Type Members

1.  final case class BucketSegment(bucket: BucketId, segment: NTBitArray) extends Product with Serializable

   A single super-mer with its bucket ID (derived from minimizer).

   A single super-mer with its bucket ID (derived from minimizer).

   bucket

   The bucket ID

   segment

   The super-mer

2.  sealed trait CountMethod extends AnyRef

   Defines a strategy for counting k-mers in Spark.

3.  class CountedKmers extends AnyRef

   Routines for converting encoded super-mers into individual counted k-mers.

4.  final case class Discount(k: Int, minimizers: MinimizerSource = Bundled, m: Int = 10, ordering: MinimizerOrdering = Frequency, sample: Double = 0.01, normalize: Boolean = false, method: CountMethod = Auto, partitions: Int = 200)(implicit spark: SparkSession) extends MinimizerConfig with Product with Serializable

   Main API entry point for Discount.

   Main API entry point for Discount. Also see the command line examples in the documentation for more information on these options.

   k

   k-mer length

   minimizers

   source of minimizers. See kmers.minimizer.MinimizerSource

   m

   minimizer width

   ordering

   minimizer ordering. See kmers.minimizer.MinimizerOrdering

   sample

   sample fraction for frequency orderings

   normalize

   whether to normalize k-mer orientation during counting. Reverse orientation k-mers are flipped to their forward orientation.

   method

   counting method to use (or None for automatic selection). See CountMethod

   partitions

   number of shuffle partitions/index buckets

   spark

   the SparkSession

5.  class GroupedSegments extends AnyRef

   A collection of counted super-mers grouped into bins (by minimizer).

   A collection of counted super-mers grouped into bins (by minimizer). Super-mers are segments of length >= k where every k-mer shares the same minimizer.

   Unlike with the Index, every k-mer in the super-mers is guaranteed to be present.

6.  class Index extends AnyRef

   A bucketed k-mer index.

   A bucketed k-mer index. Indexes store super-mers in a Dataset of bucket.ReducibleBucket, where each k-mer is associated with a tag. Typically tags are k-mer counts, and then the Index becomes a multiset of counted k-mers. Indexes are immutable, like other Spark datastructures, and operations like filtering return a new Index rather than change the existing one in place. Indexes can be combined using operations like union, intersect, and subtract, and can be written to disk in various formats. The default format used by the write() and read() methods is bucketed parquet files, which gives good data compression and avoids shuffling when the same Index is used repeatedly.

7.  class Kmers extends AnyRef

   Convenience methods for interacting with k-mers from a set of input files.