class Kmers extends AnyRef
Convenience methods for interacting with k-mers from a set of input files.
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new
Kmers(discount: Discount, inFiles: Seq[String], knownSplitter: Option[Broadcast[AnyMinSplitter]] = None)(implicit spark: SparkSession)
- discount
The Discount object
- inFiles
Input files
- knownSplitter
The splitter/minimizer scheme to use, if one is available. Otherwise, a new one will be constructed.
Value Members
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lazy val
bcSplit: Broadcast[AnyMinSplitter]
Broadcast of the read splitter associated with this set of inputs.
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clone(): AnyRef
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def
constructSampledMinimizerOrdering(writeLocation: String): MinSplitter[_]
Sample the input data, counting minimizers and writing the generated frequency ordering to HDFS.
Sample the input data, counting minimizers and writing the generated frequency ordering to HDFS.
- writeLocation
Location to write the frequency ordering to
- returns
A splitter object corresponding to the generated ordering
- val discount: Discount
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- val inFiles: Seq[String]
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lazy val
index: Index
A counting k-mer index containing all k-mers from the input sequences.
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def
inputFragments: Dataset[InputFragment]
Input fragments associated with these inputs.
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final
def
isInstanceOf[T0]: Boolean
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lazy val
method: CountMethod
The overall method used for k-mer counting.
The overall method used for k-mer counting. If not specified, this will be guessed from the input data according to a heuristic.
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notifyAll(): Unit
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def
sampledIndex(fraction: Double): Index
Construct an index from a sampled fraction of this input data.
Construct an index from a sampled fraction of this input data. Because repeated calls will sample the input differently, it is recommended to cache the Index if it will be used repeatedly.
- def segments: GroupedSegments
- def sequenceTitles: Dataset[SeqTitle]
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