution for paired-end reads
 * @return RDD of insert sizes with their frequencies
 */
def insertSizeDistribution(): RDD[(Int, Long)]

// Quality control workflow
val alignments = sc.loadBam("sample.bam")

// Filter by mapping quality
val highQuality = alignments.filterByQuality(minMappingQuality = 30)

// Calculate statistics
val stats = alignments.alignmentStatistics()
stats.collect().foreach { case (contig, stat) =>
  println(s"$contig: ${stat.mappedReads} mapped reads")
}

// Analyze insert sizes
val insertSizes = alignments.insertSizeDistribution()
val medianInsertSize = insertSizes.map(_._1).median()