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object GroupedSegments

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  5. def bucketIds(splitter: AnyMinSplitter)(implicit spark: SparkSession): Dataset[BucketId]
  6. def bucketSegments(input: NTSeq, splitter: AnyMinSplitter): Iterator[BucketSegment]

    Construct BucketSegments from a single read

    Construct BucketSegments from a single read

    input

    The raw sequence

    splitter

    Splitter for breaking the sequences into super-mers

  7. def bucketSegments(input: Dataset[NTSeq], spl: Broadcast[AnyMinSplitter])(implicit spark: SparkSession): Dataset[BucketSegment]

    Construct BucketSegments from a set of reads/sequences

    Construct BucketSegments from a set of reads/sequences

    input

    The raw sequence data

    spl

    Splitter for breaking the sequences into super-mers

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  11. def fromReads(input: Dataset[NTSeq], method: CountMethod, 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

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  18. def rankToBucketId(rank: Array[Long], width: Int): BucketId

    Convert a minimizer/rank to a bucket ID.

  19. def segmentsByBucket(segments: DataFrame): DataFrame

    Group segments by bucket/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 bucket/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

  20. def segmentsByBucketPregroup[S <: MinSplitter[MinimizerPriorities]](segments: DataFrame, spl: Broadcast[S])(implicit spark: SparkSession): DataFrame

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

    Group segments by 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

    spl

    Splitter broadcast

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