org.bdgenomics.adam.rdd

# ADAMContext

###
Related Docs: object ADAMContext | package rdd

#### 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
Ordering

1. Alphabetic
2. By Inheritance

Inherited

1. ADAMContext
2. Logging
3. Serializable
4. Serializable
5. AnyRef
6. Any

1. Hide All
2. Show All

Visibility

1. Public
2. All

### Instance Constructors

1. #### new ADAMContext(sc: SparkContext)

   sc

     The SparkContext to wrap.

### Value Members

1. #### final def !=(arg0: Any): Boolean

   Definition Classes
     AnyRef → Any

2. #### final def ##(): Int

   Definition Classes
     AnyRef → Any

3. #### final def ==(arg0: Any): Boolean

   Definition Classes
     AnyRef → Any

4. #### final def asInstanceOf[T0]: T0

   Definition Classes
     Any

5. #### def clone(): AnyRef

   Attributes
     protected[java.lang]
   Definition Classes
     AnyRef
   Annotations
     @throws( ... )

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

   Definition Classes
     AnyRef

7. #### def equals(arg0: Any): Boolean

   Definition Classes
     AnyRef → Any

8. #### def finalize(): Unit

   Attributes
     protected[java.lang]
   Definition Classes
     AnyRef
   Annotations
     @throws( classOf[java.lang.Throwable] )

9. #### final def getClass(): Class[_]

   Definition Classes
     AnyRef → Any

10. #### 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

11. #### 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

12. #### 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

13. #### def hashCode(): Int

    Definition Classes
      AnyRef → Any

14. #### final def isInstanceOf[T0]: Boolean

    Definition Classes
      Any

15. #### 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.

16. #### def isTraceEnabled(): Boolean

    Attributes
      protected
    Definition Classes
      Logging

17. #### def loadAlignments(pathName: String, optPathName2: Option[String] = None, optReadGroup: Option[String] = None, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentRecordDataset

    Load alignment records into an AlignmentRecordDataset.

    Load alignment records into an AlignmentRecordDataset.

    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 alignment records 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 AlignmentRecordDataset which wraps the genomic dataset of alignment records, sequence dictionary representing reference sequences the alignment records may be aligned to, and the read group dictionary for the alignment records if one is available.

    See also

      loadParquetAlignments

      loadInterleavedFastq

      loadFasta

      loadFastq

      loadBam

18. #### def loadBam(pathName: String, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentRecordDataset

    Load alignment records from BAM/CRAM/SAM into an AlignmentRecordDataset.

    Load alignment records from BAM/CRAM/SAM into an AlignmentRecordDataset.

    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 AlignmentRecord schema.

    pathName

      The path name to load BAM/CRAM/SAM formatted alignment records 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 AlignmentRecordDataset which wraps the genomic dataset of alignment records, sequence dictionary representing reference sequences the alignment records may be aligned to, and the read group dictionary for the alignment records if one is available.

19. #### 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.

20. #### def loadContigFragments(pathName: String, maximumLength: Long = 10000L, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): NucleotideContigFragmentDataset

    Load nucleotide contig fragments into a NucleotideContigFragmentDataset.

    Load nucleotide contig fragments into a NucleotideContigFragmentDataset.

    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 nucleotide contig fragments from. Globs/directories are supported, although file extension must be present for FASTA format.

    maximumLength

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

    returns

      Returns a NucleotideContigFragmentDataset.

    See also

      loadParquetContigFragments

      loadFasta

21. #### 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

      loadParquetFeatures

      loadIntervalList

      loadNarrowPeak

      loadGff3

      loadGtf

      loadBed

22. #### def loadFasta(pathName: String, maximumLength: Long = 10000L): NucleotideContigFragmentDataset

    Load nucleotide contig fragments from FASTA into a NucleotideContigFragmentDataset.

    Load nucleotide contig fragments from FASTA into a NucleotideContigFragmentDataset.

    pathName

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

    maximumLength

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

    returns

      Returns a NucleotideContigFragmentDataset.

23. #### def loadFastq(pathName1: String, optPathName2: Option[String], optReadGroup: Option[String] = None, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentRecordDataset

    Load unaligned alignment records from (possibly paired) FASTQ into an AlignmentRecordDataset.

    Load unaligned alignment records from (possibly paired) FASTQ into an AlignmentRecordDataset.

    pathName1

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

    optPathName2

      The path name to load the second set of unaligned alignment records 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 AlignmentRecordDataset.

    See also

      loadUnpairedFastq

      loadPairedFastq

24. #### 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

      loadParquetFeatures

      loadIntervalList

      loadNarrowPeak

      loadGff3

      loadGtf

      loadBed

25. #### 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

      loadParquetFragments

      loadInterleavedFastqAsFragments

      loadAlignments

      loadBam

26. #### 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

      loadParquetGenotypes

      loadVcf

27. #### 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.

28. #### 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.

29. #### def loadIndexedBam(pathName: String, viewRegions: Iterable[ReferenceRegion], stringency: ValidationStringency = ValidationStringency.STRICT)(implicit s: DummyImplicit): AlignmentRecordDataset

    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 alignment records 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 AlignmentRecordDataset which wraps the genomic dataset of alignment records, sequence dictionary representing reference sequences the alignment records may be aligned to, and the read group dictionary for the alignment records if one is available.

30. #### def loadIndexedBam(pathName: String, viewRegion: ReferenceRegion): AlignmentRecordDataset

    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 alignment records from. Globs/directories are supported.

    viewRegion

      The ReferenceRegion we are filtering on.

    returns

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

31. #### 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.

32. #### 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.

33. #### def loadInterleavedFastq(pathName: String): AlignmentRecordDataset

    Load unaligned alignment records from interleaved FASTQ into an AlignmentRecordDataset.

    Load unaligned alignment records from interleaved FASTQ into an AlignmentRecordDataset.

    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 alignment records from. Globs/directories are supported.

    returns

      Returns an unaligned AlignmentRecordDataset.

34. #### def loadInterleavedFastqAsFragments(pathName: String): FragmentDataset

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

    Load paired unaligned alignment records 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 alignment records from. Globs/directories are supported.

    returns

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

35. #### 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.

36. #### 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.

37. #### def loadPairedFastq(pathName1: String, pathName2: String, optReadGroup: Option[String] = None, persistLevel: Option[StorageLevel] = Some(StorageLevel.MEMORY_ONLY), stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentRecordDataset

    Load unaligned alignment records from paired FASTQ into an AlignmentRecordDataset.

    Load unaligned alignment records from paired FASTQ into an AlignmentRecordDataset.

    pathName1

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

    pathName2

      The path name to load the second set of unaligned alignment records 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 an unaligned AlignmentRecordDataset.

38. #### 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 alignment records grouped by sequencing fragment from paired FASTQ files into an FragmentDataset.

    Load paired unaligned alignment records 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 alignment records from. Globs/directories are supported.

    pathName2

      The path name to load the second set of unaligned alignment records 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.

39. #### 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.

40. #### def loadParquetAlignments(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): AlignmentRecordDataset

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

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

    pathName

      The path name to load alignment records 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 AlignmentRecordDataset which wraps the genomic dataset of alignment records, sequence dictionary representing reference sequences the alignment records may be aligned to, and the read group dictionary for the alignment records 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.

41. #### def loadParquetContigFragments(pathName: String, optPredicate: Option[FilterPredicate] = None, optProjection: Option[Schema] = None): NucleotideContigFragmentDataset

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

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

    pathName

      The path name to load nucleotide contig 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 NucleotideContigFragmentDataset.

42. #### 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.

43. #### 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.

44. #### 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.

45. #### 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.

46. #### 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.

47. #### 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.

48. #### def loadPartitionedParquetAlignments(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): AlignmentRecordDataset

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

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

    pathName

      The path name to load alignment 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 an AlignmentRecordDataset.

    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.

49. #### def loadPartitionedParquetContigFragments(pathName: String, regions: Iterable[ReferenceRegion] = Iterable.empty, optLookbackPartitions: Option[Int] = Some(1)): NucleotideContigFragmentDataset

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

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

    pathName

      The path name to load alignment 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 NucleotideContigFragmentDataset.

50. #### 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 alignment 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 FeatureDataset.

51. #### 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 alignment 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 GenotypeDataset.

52. #### 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.

53. #### 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 alignment 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 VariantDataset.

54. #### 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 loadContigFragments 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

      loadContigFragments

55. #### 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

56. #### def loadUnpairedFastq(pathName: String, setFirstOfPair: Boolean = false, setSecondOfPair: Boolean = false, optReadGroup: Option[String] = None, stringency: ValidationStringency = ValidationStringency.STRICT): AlignmentRecordDataset

    Load unaligned alignment records from unpaired FASTQ into an AlignmentRecordDataset.

    Load unaligned alignment records from unpaired FASTQ into an AlignmentRecordDataset.

    pathName

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

    setFirstOfPair

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

    setSecondOfPair

      If true, sets the unaligned alignment record 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 AlignmentRecordDataset.

57. #### 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.

58. #### 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

      loadParquetVariants

      loadVcf

59. #### 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.

60. #### 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.

61. #### def log: Logger

    Attributes
      protected
    Definition Classes
      Logging

62. #### def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
      protected
    Definition Classes
      Logging

63. #### def logDebug(msg: ⇒ String): Unit

    Attributes
      protected
    Definition Classes
      Logging

64. #### def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
      protected
    Definition Classes
      Logging

65. #### def logError(msg: ⇒ String): Unit

    Attributes
      protected
    Definition Classes
      Logging

66. #### def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
      protected
    Definition Classes
      Logging

67. #### def logInfo(msg: ⇒ String): Unit

    Attributes
      protected
    Definition Classes
      Logging

68. #### def logName: String

    Attributes
      protected
    Definition Classes
      Logging

69. #### def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
      protected
    Definition Classes
      Logging

70. #### def logTrace(msg: ⇒ String): Unit

    Attributes
      protected
    Definition Classes
      Logging

71. #### def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
      protected
    Definition Classes
      Logging

72. #### def logWarning(msg: ⇒ String): Unit

    Attributes
      protected
    Definition Classes
      Logging

73. #### final def ne(arg0: AnyRef): Boolean

    Definition Classes
      AnyRef

74. #### final def notify(): Unit

    Definition Classes
      AnyRef

75. #### final def notifyAll(): Unit

    Definition Classes
      AnyRef

76. #### val sc: SparkContext

    The SparkContext to wrap.

77. #### final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
      AnyRef

78. #### def toString(): String

    Definition Classes
      AnyRef → Any

79. #### final def wait(): Unit

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

80. #### final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

81. #### final def wait(arg0: Long): Unit

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

### Inherited from Logging

### Inherited from Serializable

### Inherited from AnyRef

### Inherited from Any

### Ungrouped