class ADAMContext extends Serializable with Logging

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

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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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  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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  23. def isDebugEnabled: Boolean
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  24. def isErrorEnabled: Boolean
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  25. def isInfoEnabled: 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
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  29. def isWarnEnabled: Boolean
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  30. 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

    loadBam

    loadFastq

    loadFasta

    loadInterleavedFastq

    loadParquetAlignments

  31. 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.

  32. 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.

  33. 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

    loadFasta

    loadParquetContigFragments

  34. 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

  35. 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.

  36. 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

    loadPairedFastq

    loadUnpairedFastq

  37. 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

  38. 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

  39. 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

  40. 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.

  41. 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.

  42. 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.

  43. 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.

  44. 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.

  45. 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.

  46. 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.

  47. 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.

  48. 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.

  49. 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.

  50. 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.

  51. 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.

  52. 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.

  53. 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.

  54. 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.

  55. 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.

  56. 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.

  57. 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.

  58. 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.

  59. 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.

  60. 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.

  61. 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.

  62. 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.

  63. 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.

  64. 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.

  65. 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.

  66. 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.

  67. 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

  68. 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

  69. 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.

  70. 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.

  71. 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

  72. 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.

  73. 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.

  74. def logger: Logger
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  75. def loggerName: String
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  76. final def ne(arg0: AnyRef): Boolean
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  79. val sc: SparkContext
  80. final def synchronized[T0](arg0: ⇒ T0): T0
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  81. def toString(): String
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  82. def trace(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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  83. def trace(msg: ⇒ Any, t: ⇒ Throwable): Unit
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  85. final def wait(): Unit
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  86. final def wait(arg0: Long, arg1: Int): Unit
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  87. final def wait(arg0: Long): Unit
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  88. def warn(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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  89. def warn(msg: ⇒ Any, t: ⇒ Throwable): Unit
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  90. def warn(msg: ⇒ Any): Unit
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