Genomic Algorithms

/**
 * Base trait for consensus sequence generation algorithms
 * Provides interface for generating consensus from genomic data
 */
trait ConsensusGenerator {
  /**
   * Generate consensus sequence from aligned reads
   * @param reads - Iterable collection of alignment records
   * @return Consensus object containing consensus sequence and quality information
   */
  def findConsensus(reads: Iterable[AlignmentRecord]): Consensus
}

/**
 * Generate consensus sequences from aligned reads using coverage-based calling
 * Implements majority-rule consensus with configurable thresholds
 */
class ConsensusGeneratorFromReads(
  consensusFrequencyThreshold: Double = 0.4,
  minReadsAtPosition: Int = 5
) extends ConsensusGenerator {
  
  def findConsensus(reads: Iterable[AlignmentRecord]): Consensus
}

/**
 * Generate consensus incorporating known variant sites
 * Uses known variants to guide consensus calling in ambiguous regions
 */
class ConsensusGeneratorFromKnowns(
  variants: RDD[Variant],
  minAlleleFraction: Double = 0.25
) extends ConsensusGenerator {
  
  def findConsensus(reads: Iterable[AlignmentRecord]): Consensus
}

/**
 * Union consensus generator combining multiple consensus strategies
 * Merges results from multiple consensus generators with conflict resolution
 */
class UnionConsensusGenerator(
  generators: List[ConsensusGenerator]
) extends ConsensusGenerator {
  
  def findConsensus(reads: Iterable[AlignmentRecord]): Consensus
}

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

// Basic consensus from reads
val generator = new ConsensusGeneratorFromReads(
  consensusFrequencyThreshold = 0.6,
  minReadsAtPosition = 10
)

val alignments = sc.loadBam("sample.bam")
val readsAtPosition = alignments.rdd.filter(_.getContigName == "chr1")
val consensus = generator.findConsensus(readsAtPosition.collect())

// Consensus incorporating known variants
val knownVariants = sc.loadVcf("dbsnp.vcf").toVariants().rdd
val variantGenerator = new ConsensusGeneratorFromKnowns(
  knownVariants,
  minAlleleFraction = 0.3
)

// Combined consensus strategy
val unionGenerator = new UnionConsensusGenerator(
  List(generator, variantGenerator)
)

/**
 * Smith-Waterman local sequence alignment algorithm
 * Provides optimal local alignment between two sequences
 */
object SmithWaterman {
  /**
   * Perform local sequence alignment between reference and query sequences
   * @param reference - Reference sequence string
   * @param read - Query sequence string  
   * @param scoring - Scoring scheme for alignment
   * @return Alignment object containing optimal local alignment
   */
  def align(reference: String, 
           read: String, 
           scoring: SmithWatermanScoring): Alignment
}

/**
 * Scoring scheme interface for sequence alignment
 */
trait SmithWatermanScoring {
  /** Score for matching bases */
  def matchScore: Int
  
  /** Score for mismatching bases */
  def mismatchScore: Int
  
  /** Score for opening a gap */
  def gapOpenScore: Int
  
  /** Score for extending a gap */
  def gapExtendScore: Int
}

/**
 * Constant gap scoring scheme with fixed penalties
 * @param matchScore - Score for matches
 * @param mismatchScore - Penalty for mismatches  
 * @param gapScore - Penalty for gaps
 */
class SmithWatermanConstantGapScoring(
  matchScore: Int = 10,
  mismatchScore: Int = -2,
  gapScore: Int = -8
) extends SmithWatermanScoring {
  
  def gapOpenScore: Int = gapScore
  def gapExtendScore: Int = gapScore
}

/**
 * Affine gap scoring with different open and extend penalties
 * @param matchScore - Score for matches
 * @param mismatchScore - Penalty for mismatches
 * @param gapOpenScore - Penalty for opening gaps
 * @param gapExtendScore - Penalty for extending gaps
 */
class SmithWatermanGapScoringFromFn(
  matchScore: Int,
  mismatchScore: Int, 
  gapOpenScore: Int,
  gapExtendScore: Int
) extends SmithWatermanScoring

import org.bdgenomics.adam.algorithms.smithwaterman._

// Set up scoring scheme
val scoring = new SmithWatermanConstantGapScoring(
  matchScore = 10,
  mismatchScore = -2,
  gapScore = -5
)

// Perform alignment
val reference = "ACGTACGTACGT"
val query = "ACGTACGT"
val alignment = SmithWaterman.align(reference, query, scoring)

// Affine gap scoring for more realistic penalties
val affineScoring = new SmithWatermanGapScoringFromFn(
  matchScore = 10,
  mismatchScore = -4,
  gapOpenScore = -10,
  gapExtendScore = -1
)

val affineAlignment = SmithWaterman.align(reference, query, affineScoring)

/**
 * Utilities for normalizing variant representations
 * Implements variant normalization standards for consistent representation
 */
object NormalizationUtils {
  /**
   * Left-align and trim variant representation
   * @param variant - Variant to normalize
   * @param reference - Reference sequence context
   * @return Normalized variant representation
   */
  def leftAlignAndTrim(variant: Variant, reference: String): Variant
  
  /**
   * Decompose multi-nucleotide variants into primitive representations
   * @param variant - Complex variant to decompose
   * @return Sequence of primitive variants
   */
  def decomposeMNV(variant: Variant): Seq[Variant]
  
  /**
   * Normalize indel representation to canonical form
   * @param variant - Indel variant to normalize
   * @param reference - Reference sequence context
   * @return Canonically represented indel
   */
  def normalizeIndel(variant: Variant, reference: String): Variant
}

import org.bdgenomics.adam.algorithms.consensus.NormalizationUtils
import org.bdgenomics.adam.rdd.ADAMContext._

// Load variants and reference
val variants = sc.loadVcf("variants.vcf").toVariants()
val reference = sc.loadFasta("reference.fasta")

// Normalize variants
val normalized = variants.rdd.map { variant =>
  val refContext = reference.extract(
    ReferenceRegion(variant.getContigName, variant.getStart - 10, variant.getEnd + 10)
  )
  
  NormalizationUtils.leftAlignAndTrim(variant, refContext)
}

// Decompose complex variants
val decomposed = variants.rdd.flatMap { variant =>
  if (variant.getAlternateAllele.length > 1) {
    NormalizationUtils.decomposeMNV(variant)
  } else {
    Seq(variant)
  }
}

/**
 * Base quality score recalibration using known variant sites
 * Implements GATK-style base quality score recalibration
 * @param alignments - AlignmentRecordRDD to recalibrate
 * @param knownSnps - Known variant sites for recalibration modeling
 * @return AlignmentRecordRDD with recalibrated quality scores
 */
def recalibrateBaseQualities(alignments: AlignmentRecordRDD, 
                           knownSnps: VariantRDD): AlignmentRecordRDD

/**
 * Build quality score recalibration model
 * @param alignments - Training alignments
 * @param knownSites - Known variant sites
 * @return Recalibration model for applying to new data
 */
def buildRecalibrationModel(alignments: AlignmentRecordRDD,
                          knownSites: VariantRDD): RecalibrationModel

/**
 * Mark duplicate reads based on alignment coordinates
 * Implements Picard-compatible duplicate marking algorithm
 * @param alignments - AlignmentRecordRDD to process
 * @return AlignmentRecordRDD with duplicate reads flagged
 */
def markDuplicates(alignments: AlignmentRecordRDD): AlignmentRecordRDD

/**
 * Mark duplicate fragments for paired-end data
 * @param fragments - FragmentRDD to process  
 * @return FragmentRDD with duplicate fragments flagged
 */
def markDuplicates(fragments: FragmentRDD): FragmentRDD

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

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

// Mark duplicates
val markedDuplicates = alignments.markDuplicates()

// Remove duplicates for analysis
val uniqueReads = markedDuplicates.transform(_.filter(!_.getDuplicateRead))

// Quality recalibration
val knownSites = sc.loadVcf("dbsnp.vcf").toVariants()
val recalibrated = alignments.recalibrateBaseQualities(knownSites)

/**
 * Local realignment around indels to improve alignment accuracy
 * @param alignments - AlignmentRecordRDD to realign
 * @param knownIndels - Optional known indel sites for guidance
 * @return AlignmentRecordRDD with improved alignments around indels
 */
def realignIndels(alignments: AlignmentRecordRDD, 
                 knownIndels: Option[VariantRDD] = None): AlignmentRecordRDD

/**
 * Identify candidate regions for realignment
 * @param alignments - Input alignments
 * @return RDD of genomic regions requiring realignment
 */
def findRealignmentCandidates(alignments: AlignmentRecordRDD): RDD[ReferenceRegion]

/**
 * Calculate coverage depth across genomic positions
 * @param alignments - AlignmentRecordRDD to analyze
 * @param collapse - Whether to merge adjacent positions with same coverage
 * @return CoverageRDD with depth information
 */
def calculateCoverage(alignments: AlignmentRecordRDD, 
                     collapse: Boolean = true): CoverageRDD

/**
 * Identify regions with coverage above specified threshold
 * @param coverage - CoverageRDD to analyze
 * @param threshold - Minimum coverage threshold
 * @return FeatureRDD of high-coverage regions
 */
def findHighCoverageRegions(coverage: CoverageRDD, 
                          threshold: Double): FeatureRDD

/**
 * Calculate coverage statistics across genomic regions
 * @param coverage - Coverage data
 * @param regions - Regions of interest
 * @return RDD of coverage statistics per region
 */
def coverageStatistics(coverage: CoverageRDD, 
                      regions: FeatureRDD): RDD[(Feature, CoverageStatistics)]

/**
 * Count k-mers in sequencing reads
 * @param alignments - Reads to analyze
 * @param kmerLength - Length of k-mers to count
 * @return RDD of k-mer sequences with occurrence counts
 */
def countKmers(alignments: AlignmentRecordRDD, 
              kmerLength: Int): RDD[(String, Long)]

/**
 * Find overrepresented k-mers in reads
 * @param kmers - K-mer counts from countKmers
 * @param threshold - Minimum count threshold for overrepresentation
 * @return RDD of overrepresented k-mers
 */
def findOverrepresentedKmers(kmers: RDD[(String, Long)], 
                           threshold: Long): RDD[(String, Long)]

// K-mer analysis workflow
val alignments = sc.loadBam("sample.bam")

// Count 21-mers
val kmers = alignments.countKmers(21)

// Find most frequent k-mers
val topKmers = kmers.top(100)(Ordering.by(_._2))

// Identify overrepresented sequences
val overrepresented = kmers.filter(_._2 > 1000)