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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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6.  final def eq(arg0: AnyRef): Boolean

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7.  def equals(arg0: Any): Boolean

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8.  def finalize(): Unit

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9.  def fromReads(input: Dataset[NTSeq], method: CountMethod, normalize: Boolean, spl: Broadcast[AnyMinSplitter])(implicit spark: SparkSession): GroupedSegments

   Construct GroupedSegments from a set of reads/sequences

   Construct GroupedSegments from a set of reads/sequences

   input

   The raw sequence data

   method

   Counting method/pipeline type

   spl

   Splitter for breaking the sequences into super-mers

10.  final def getClass(): Class[_]

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11.  def hashCode(): Int

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12.  def hashSegments(input: NTSeq, splitter: AnyMinSplitter): Iterator[HashSegment]

    Construct HashSegments from a single read

    Construct HashSegments from a single read

    input

    The raw sequence

    splitter

    Splitter for breaking the sequences into super-mers

13.  def hashSegments(input: Dataset[NTSeq], spl: Broadcast[AnyMinSplitter])(implicit spark: SparkSession): Dataset[HashSegment]

    Construct HashSegments from a set of reads/sequences

    Construct HashSegments from a set of reads/sequences

    input

    The raw sequence data

    spl

    Splitter for breaking the sequences into super-mers

14.  final def isInstanceOf[T0]: Boolean

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15.  final def ne(arg0: AnyRef): Boolean

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16.  final def notify(): Unit

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17.  final def notifyAll(): Unit

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18.  def segmentsByHash(segments: DataFrame)(implicit spark: SparkSession): DataFrame

    Group segments by hash/minimizer, non-precounted This straightforward method is more efficient when supermers are not highly repeated in the data (low redundancy), or when the data is moderately sized.

    Group segments by hash/minimizer, non-precounted This straightforward method is more efficient when supermers are not highly repeated in the data (low redundancy), or when the data is moderately sized. The outputs are compatible with the method above.

    segments

    Supermers to group

19.  def segmentsByHashPregroup[S <: MinSplitter[MinimizerPriorities]](segments: DataFrame, addRC: Boolean, spl: Broadcast[S])(implicit spark: SparkSession): DataFrame

    Group segments by hash/minimizer, pre-grouping and counting identical supermers at an early stage, before assigning to buckets.

    Group segments by hash/minimizer, pre-grouping and counting identical supermers at an early stage, before assigning to buckets. This helps with high redundancy datasets and can greatly reduce the data volume that must be processed by later stages. However, it leads to one extra shuffle, so it may not be the best choice for moderately sized datasets. Reverse complements are optionally added after pregrouping (when we need to normalize k-mer orientation)

    segments

    Supermers to group

    addRC

    Whether to add reverse complements

    spl

    Splitter broadcast

20.  final def synchronized[T0](arg0: ⇒ T0): T0

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21.  def toString(): String

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22.  final def wait(): Unit

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23.  final def wait(arg0: Long, arg1: Int): Unit

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24.  final def wait(arg0: Long): Unit

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