Using ADAM’s Pipe API

ADAM’s GenomicDataset API provides support for piping the underlying genomic data out to a single node process through the use of a pipe API. This builds off of Apache Spark’s RDD.pipe API. However, RDD.pipe prints the objects as strings to the pipe. ADAM’s pipe API adds several important functions:

  • It supports on-the-fly conversions to widely used genomic file formats
  • It does not require input/output type matching (i.e., you can pipe reads in and get variants back from the pipe)
  • It adds the ability to set environment variables and to make local files (e.g., a reference genome) available to the run command
  • If the data are aligned, we ensure that each subcommand runs over a contiguous section of the reference genome, and that data are sorted on this chunk. We provide control over the size of any flanking region that is desired.

The method signature of a pipe command is below:

def pipe[X, Y <: GenomicDataset[X, Y], V <: InFormatter[T, U, V]](cmd: Seq[String],
                                                              files: Seq[String] = Seq.empty,
                                                              environment: Map[String, String] = Map.empty,
                                                              flankSize: Int = 0)(implicit tFormatterCompanion: InFormatterCompanion[T, U, V],
                                                                                  xFormatter: OutFormatter[X],
                                                                                  convFn: (U, RDD[X]) => Y,
                                                                                  tManifest: ClassTag[T],
                                                                                  xManifest: ClassTag[X]): Y

X is the type of the records that are returned (e.g., for reads, Alignment) and Y is the type of the GenomicDatset that is returned (e.g., for reads, AlignmentDataset). As explicit parameters, we take:

  • cmd: The command to run.
  • files: Files to make available locally to each running command. These files can be referenced from cmd by using $# syntax, where # is the number of the file in the files sequence (e.g., $0 is the head of the list, $1 is the second file in the list, and so on).
  • environment: Environment variable/value pairs to set locally for each running command.
  • flankSize: The number of base pairs to flank each partition by, if piping genome aligned data.

Additionally, we take several important implicit parameters:

  • tFormatter: The InFormatter that converts the data that is piped into the run command from the underlying GenomicDataset type.
  • xFormatter: The OutFormatter that converts the data that is piped out of the run command back to objects for the output GenomicDataset.
  • convFn: A function that applies any necessary metadata conversions and creates a new GenomicDataset.

The tManifest and xManifest implicit parameters are Scala ClassTags and will be provided by the compiler.

What are the implicit parameters used for? For each of the genomic datatypes in ADAM, we support multiple legacy genomic filetypes (e.g., reads can be saved to or read from BAM, CRAM, FASTQ, and SAM). The InFormatter and OutFormatter parameters specify the format that is being read into or out of the pipe. We support the following:

  • AlignmentDataset:
    • InFormatters: SAMInFormatter and BAMInFormatter write SAM or BAM out to a pipe.
    • OutFormatter: AnySAMOutFormatter supports reading SAM and BAM from a pipe, with the exact format autodetected from the stream.
    • We do not support piping CRAM due to complexities around the reference-based compression.
  • FeatureDataset:
    • InFormatters: BEDInFormatter, GFF3InFormatter, GTFInFormatter, and NarrowPeakInFormatter for writing features out to a pipe in BED, GFF3, GTF/GFF2, or NarrowPeak format, respectively.
    • OutFormatters: BEDOutFormatter, GFF3OutFormatter, GTFOutFormatter, and NarrowPeakInFormatter for reading features in BED, GFF3, GTF/GFF2, or NarrowPeak format in from a pipe, respectively.
  • FragmentDataset:
    • InFormatter: InterleavedFASTQInFormatter writes FASTQ with the reads from a paired sequencing protocol interleaved in the FASTQ stream to a pipe.
  • VariantContextDataset: - InFormatter: VCFInFormatter writes VCF to a pipe. - OutFormatter: VCFOutFormatter reads VCF from a pipe.

The convFn implementations are provided as implicit values in the ADAMContext. These conversion functions are needed to adapt the metadata stored in a single GenomicDataset to the type of a different GenomicDataset (e.g., if piping an AlignmentDataset through a command that returns a VariantContextDataset, we will need to convert the AlignmentDatasets ReadGroupDictionary into an array of Samples for the VariantContextDataset). We provide four implementations:

  • ADAMContext.sameTypeConversionFn: For piped commands that do not change the type of the GenomicDataset (e.g., AlignmentDatasetAlignmentDataset).
  • ADAMContext.readsToVCConversionFn: For piped commands that go from an AlignmentDataset to a VariantContextDataset.
  • ADAMContext.fragmentsToReadsConversionFn: For piped commands that go from a FragmentDataset to an AlignmentDataset.

To put everything together, here is an example command. Here, we will run a command my_variant_caller, which accepts one argument -R <reference>.fa, SAM on standard input, and outputs VCF on standard output:

// import genomic dataset load functions and conversion functions
import org.bdgenomics.adam.ds.ADAMContext._

// import functionality for piping SAM into pipe

// import functionality for reading VCF from pipe
import org.bdgenomics.adam.converters.DefaultHeaderLines
import org.bdgenomics.adam.ds.variant.{

// load the reads
val reads = sc.loadAlignments("hdfs://mynamenode/my/read/file.bam")

// define implicit informatter for sam
implicit val tFormatter = SAMInFormatter

// define implicit outformatter for vcf
// attach all default headerlines
implicit val uFormatter = new VCFOutFormatter(DefaultHeaderLines.allHeaderLines)

// run the piped command
// providing the explicit return type (VariantContextDataset) will ensure that
// the correct implicit convFn is selected
val variantContexts: VariantContextDataset = reads.pipe(
  cmd = Seq("my_variant_caller", "-R", "$0"),
  files = Seq("hdfs://mynamenode/my/reference/genome.fa"))

// save to vcf

In this example, we assume that my_variant_caller is on the PATH on each machine in our cluster. We suggest several different approaches:

  • Install the executable on the local filesystem of each machine on your cluster.
  • Install the executable on a shared file system (e.g., NFS) that is accessible from every machine in your cluster, and make sure that necessary prerequisites (e.g., python, dynamically linked libraries) are installed across each node on your cluster.
  • Run the command using a container system such as Docker or Singularity.

Using the Pipe API from Java

The pipe API example above uses Scala’s implicit system and type inference to make it easier to use the pipe API. However, we also provide a Java equivalent. There are several changes:

  • The out-formatter is provided explicitly.
  • Instead of implicitly providing the companion object for the in-formatter, you provide the class of the in-formatter. This allows us to access the companion object via reflection.
  • For the conversion function, you can provide any function that implements the interface. We provide common functions equivalent to those in ADAMContext in

To run the Scala example code above using Java, we would write:

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.bdgenomics.adam.models.VariantContext
import org.bdgenomics.adam.ds.variant.VariantContextDataset;
import org.bdgenomics.adam.ds.variant.VCFOutFormatter;

class PipeRunner {

  VariantContextDataset runPipe(AlignmentDataset reads) {

    List<String> cmd = new ArrayList<String>();

    List<String> files = new ArrayList<String>();

    Map<String, String> env = new HashMap<String, String>();

    return reads.pipe<VariantContext,
                                      new VCFOutFormatter,
                                      new AlignmentToVariantContextConverter);

Using the Pipe API from Python/R

Python and R follow the same calling style as the Java pipe API, but the in/out-formatter and conversion functions are passed by name. We then use the classnames that are passed to the function to create the objects via reflection. To run the example code from above in Python, we would write:

from bigdatagenomics.adam.adamContext import ADAMContext

ac = ADAMContext(
reads = ac.loadAlignments("hdfs://mynamenode/my/read/file.bam")

variants = reads.pipe(["my_variant_caller", "-R", "$0"],
                      files=[ "hdfs://mynamenode/my/reference/genome.fa" ])

In R, we would write:


ac <- ADAMContext(sc)

reads <- loadAlignments(ac, "hdfs://mynamenode/my/read/file.bam")

cmd <- list("my_variant_caller", "-R", "$0")
files <- list("hdfs://mynamenode/my/reference/genome.fa")

variants <- pipe(reads,