ADAM_2.12: Core 1.0 API < Back
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# Packages

-  package root
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    root
-  package org
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    root
-  package bdgenomics
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    org
-  package adam
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    bdgenomics
-  package ds
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    adam
-  package feature
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    ds
-  package fragment
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    ds
-  package read
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    ds
-  package sequence
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    ds
-  package variant
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    ds
- ADAMContext
- ADAMSaveAnyArgs
- AvroGenomicDataset
- AvroReadGroupGenomicDataset
- DatasetBoundGenericGenomicDataset
- DatasetBoundGenomicDataset
- FullOuterShuffleRegionJoin
- GenericConverter
- GenericGenomicDataset
- GenomeBins
- GenomicBroadcast
- GenomicDataset
- GenomicDatasetConversion
- GenomicDatasetWithLineage
- GenomicPositionPartitioner
- GenomicRegionPartitioner
- InFormatter
- InFormatterCompanion
- InnerShuffleRegionJoin
- InnerShuffleRegionJoinAndGroupByLeft
- InnerTreeRegionJoin
- InnerTreeRegionJoinAndGroupByRight
- LeftOuterShuffleRegionJoin
- LeftOuterShuffleRegionJoinAndGroupByLeft
- MultisampleAvroGenomicDataset
- MultisampleGenomicDataset
- OutFormatter
- RDDBoundGenericGenomicDataset
- ReferencePartitioner
- RegionJoin
- RightOuterShuffleRegionJoinAndGroupByLeft
- RightOuterTreeRegionJoin
- RightOuterTreeRegionJoinAndGroupByRight
- ShuffleRegionJoin
- SortedIntervalPartitionJoinWithVictims
- TreeRegionJoin
- VictimlessSortedIntervalPartitionJoin

c

org.bdgenomics.adam.ds

# ADAMContext 

###
Companion object ADAMContext

#### class ADAMContext extends Serializable with Logging

The ADAMContext provides functions on top of a SparkContext for loading genomic data.

Linear Supertypes
Logging, Serializable, Serializable, AnyRef, Any
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### Instance Constructors

1.  new ADAMContext(sc: SparkContext)

   sc

     The SparkContext to wrap.

### Value Members

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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     @throws( ... ) @native()
6.  def debug(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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7.  def debug(msg: ⇒ Any, t: ⇒ Throwable): Unit
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8.  def debug(msg: ⇒ Any): Unit
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9.  final def eq(arg0: AnyRef): Boolean
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10.  def equals(arg0: Any): Boolean
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11.  def error(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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12.  def error(msg: ⇒ Any, t: ⇒ Throwable): Unit
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13.  def error(msg: ⇒ Any): Unit
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14.  def finalize(): Unit
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      @throws( classOf[java.lang.Throwable] )
15.  final def getClass(): Class[_]
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      @native()
16.  def getFiles(path: Path, fs: FileSystem): Array[Path]

    Elaborates out a directory/glob/plain path.

    Elaborates out a directory/glob/plain path.

    path

      Path to elaborate.

    fs

      The underlying file system that this path is on.

    returns

      Returns an array of Paths to load.

    Attributes
      protected
    Exceptions thrown

      `FileNotFoundException` if the path does not match any files.

    See also

      getFsAndFiles

17.  def getFsAndFiles(path: Path): Array[Path]

    Elaborates out a directory/glob/plain path.

    Elaborates out a directory/glob/plain path.

    path

      Path to elaborate.

    returns

      Returns an array of Paths to load.

    Attributes
      protected
    Exceptions thrown

      `FileNotFoundException` if the path does not match any files.

    See also

      getFiles

18.  def getFsAndFilesWithFilter(pathName: String, filter: PathFilter): Array[Path]

    Elaborates out a directory/glob/plain path name.

    Elaborates out a directory/glob/plain path name.

    pathName

      Path name to elaborate.

    filter

      Filter to discard paths.

    returns

      Returns an array of Paths to load.

    Attributes
      protected
    Exceptions thrown

      `FileNotFoundException` if the path does not match any files.

    See also

      getFiles

19.  def hashCode(): Int
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20.  def info(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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21.  def info(msg: ⇒ Any, t: ⇒ Throwable): Unit
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22.  def info(msg: ⇒ Any): Unit
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23.  def isDebugEnabled: Boolean
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24.  def isErrorEnabled: Boolean
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25.  def isInfoEnabled: Boolean
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26.  final def isInstanceOf[T0]: Boolean
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27.  def isPartitioned(pathName: String): Boolean

    Return true if the specified path of Parquet + Avro files is partitioned.

    Return true if the specified path of Parquet + Avro files is partitioned.

    pathName

      Path in which to look for partitioned flag.

    returns

      Return true if the specified path of Parquet + Avro files is partitioned. Behavior is undefined if some paths in glob contain _partitionedByStartPos flag file and some do not.

28.  def isTraceEnabled: Boolean
    Attributes
      protected
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      Logging
29.  def isWarnEnabled: Boolean
    Attributes
      protected
    Definition Classes
      Logging
30.  def loadAlignments(df: DataFrame, references: SequenceDictionary, readGroups: ReadGroupDictionary, processingSteps: Seq[ProcessingStep]): AlignmentDataset

    Load the specified data frame, references, read groups, and processing steps into a AlignmentDataset.

    Load the specified data frame, references, read groups, and processing steps into a AlignmentDataset.

    df

      Data frame to load from.

    references

      References for the AlignmentDataset, may be empty.

    readGroups

      Read groups for the AlignmentDataset, may be empty.

    processingSteps

      Processing steps for the AlignmentDataset, may be empty.

    returns

      Returns a new AlignmentDataset loaded from the specified data frame, references, read groups, and processing steps.

31.  def loadAlignments(df: DataFrame, metadataPathName: String): AlignmentDataset

    Load the specified data frame into a AlignmentDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a AlignmentDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new AlignmentDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

32.  def loadAlignments(df: DataFrame): AlignmentDataset

    Load the specified data frame into a AlignmentDataset, with empty metadata.

    Load the specified data frame into a AlignmentDataset, with empty metadata.

    df

      Data frame to load from.

    returns

      Returns a new AlignmentDataset loaded from the specified data frame, with empty metadata.

33.  def loadAlignments(pathName: String, optPathName2: Option[String] = None, optReadGroup: Option[String] = None, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentDataset

    Load alignments into an AlignmentDataset.

    Load alignments into an AlignmentDataset.

    Loads path names ending in: * .bam/.cram/.sam as BAM/CRAM/SAM format, * .fa/.fasta as FASTA format, * .fq/.fastq as FASTQ format, and * .ifq as interleaved FASTQ format.

    If none of these match, fall back to Parquet + Avro.

    For FASTA, FASTQ, and interleaved FASTQ formats, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load alignments from. Globs/directories are supported, although file extension must be present for BAM/CRAM/SAM, FASTA, and FASTQ formats.

    optPathName2

      The optional path name to load the second set of alignment records from, if loading paired FASTQ format. Globs/directories are supported, although file extension must be present. Defaults to None.

    optReadGroup

      The optional read group identifier to associate to the alignment records. Defaults to None.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    stringency

      The validation stringency to use when validating BAM/CRAM/SAM or FASTQ formats. Defaults to ValidationStringency.STRICT.

    returns

      Returns an AlignmentDataset which wraps the genomic dataset of alignments, sequence dictionary representing reference sequences the alignments may be aligned to, and the read group dictionary for the alignments if one is available.

    See also

      loadBam

      loadFastq

      loadFastaDna(String, Long)

      loadInterleavedFastq

      loadParquetAlignments

34.  def loadBam(pathName: String, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentDataset

    Load alignments from BAM/CRAM/SAM into an AlignmentDataset.

    Load alignments from BAM/CRAM/SAM into an AlignmentDataset.

    This reads the sequence and read group dictionaries from the BAM/CRAM/SAM file header. SAMRecords are read from the file and converted to the Alignment schema.

    pathName

      The path name to load BAM/CRAM/SAM formatted alignments from. Globs/directories are supported.

    stringency

      The validation stringency to use when validating the BAM/CRAM/SAM format header. Defaults to ValidationStringency.STRICT.

    returns

      Returns an AlignmentDataset which wraps the genomic dataset of alignments, sequence dictionary representing reference sequences the alignments may be aligned to, and the read group dictionary for the alignments if one is available.

35.  def loadBed(pathName: String, optSequenceDictionary: Option[SequenceDictionary] = None, optMinPartitions: Option[Int] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FeatureDataset

    Load a path name in BED6/12 format into a FeatureDataset.

    Load a path name in BED6/12 format into a FeatureDataset.

    pathName

      The path name to load features in BED6/12 format from. Globs/directories are supported.

    optSequenceDictionary

      Optional sequence dictionary. Defaults to None.

    optMinPartitions

      An optional minimum number of partitions to load. If not set, falls back to the configured Spark default parallelism. Defaults to None.

    stringency

      The validation stringency to use when validating BED6/12 format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset.

36.  def loadCoverage(pathName: String, optSequenceDictionary: Option[SequenceDictionary] = None, optMinPartitions: Option[Int] = None, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): CoverageDataset

    Load features into a FeatureDataset and convert to a CoverageDataset.

    Load features into a FeatureDataset and convert to a CoverageDataset. Coverage is stored in the score field of Feature.

    Loads path names ending in: * .bed as BED6/12 format, * .gff3 as GFF3 format, * .gtf/.gff as GTF/GFF2 format, * .narrow[pP]eak as NarrowPeak format, and * .interval_list as IntervalList format.

    If none of these match, fall back to Parquet + Avro.

    For BED6/12, GFF3, GTF/GFF2, NarrowPeak, and IntervalList formats, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load features from. Globs/directories are supported, although file extension must be present for BED6/12, GFF3, GTF/GFF2, NarrowPeak, or IntervalList formats.

    optSequenceDictionary

      Optional sequence dictionary. Defaults to None.

    optMinPartitions

      An optional minimum number of partitions to use. For textual formats, if this is None, fall back to the Spark default parallelism. Defaults to None.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    stringency

      The validation stringency to use when validating BED6/12, GFF3, GTF/GFF2, NarrowPeak, or IntervalList formats. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset converted to a CoverageDataset.

    See also

      loadBed

      loadGtf

      loadGff3

      loadNarrowPeak

      loadIntervalList

      loadParquetFeatures

37.  def loadDnaSequences(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): SequenceDataset

    Load DNA sequences into a SequenceDataset.

    Load DNA sequences into a SequenceDataset.

    If the path name has a .fa/.fasta extension, load as FASTA format. Else, fall back to Parquet + Avro.

    For FASTA format, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load sequences from. Globs/directories are supported, although file extension must be present for FASTA format.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a SequenceDataset containing DNA sequences.

    See also

      loadFastaDna

      loadParquetSequences

38.  def loadFastaDna(pathName: String, maximumLength: Long): SliceDataset

    Load DNA slices from FASTA into a SliceDataset.

    Load DNA slices from FASTA into a SliceDataset.

    pathName

      The path name to load slices from. Globs/directories are supported.

    maximumLength

      Maximum fragment length. Values greater than 1e9 should be avoided.

    returns

      Returns a SliceDataset containing DNA slices.

39.  def loadFastaDna(pathName: String): SequenceDataset

    Load DNA sequences from FASTA into a SequenceDataset.

    Load DNA sequences from FASTA into a SequenceDataset.

    pathName

      The path name to load sequences from. Globs/directories are supported.

    returns

      Returns a SequenceDataset containing DNA sequences.

40.  def loadFastaProtein(pathName: String): SequenceDataset

    Load protein sequences from FASTA into a SequenceDataset.

    Load protein sequences from FASTA into a SequenceDataset.

    pathName

      The path name to load sequences from. Globs/directories are supported.

    returns

      Returns a SequenceDataset containing protein sequences.

41.  def loadFastaRna(pathName: String): SequenceDataset

    Load RNA sequences from FASTA into a SequenceDataset.

    Load RNA sequences from FASTA into a SequenceDataset.

    pathName

      The path name to load sequences from. Globs/directories are supported.

    returns

      Returns a SequenceDataset containing RNA sequences.

42.  def loadFastq(pathName1: String, optPathName2: Option[String], optReadGroup: Option[String] = None, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentDataset

    Load unaligned alignments from (possibly paired) FASTQ into an AlignmentDataset.

    Load unaligned alignments from (possibly paired) FASTQ into an AlignmentDataset.

    pathName1

      The path name to load the first set of unaligned alignments from. Globs/directories are supported.

    optPathName2

      The path name to load the second set of unaligned alignments from, if provided. Globs/directories are supported.

    optReadGroup

      The optional read group identifier to associate to the unaligned alignment records. Defaults to None.

    stringency

      The validation stringency to use when validating (possibly paired) FASTQ format. Defaults to ValidationStringency.STRICT.

    returns

      Returns an unaligned AlignmentDataset.

    See also

      loadPairedFastq

      loadUnpairedFastq

43.  def loadFeatures(df: DataFrame, references: SequenceDictionary, samples: Seq[Sample]): FeatureDataset

    Load the specified data frame, references, and samples into a FeatureDataset.

    Load the specified data frame, references, and samples into a FeatureDataset.

    df

      Data frame to load from.

    references

      References for the FeatureDataset, may be empty.

    samples

      Samples for the FeatureDataset, may be empty.

    returns

      Returns a new FeatureDataset loaded from the specified data frame, references, and samples.

44.  def loadFeatures(df: DataFrame, metadataPathName: String): FeatureDataset

    Load the specified data frame into a FeatureDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a FeatureDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new FeatureDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

45.  def loadFeatures(df: DataFrame): FeatureDataset

    Load the specified data frame into a FeatureDataset, with empty metadata.

    Load the specified data frame into a FeatureDataset, with empty metadata.

    df

      Data frame to load from.

    returns

      Returns a new FeatureDataset loaded from the specified data frame, with empty metadata.

46.  def loadFeatures(pathName: String, optSequenceDictionary: Option[SequenceDictionary] = None, optMinPartitions: Option[Int] = None, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FeatureDataset

    Load features into a FeatureDataset.

    Load features into a FeatureDataset.

    Loads path names ending in: * .bed as BED6/12 format, * .gff3 as GFF3 format, * .gtf/.gff as GTF/GFF2 format, * .narrow[pP]eak as NarrowPeak format, and * .interval_list as IntervalList format.

    If none of these match, fall back to Parquet + Avro.

    For BED6/12, GFF3, GTF/GFF2, NarrowPeak, and IntervalList formats, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load features from. Globs/directories are supported, although file extension must be present for BED6/12, GFF3, GTF/GFF2, NarrowPeak, or IntervalList formats.

    optSequenceDictionary

      Optional sequence dictionary. Defaults to None.

    optMinPartitions

      An optional minimum number of partitions to use. For textual formats, if this is None, fall back to the Spark default parallelism. Defaults to None.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    stringency

      The validation stringency to use when validating BED6/12, GFF3, GTF/GFF2, NarrowPeak, or IntervalList formats. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset.

    See also

      loadBed

      loadGtf

      loadGff3

      loadNarrowPeak

      loadIntervalList

      loadParquetFeatures

47.  def loadFragments(df: DataFrame, references: SequenceDictionary, readGroups: ReadGroupDictionary, processingSteps: Seq[ProcessingStep]): FragmentDataset

    Load the specified data frame, references, read groups, and processing steps into a FragmentDataset.

    Load the specified data frame, references, read groups, and processing steps into a FragmentDataset.

    df

      Data frame to load from.

    references

      References for the FragmentDataset, may be empty.

    readGroups

      Read groups for the FragmentDataset, may be empty.

    processingSteps

      Processing steps for the FragmentDataset, may be empty.

    returns

      Returns a new FragmentDataset loaded from the specified data frame, references, read groups, and processing steps.

48.  def loadFragments(df: DataFrame, metadataPathName: String): FragmentDataset

    Load the specified data frame into a FragmentDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a FragmentDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new FragmentDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

49.  def loadFragments(df: DataFrame): FragmentDataset

    Load the specified data frame into a FragmentDataset, with empty metadata.

    Load the specified data frame into a FragmentDataset, with empty metadata.

    df

      Data frame to load from.

    returns

      Returns a new FragmentDataset loaded from the specified data frame, with empty metadata.

50.  def loadFragments(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FragmentDataset

    Load fragments into a FragmentDataset.

    Load fragments into a FragmentDataset.

    Loads path names ending in: * .bam/.cram/.sam as BAM/CRAM/SAM format and * .ifq as interleaved FASTQ format.

    If none of these match, fall back to Parquet + Avro.

    For interleaved FASTQ format, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load fragments from. Globs/directories are supported, although file extension must be present for BAM/CRAM/SAM and FASTQ formats.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    stringency

      The validation stringency to use when validating BAM/CRAM/SAM or FASTQ formats. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FragmentDataset.

    See also

      loadBam

      loadAlignments

      loadInterleavedFastqAsFragments

      loadParquetFragments

51.  def loadGenotypes(df: DataFrame, references: SequenceDictionary, samples: Seq[Sample], headerLines: Seq[VCFHeaderLine]): GenotypeDataset

    Load the specified data frame, references, samples, and header lines into a GenotypeDataset.

    Load the specified data frame, references, samples, and header lines into a GenotypeDataset.

    df

      Data frame to load from.

    references

      References for the GenotypeDataset, may be empty.

    samples

      Samples for the GenotypeDataset, may be empty.

    headerLines

      Header lines for the GenotypeDataset, may be empty.

    returns

      Returns a new GenotypeDataset loaded from the specified data frame, references, samples, and header lines.

52.  def loadGenotypes(df: DataFrame, references: SequenceDictionary, samples: Seq[Sample]): GenotypeDataset

    Load the specified data frame, references, samples, and header lines into a GenotypeDataset, with the default header lines.

    Load the specified data frame, references, samples, and header lines into a GenotypeDataset, with the default header lines.

    df

      Data frame to load from.

    references

      References for the GenotypeDataset, may be empty.

    samples

      Samples for the GenotypeDataset, may be empty.

    returns

      Returns a new GenotypeDataset loaded from the specified data frame, references, and samples, with the default header lines.

53.  def loadGenotypes(df: DataFrame, metadataPathName: String): GenotypeDataset

    Load the specified data frame into a GenotypeDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a GenotypeDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new GenotypeDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

54.  def loadGenotypes(df: DataFrame): GenotypeDataset

    Load the specified data frame into a GenotypeDataset, with empty metadata and the default header lines.

    Load the specified data frame into a GenotypeDataset, with empty metadata and the default header lines.

    df

      Data frame to load from.

    returns

      Returns a new GenotypeDataset loaded from the specified data frame, with empty metadata and the default header lines.

55.  def loadGenotypes(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): GenotypeDataset

    Load genotypes into a GenotypeDataset.

    Load genotypes into a GenotypeDataset.

    If the path name has a .vcf/.vcf.gz/.vcf.bgz extension, load as VCF format. Else, fall back to Parquet + Avro.

    pathName

      The path name to load genotypes from. Globs/directories are supported, although file extension must be present for VCF format.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    stringency

      The validation stringency to use when validating VCF format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a GenotypeDataset.

    See also

      loadVcf

      loadParquetGenotypes

56.  def loadGff3(pathName: String, optSequenceDictionary: Option[SequenceDictionary] = None, optMinPartitions: Option[Int] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FeatureDataset

    Load a path name in GFF3 format into a FeatureDataset.

    Load a path name in GFF3 format into a FeatureDataset.

    pathName

      The path name to load features in GFF3 format from. Globs/directories are supported.

    optSequenceDictionary

      Optional sequence dictionary. Defaults to None.

    optMinPartitions

      An optional minimum number of partitions to load. If not set, falls back to the configured Spark default parallelism. Defaults to None.

    stringency

      The validation stringency to use when validating GFF3 format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset.

57.  def loadGtf(pathName: String, optSequenceDictionary: Option[SequenceDictionary] = None, optMinPartitions: Option[Int] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FeatureDataset

    Load a path name in GTF/GFF2 format into a FeatureDataset.

    Load a path name in GTF/GFF2 format into a FeatureDataset.

    pathName

      The path name to load features in GTF/GFF2 format from. Globs/directories are supported.

    optSequenceDictionary

      Optional sequence dictionary. Defaults to None.

    optMinPartitions

      An optional minimum number of partitions to load. If not set, falls back to the configured Spark default parallelism. Defaults to None.

    stringency

      The validation stringency to use when validating GTF/GFF2 format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset.

58.  def loadIndexedBam(pathName: String, viewRegions: Iterable[ReferenceRegion], stringency: ValidationStringency = ValidationStringency.STRICT)(implicit s: DummyImplicit): AlignmentDataset

    Functions like loadBam, but uses BAM index files to look at fewer blocks, and only returns records within the specified ReferenceRegions.

    Functions like loadBam, but uses BAM index files to look at fewer blocks, and only returns records within the specified ReferenceRegions. BAM index file required.

    pathName

      The path name to load indexed BAM formatted alignments from. Globs/directories are supported.

    viewRegions

      Iterable of ReferenceRegion we are filtering on.

    stringency

      The validation stringency to use when validating the BAM/CRAM/SAM format header. Defaults to ValidationStringency.STRICT.

    returns

      Returns an AlignmentDataset which wraps the genomic dataset of alignments, sequence dictionary representing reference sequences the alignments may be aligned to, and the read group dictionary for the alignments if one is available.

59.  def loadIndexedBam(pathName: String, viewRegion: ReferenceRegion): AlignmentDataset

    Functions like loadBam, but uses BAM index files to look at fewer blocks, and only returns records within a specified ReferenceRegion.

    Functions like loadBam, but uses BAM index files to look at fewer blocks, and only returns records within a specified ReferenceRegion. BAM index file required.

    pathName

      The path name to load indexed BAM formatted alignments from. Globs/directories are supported.

    viewRegion

      The ReferenceRegion we are filtering on.

    returns

      Returns an AlignmentDataset which wraps the genomic dataset of alignments, sequence dictionary representing reference sequences the alignments may be aligned to, and the read group dictionary for the alignments if one is available.

60.  def loadIndexedVcf(pathName: String, viewRegions: Iterable[ReferenceRegion], stringency: ValidationStringency = ValidationStringency.STRICT)(implicit s: DummyImplicit): VariantContextDataset

    Load variant context records from VCF indexed by tabix (tbi) into a VariantContextDataset.

    Load variant context records from VCF indexed by tabix (tbi) into a VariantContextDataset.

    pathName

      The path name to load VCF variant context records from. Globs/directories are supported.

    viewRegions

      Iterator of ReferenceRegions we are filtering on.

    stringency

      The validation stringency to use when validating VCF format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a VariantContextDataset.

61.  def loadIndexedVcf(pathName: String, viewRegion: ReferenceRegion): VariantContextDataset

    Load variant context records from VCF indexed by tabix (tbi) into a VariantContextDataset.

    Load variant context records from VCF indexed by tabix (tbi) into a VariantContextDataset.

    pathName

      The path name to load VCF variant context records from. Globs/directories are supported.

    viewRegion

      ReferenceRegion we are filtering on.

    returns

      Returns a VariantContextDataset.

62.  def loadInterleavedFastq(pathName: String): AlignmentDataset

    Load unaligned alignments from interleaved FASTQ into an AlignmentDataset.

    Load unaligned alignments from interleaved FASTQ into an AlignmentDataset.

    In interleaved FASTQ, the two reads from a paired sequencing protocol are interleaved in a single file. This is a zipped representation of the typical paired FASTQ.

    pathName

      The path name to load unaligned alignments from. Globs/directories are supported.

    returns

      Returns an unaligned AlignmentDataset.

63.  def loadInterleavedFastqAsFragments(pathName: String): FragmentDataset

    Load paired unaligned alignments grouped by sequencing fragment from interleaved FASTQ into an FragmentDataset.

    Load paired unaligned alignments grouped by sequencing fragment from interleaved FASTQ into an FragmentDataset.

    In interleaved FASTQ, the two reads from a paired sequencing protocol are interleaved in a single file. This is a zipped representation of the typical paired FASTQ.

    Fragments represent all of the reads from a single sequenced fragment as a single object, which is a useful representation for some tasks.

    pathName

      The path name to load unaligned alignments from. Globs/directories are supported.

    returns

      Returns a FragmentDataset containing the paired reads grouped by sequencing fragment.

64.  def loadIntervalList(pathName: String, optMinPartitions: Option[Int] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FeatureDataset

    Load a path name in IntervalList format into a FeatureDataset.

    Load a path name in IntervalList format into a FeatureDataset.

    pathName

      The path name to load features in IntervalList format from. Globs/directories are supported.

    optMinPartitions

      An optional minimum number of partitions to load. If not set, falls back to the configured Spark default parallelism. Defaults to None.

    stringency

      The validation stringency to use when validating IntervalList format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset.

65.  def loadNarrowPeak(pathName: String, optSequenceDictionary: Option[SequenceDictionary] = None, optMinPartitions: Option[Int] = None, stringency: ValidationStringency = ValidationStringency.STRICT): FeatureDataset

    Load a path name in NarrowPeak format into a FeatureDataset.

    Load a path name in NarrowPeak format into a FeatureDataset.

    pathName

      The path name to load features in NarrowPeak format from. Globs/directories are supported.

    optSequenceDictionary

      Optional sequence dictionary. Defaults to None.

    optMinPartitions

      An optional minimum number of partitions to load. If not set, falls back to the configured Spark default parallelism. Defaults to None.

    stringency

      The validation stringency to use when validating NarrowPeak format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FeatureDataset.

66.  def loadPairedFastq(pathName1: String, pathName2: String, optReadGroup: Option[String] = None, persistLevel: Option[StorageLevel] = Some(StorageLevel.MEMORY_ONLY), stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentDataset

    Load unaligned alignments from paired FASTQ into an AlignmentDataset.

    Load unaligned alignments from paired FASTQ into an AlignmentDataset.

    pathName1

      The path name to load the first set of unaligned alignments from. Globs/directories are supported.

    pathName2

      The path name to load the second set of unaligned alignments from. Globs/directories are supported.

    optReadGroup

      The optional read group identifier to associate to the unaligned alignment records. Defaults to None.

    persistLevel

      An optional persistence level to set. If this level is set, then reads will be cached (at the given persistence) level as part of validation. Defaults to StorageLevel.MEMORY_ONLY.

    stringency

      The validation stringency to use when validating paired FASTQ format. Defaults to ValidationStringency.STRICT.

    returns

      Returns an unaligned AlignmentDataset.

67.  def loadPairedFastqAsFragments(pathName1: String, pathName2: String, optReadGroup: Option[String] = None, persistLevel: Option[StorageLevel] = Some(StorageLevel.MEMORY_ONLY), stringency: ValidationStringency = ValidationStringency.STRICT): FragmentDataset

    Load paired unaligned alignments grouped by sequencing fragment from paired FASTQ files into an FragmentDataset.

    Load paired unaligned alignments grouped by sequencing fragment from paired FASTQ files into an FragmentDataset.

    Fragments represent all of the reads from a single sequenced fragment as a single object, which is a useful representation for some tasks.

    pathName1

      The path name to load the first set of unaligned alignments from. Globs/directories are supported.

    pathName2

      The path name to load the second set of unaligned alignments from. Globs/directories are supported.

    optReadGroup

      The optional read group identifier to associate to the unaligned alignment records. Defaults to None.

    persistLevel

      An optional persistance level to set. If this level is set, then reads will be cached (at the given persistance) level as part of validation. Defaults to StorageLevel.MEMORY_ONLY.

    stringency

      The validation stringency to use when validating paired FASTQ format. Defaults to ValidationStringency.STRICT.

    returns

      Returns a FragmentDataset containing the paired reads grouped by sequencing fragment.

68.  def loadParquet[T](pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None)(implicit ev1: (T) ⇒ SpecificRecord, ev2: Manifest[T]): RDD[T]

    Load a path name in Parquet + Avro format into an RDD.

    Load a path name in Parquet + Avro format into an RDD.

    T

      The type of records to return.

    pathName

      The path name to load Parquet + Avro formatted data from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      An RDD with records of the specified type.

69.  def loadParquetAlignments(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): AlignmentDataset

    Load a path name in Parquet + Avro format into an AlignmentDataset.

    Load a path name in Parquet + Avro format into an AlignmentDataset.

    pathName

      The path name to load alignments from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns an AlignmentDataset which wraps the genomic dataset of alignments, sequence dictionary representing reference sequences the alignments may be aligned to, and the read group dictionary for the alignments if one is available.

    Note

      The sequence dictionary is read from an Avro file stored at pathName/_references.avro and the read group dictionary is read from an Avro file stored at pathName/_readGroups.avro. These files are pure Avro, not Parquet + Avro.

70.  def loadParquetCoverage(pathName: String, optPredicate: Option[FilterPredicate] = None, forceRdd: Boolean = false): CoverageDataset

    Load a path name in Parquet + Avro format into a FeatureDataset and convert to a CoverageDataset.

    Load a path name in Parquet + Avro format into a FeatureDataset and convert to a CoverageDataset. Coverage is stored in the score field of Feature.

    pathName

      The path name to load features from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    forceRdd

      Forces loading the RDD.

    returns

      Returns a FeatureDataset converted to a CoverageDataset.

71.  def loadParquetFeatures(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): FeatureDataset

    Load a path name in Parquet + Avro format into a FeatureDataset.

    Load a path name in Parquet + Avro format into a FeatureDataset.

    pathName

      The path name to load features from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a FeatureDataset.

72.  def loadParquetFragments(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): FragmentDataset

    Load a path name in Parquet + Avro format into a FragmentDataset.

    Load a path name in Parquet + Avro format into a FragmentDataset.

    pathName

      The path name to load fragments from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a FragmentDataset.

73.  def loadParquetGenotypes(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): GenotypeDataset

    Load a path name in Parquet + Avro format into a GenotypeDataset.

    Load a path name in Parquet + Avro format into a GenotypeDataset.

    pathName

      The path name to load genotypes from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a GenotypeDataset.

74.  def loadParquetReads(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): ReadDataset

    Load a path name in Parquet + Avro format into a ReadDataset.

    Load a path name in Parquet + Avro format into a ReadDataset.

    pathName

      The path name to load reads from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a ReadDataset.

75.  def loadParquetSequences(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): SequenceDataset

    Load a path name in Parquet + Avro format into a SequenceDataset.

    Load a path name in Parquet + Avro format into a SequenceDataset.

    pathName

      The path name to load sequences from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a SequenceDataset.

76.  def loadParquetSlices(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): SliceDataset

    Load a path name in Parquet + Avro format into a SliceDataset.

    Load a path name in Parquet + Avro format into a SliceDataset.

    pathName

      The path name to load slices from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a SliceDataset.

77.  def loadParquetVariantContexts(pathName: String): VariantContextDataset

    Load a path name in Parquet + Avro format into a VariantContextDataset.

    Load a path name in Parquet + Avro format into a VariantContextDataset.

    pathName

      The path name to load variant context records from. Globs/directories are supported.

    returns

      Returns a VariantContextDataset.

78.  def loadParquetVariants(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): VariantDataset

    Load a path name in Parquet format into a VariantDataset.

    Load a path name in Parquet format into a VariantDataset.

    pathName

      The path name to load variants from. Globs/directories are supported.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An option projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a VariantDataset.

79.  def loadPartitionedParquetAlignments(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): AlignmentDataset

    Load a path name with range binned partitioned Parquet format into an AlignmentDataset.

    Load a path name with range binned partitioned Parquet format into an AlignmentDataset.

    pathName

      The path name to load alignments from. Globs/directories are supported.

    regions

      Optional list of genomic regions to load.

    optLookbackPartitions

      Number of partitions to lookback to find beginning of an overlapping region when using the filterByOverlappingRegions function on the returned dataset. Defaults to one partition.

    returns

      Returns an AlignmentDataset.

    Note

      The sequence dictionary is read from an Avro file stored at pathName/_references.avro and the read group dictionary is read from an Avro file stored at pathName/_readGroups.avro. These files are pure Avro, not Parquet + Avro.

80.  def loadPartitionedParquetFeatures(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): FeatureDataset

    Load a path name with range binned partitioned Parquet format into a FeatureDataset.

    Load a path name with range binned partitioned Parquet format into a FeatureDataset.

    pathName

      The path name to load alignments from. Globs/directories are supported.

    regions

      Optional list of genomic regions to load.

    optLookbackPartitions

      Number of partitions to lookback to find beginning of an overlapping region when using the filterByOverlappingRegions function on the returned dataset. Defaults to one partition.

    returns

      Returns a FeatureDataset.

81.  def loadPartitionedParquetGenotypes(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): GenotypeDataset

    Load a path name with range binned partitioned Parquet format into a GenotypeDataset.

    Load a path name with range binned partitioned Parquet format into a GenotypeDataset.

    pathName

      The path name to load alignments from. Globs/directories are supported.

    regions

      Optional list of genomic regions to load.

    optLookbackPartitions

      Number of partitions to lookback to find beginning of an overlapping region when using the filterByOverlappingRegions function on the returned dataset. Defaults to one partition.

    returns

      Returns a GenotypeDataset.

82.  def loadPartitionedParquetVariantContexts(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): VariantContextDataset

    Load a path name with range binned partitioned Parquet format into a VariantContextDataset.

    Load a path name with range binned partitioned Parquet format into a VariantContextDataset.

    pathName

      The path name to load variant context records from. Globs/directories are supported.

    regions

      Optional list of genomic regions to load.

    optLookbackPartitions

      Number of partitions to lookback to find beginning of an overlapping region when using the filterByOverlappingRegions function on the returned dataset. Defaults to one partition.

    returns

      Returns a VariantContextDataset.

83.  def loadPartitionedParquetVariants(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): VariantDataset

    Load a path name with range binned partitioned Parquet format into a VariantDataset.

    Load a path name with range binned partitioned Parquet format into a VariantDataset.

    pathName

      The path name to load alignments from. Globs/directories are supported.

    regions

      Optional list of genomic regions to load.

    optLookbackPartitions

      Number of partitions to lookback to find beginning of an overlapping region when using the filterByOverlappingRegions function on the returned dataset. Defaults to one partition.

    returns

      Returns a VariantDataset.

84.  def loadProteinSequences(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): SequenceDataset

    Load protein sequences into a SequenceDataset.

    Load protein sequences into a SequenceDataset.

    If the path name has a .fa/.fasta extension, load as FASTA format. Else, fall back to Parquet + Avro.

    For FASTA format, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load sequences from. Globs/directories are supported, although file extension must be present for FASTA format.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a SequenceRDD containing protein sequences.

    See also

      loadFastaProtein

      loadParquetSequences

85.  def loadReads(df: DataFrame, references: SequenceDictionary, samples: Iterable[Sample]): ReadDataset

    Load the specified data frame and references into a ReadDataset.

    Load the specified data frame and references into a ReadDataset.

    df

      Data frame to load from.

    references

      References for the ReadDataset, may be empty.

    samples

      Samples for the ReadDataset, may be empty.

    returns

      Returns a new ReadDataset loaded from the specified data frame, references, and samples.

86.  def loadReads(df: DataFrame, metadataPathName: String): ReadDataset

    Load the specified data frame into a ReadDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a ReadDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new ReadDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

87.  def loadReads(df: DataFrame): ReadDataset

    Load the specified data frame into a ReadDataset, with empty metadata.

    Load the specified data frame into a ReadDataset, with empty metadata.

    df

      Data frame to load from.

    returns

      Returns a new ReadDataset loaded from the specified data frame, with empty metadata.

88.  def loadReferenceFile(pathName: String, maximumLength: Long): ReferenceFile

    Load reference sequences into a broadcastable ReferenceFile.

    Load reference sequences into a broadcastable ReferenceFile.

    If the path name has a .2bit extension, loads a 2bit file. Else, uses loadSlices to load the reference as an RDD, which is then collected to the driver.

    pathName

      The path name to load reference sequences from. Globs/directories for 2bit format are not supported.

    maximumLength

      Maximum fragment length. Defaults to 10000L. Values greater than 1e9 should be avoided.

    returns

      Returns a broadcastable ReferenceFile.

    See also

      loadSlices

89.  def loadRnaSequences(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): SequenceDataset

    Load RNA sequences into a SequenceDataset.

    Load RNA sequences into a SequenceDataset.

    If the path name has a .fa/.fasta extension, load as FASTA format. Else, fall back to Parquet + Avro.

    For FASTA format, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load sequences from. Globs/directories are supported, although file extension must be present for FASTA format.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a SequenceDataset containing RNA sequences.

    See also

      loadFastaRna

      loadParquetSequences

90.  def loadSequenceDictionary(pathName: String): SequenceDictionary

    Load a sequence dictionary.

    Load a sequence dictionary.

    Loads path names ending in: * .dict as HTSJDK sequence dictionary format, * .genome as Bedtools genome file format, * .txt as UCSC Genome Browser chromInfo files.

    Compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load a sequence dictionary from.

    returns

      Returns a sequence dictionary.

    Exceptions thrown

      `IllegalArgumentException` if pathName file extension not one of .dict, .genome, or .txt

91.  def loadSequences(df: DataFrame, references: SequenceDictionary, samples: Iterable[Sample]): SequenceDataset

    Load the specified data frame and references into a SequenceDataset.

    Load the specified data frame and references into a SequenceDataset.

    df

      Data frame to load from.

    references

      References for the SequenceDataset, may be empty.

    samples

      Samples for the SequenceDataset, may be empty.

    returns

      Returns a new SequenceDataset loaded from the specified data frame, references, and samples.

92.  def loadSequences(df: DataFrame, metadataPathName: String): SequenceDataset

    Load the specified data frame into a SequenceDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a SequenceDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new SequenceDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

93.  def loadSequences(df: DataFrame): SequenceDataset

    Load the specified data frame into a SequenceDataset, with empty metadata.

    Load the specified data frame into a SequenceDataset, with empty metadata.

    df

      Data frame to load from.

    returns

      Returns a new SequenceDataset loaded from the specified data frame, with empty metadata.

94.  def loadSlices(df: DataFrame, references: SequenceDictionary, samples: Iterable[Sample]): SliceDataset

    Load the specified data frame and references into a SliceDataset.

    Load the specified data frame and references into a SliceDataset.

    df

      Data frame to load from.

    references

      References for the SliceDataset, may be empty.

    samples

      Samples for the SliceDataset, may be empty.

    returns

      Returns a new SliceDataset loaded from the specified data frame and references.

95.  def loadSlices(df: DataFrame, metadataPathName: String): SliceDataset

    Load the specified data frame into a SliceDataset, with metadata loaded from the specified metadata path name.

    Load the specified data frame into a SliceDataset, with metadata loaded from the specified metadata path name.

    df

      Data frame to load from.

    metadataPathName

      Path name to load metadata from.

    returns

      Returns a new SliceDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

96.  def loadSlices(df: DataFrame): SliceDataset

    Load the specified data frame into a SliceDataset, with empty metadata.

    Load the specified data frame into a SliceDataset, with empty metadata.

    df

      Data frame to load from.

    returns

      Returns a new SliceDataset loaded from the specified data frame, with empty metadata.

97.  def loadSlices(pathName: String, maximumLength: Long = 10000L, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): SliceDataset

    Load slices into a SliceDataset.

    Load slices into a SliceDataset.

    If the path name has a .fa/.fasta extension, load as DNA in FASTA format. Else, fall back to Parquet + Avro.

    For FASTA format, compressed files are supported through compression codecs configured in Hadoop, which by default include .gz and .bz2, but can include more.

    pathName

      The path name to load DNA slices from. Globs/directories are supported, although file extension must be present for FASTA format.

    maximumLength

      Maximum slice length. Defaults to 10000L. Values greater than 1e9 should be avoided.

    optPredicate

      An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

    optProjection

      An optional projection schema to use when reading Parquet + Avro. Defaults to None.

    returns

      Returns a SliceDataset.

    See also

      loadFastaDna(String, Long)

      loadParquetSlices

98.  def loadUnpairedFastq(pathName: String, setFirstOfPair: Boolean = false, setSecondOfPair: Boolean = false, optReadGroup: Option[String] = None, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentDataset

    Load unaligned alignments from unpaired FASTQ into an AlignmentDataset.

    Load unaligned alignments from unpaired FASTQ into an AlignmentDataset.

    pathName

      The path name to load unaligned alignments from. Globs/directories are supported.

    setFirstOfPair

      If true, sets the unaligned alignment as first from the fragment. Defaults to false.

    setSecondOfPair

      If true, sets the unaligned alignment as second from the fragment. Defaults to false.

    optReadGroup

      The optional read group identifier to associate to the unaligned alignment records. Defaults to None.

    stringency

      The validation stringency to use when validating unpaired FASTQ format. Defaults to ValidationStringency.STRICT.

    returns

      Returns an unaligned AlignmentDataset.

99.  def loadVariantContexts(df: DataFrame, references: SequenceDictionary, samples: Seq[Sample], headerLines: Seq[VCFHeaderLine]): VariantContextDataset

    Load the specified data frame, references, samples, and header lines into a VariantContextDataset.

    Load the specified data frame, references, samples, and header lines into a VariantContextDataset.

    df

      Data frame to load from.

    references

      References for the VariantContextDataset, may be empty.

    samples

      Samples for the VariantContextDataset, may be empty.

    headerLines

      Header lines for the VariantContextDataset, may be empty.

    returns

      Returns a new VariantContextDataset loaded from the specified data frame, references, samples, and header lines.

100.  def loadVariantContexts(df: DataFrame, references: SequenceDictionary, samples: Seq[Sample]): VariantContextDataset

     Load the specified data frame, references, and samples into a VariantContextDataset, with the default header lines.

     Load the specified data frame, references, and samples into a VariantContextDataset, with the default header lines.

     df

       Data frame to load from.

     references

       References for the VariantContextDataset, may be empty.

     samples

       Samples for the GenotypeDataset, may be empty.

     returns

       Returns a new VariantContextDataset loaded from the specified data frame, references, and samples, with the default header lines.

101.  def loadVariantContexts(df: DataFrame, metadataPathName: String): VariantContextDataset

     Load the specified data frame into a VariantContextDataset, with metadata loaded from the specified metadata path name.

     Load the specified data frame into a VariantContextDataset, with metadata loaded from the specified metadata path name.

     df

       Data frame to load from.

     metadataPathName

       Path name to load metadata from.

     returns

       Returns a new VariantContextDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

102.  def loadVariantContexts(df: DataFrame): VariantContextDataset

     Load the specified data frame into a VariantContextDataset, with empty metadata and the default header lines.

     Load the specified data frame into a VariantContextDataset, with empty metadata and the default header lines.

     df

       Data frame to load from.

     returns

       Returns a new VariantContextDataset loaded from the specified data frame, with empty metadata and the default header lines.

103.  def loadVariantContexts(pathName: String): VariantContextDataset

     Load a path name in VCF or Parquet format into a VariantContextDataset.

     Load a path name in VCF or Parquet format into a VariantContextDataset.

     pathName

       The path name to load variant context records from. Globs/directories are supported.

     returns

       Returns a VariantContextDataset.

104.  def loadVariantContexts(pathName: String, stringency: ValidationStringency): VariantContextDataset

     Load a path name in VCF or Parquet format into a VariantContextDataset.

     Load a path name in VCF or Parquet format into a VariantContextDataset.

     pathName

       The path name to load variant context records from. Globs/directories are supported.

     stringency

       The validation stringency to use when validating VCF format.

     returns

       Returns a VariantContextDataset.

105.  def loadVariants(df: DataFrame, references: SequenceDictionary, headerLines: Seq[VCFHeaderLine]): VariantDataset

     Load the specified data frame, references, and header lines into a VariantDataset.

     Load the specified data frame, references, and header lines into a VariantDataset.

     df

       Data frame to load from.

     references

       References for the VariantDataset, may be empty.

     headerLines

       Header lines for the VariantDataset, may be empty.

     returns

       Returns a new VariantDataset loaded from the specified data frame, references, and header lines.

106.  def loadVariants(df: DataFrame, references: SequenceDictionary): VariantDataset

     Load the specified data frame and references into a VariantDataset, with the default header lines.

     Load the specified data frame and references into a VariantDataset, with the default header lines.

     df

       Data frame to load from.

     references

       References for the VariantDataset, may be empty.

     returns

       Returns a new VariantDataset loaded from the specified data frame and references, with the default header lines.

107.  def loadVariants(df: DataFrame, metadataPathName: String): VariantDataset

     Load the specified data frame into a VariantDataset, with metadata loaded from the specified metadata path name.

     Load the specified data frame into a VariantDataset, with metadata loaded from the specified metadata path name.

     df

       Data frame to load from.

     metadataPathName

       Path name to load metadata from.

     returns

       Returns a new VariantDataset loaded from the specified data frame, with metadata loaded from the specified metadata path name.

108.  def loadVariants(df: DataFrame): VariantDataset

     Load the specified data frame into a VariantDataset, with empty metadata and the default header lines.

     Load the specified data frame into a VariantDataset, with empty metadata and the default header lines.

     df

       Data frame to load from.

     returns

       Returns a new VariantDataset loaded from the specified data frame, with empty metadata and the default header lines.

109.  def loadVariants(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): VariantDataset

     Load variants into a VariantDataset.

     Load variants into a VariantDataset.

     If the path name has a .vcf/.vcf.gz/.vcf.bgz extension, load as VCF format. Else, fall back to Parquet + Avro.

     pathName

       The path name to load variants from. Globs/directories are supported, although file extension must be present for VCF format.

     optPredicate

       An optional pushdown predicate to use when reading Parquet + Avro. Defaults to None.

     optProjection

       An option projection schema to use when reading Parquet + Avro. Defaults to None.

     stringency

       The validation stringency to use when validating VCF format. Defaults to ValidationStringency.STRICT.

     returns

       Returns a VariantDataset.

     See also

       loadVcf

       loadParquetVariants

110.  def loadVcf(pathName: String, stringency: ValidationStringency = ValidationStringency.STRICT): VariantContextDataset

     Load variant context records from VCF into a VariantContextDataset.

     Load variant context records from VCF into a VariantContextDataset.

     pathName

       The path name to load VCF variant context records from. Globs/directories are supported.

     stringency

       The validation stringency to use when validating VCF format. Defaults to ValidationStringency.STRICT.

     returns

       Returns a VariantContextDataset.

111.  def loadVcfWithProjection(pathName: String, infoFields: Set[String], formatFields: Set[String], stringency: ValidationStringency = ValidationStringency.STRICT): VariantContextDataset

     Load variant context records from VCF into a VariantContextDataset.

     Load variant context records from VCF into a VariantContextDataset.

     Only converts the core Genotype/Variant fields, and the fields set in the requested projection. Core variant fields include:

     * Names (ID) * Filters (FILTER)

     Core genotype fields include:

     * Allelic depth (AD) * Read depth (DP) * Min read depth (MIN_DP) * Genotype quality (GQ) * Genotype likelihoods (GL/PL) * Strand bias components (SB) * Phase info (PS,PQ)

     pathName

       The path name to load VCF variant context records from. Globs/directories are supported.

     infoFields

       The info fields to include, in addition to the ID and FILTER attributes.

     formatFields

       The format fields to include, in addition to the core fields listed above.

     stringency

       The validation stringency to use when validating VCF format. Defaults to ValidationStringency.STRICT.

     returns

       Returns a VariantContextDataset.

112.  def logger: Logger
     Attributes
       protected
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       Logging
113.  def loggerName: String
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       protected
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114.  final def ne(arg0: AnyRef): Boolean
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115.  final def notify(): Unit
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       @native()
116.  final def notifyAll(): Unit
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       @native()
117.  val sc: SparkContext
118.  lazy val spark: SparkSession
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       @transient()
119.  final def synchronized[T0](arg0: ⇒ T0): T0
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120.  def toString(): String
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121.  def trace(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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       protected
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122.  def trace(msg: ⇒ Any, t: ⇒ Throwable): Unit
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123.  def trace(msg: ⇒ Any): Unit
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124.  final def wait(): Unit
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       @throws( ... )
125.  final def wait(arg0: Long, arg1: Int): Unit
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       @throws( ... )
126.  final def wait(arg0: Long): Unit
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       @throws( ... ) @native()
127.  def warn(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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128.  def warn(msg: ⇒ Any, t: ⇒ Throwable): Unit
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129.  def warn(msg: ⇒ Any): Unit
     Attributes
       protected
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### Inherited from Logging

### Inherited from Serializable

### Inherited from AnyRef

### Inherited from Any

### Ungrouped