A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
Current genomic file formats are not designed for distributed processing. ADAM addresses this by explicitly defining data formats as Apache Avro objects and storing them in Parquet files. Apache Spark is used as the cluster execution system.
The Sequencing Alignment Map (SAM) and Binary Alignment Map (BAM) file specification defines a data format for storing reads from aligners. The specification is well-written but provides no tools for developers to implement the format. Developers have to hand-craft source code to encode and decode the records which is error prone and an unneccesary hassle.
In contrast, the [ADAM specification for storing reads] (https://github.com/bigdatagenomics/bdg-formats/blob/master/src/main/resources/avro/bdg.avdl) is defined in the Avro Interface Description Language (IDL) which is directly converted into source code. Avro supports a number of computer languages. ADAM uses Java; you could just as easily use this Avro IDL description as the basis for a Python project. Avro currently supports c, c++, csharp, java, javascript, php, python and ruby.
The SAM/BAM format is record-oriented with a single record for each read. However, the typical data access pattern is column oriented, e.g. search for bases at a specific position in a reference genome. The BAM specification tries to support this pattern by defining a format for a separate index file. However, this index needs to be regenerated anytime your BAM file changes which is costly. The index does help keep the cost down on file seeks but the columnar store ADAM uses reduces the cost of seeks even more.
Once you convert your BAM file to ADAM, it can be directly accessed by Hadoop Map-Reduce, Spark, Shark, Impala, Pig, Hive, whatever. Using ADAM will unlock your genomic data and make it available to a broader range of systems.
You will need to have Maven installed in order to build ADAM.
Note: The default configuration is for Hadoop 2.2.0. If building against a different version of Hadoop, please edit the build configuration in the
<properties>section of thepom.xmlfile.
$ git clone https://github.com/bigdatagenomics/adam.git
$ cd adam
$ export MAVEN_OPTS="-Xmx512m -XX:MaxPermSize=128m"
$ mvn clean package -DskipTests
...
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 9.647s
[INFO] Finished at: Thu May 23 15:50:42 PDT 2013
[INFO] Final Memory: 19M/81M
[INFO] ------------------------------------------------------------------------
You might want to take a peek at the scripts/jenkins-test script and give it a run. It will fetch a mouse chromosome, encode it to ADAM
reads and pileups, run flagstat, etc. We use this script to test that ADAM is working correctly.
ADAM is packaged via appassembler and includes all necessary dependencies
You might want to add the following to your .bashrc to make running adam easier:
alias adam-submit="${ADAM_HOME}/bin/adam-submit"
alias adam-shell="${ADAM_HOME}/bin/adam-shell"
$ADAM_HOME should be the path to where you have checked ADAM out on your local filesystem.
The first alias should be used for running ADAM jobs that operate locally. The latter two aliases
call scripts that wrap the spark-submit and spark-shell commands to set up ADAM. You'll need
to have the Spark binaries on your system; prebuilt binaries can be downloaded from the
Spark website. Currently, we build for
Spark 1.1, and Hadoop 2.3 (CDH5).
Once this alias is in place, you can run adam by simply typing adam-submit at the commandline, e.g.
$ adam-submit
e 888~-_ e e e
d8b 888 \ d8b d8b d8b
/Y88b 888 | /Y88b d888bdY88b
/ Y88b 888 | / Y88b / Y88Y Y888b
/____Y88b 888 / /____Y88b / YY Y888b
/ Y88b 888_-~ / Y88b / Y888b
Choose one of the following commands:
transform : Convert SAM/BAM to ADAM format and optionally perform read pre-processing transformations
flagstat : Print statistics on reads in an ADAM file (similar to samtools flagstat)
reads2ref : Convert an ADAM read-oriented file to an ADAM reference-oriented file
mpileup : Output the samtool mpileup text from ADAM reference-oriented data
print : Print an ADAM formatted file
aggregate_pileups : Aggregate pileups in an ADAM reference-oriented file
listdict : Print the contents of an ADAM sequence dictionary
compare : Compare two ADAM files based on read name
compute_variants : Compute variant data from genotypes
bam2adam : Single-node BAM to ADAM converter (Note: the 'transform' command can take SAM or BAM as input)
adam2vcf : Convert an ADAM variant to the VCF ADAM format
vcf2adam : Convert a VCF file to the corresponding ADAM format
ADAM outputs all the commands that are available for you to run. To get
help for a specific command, run adam-submit <command> without any additional arguments.
$ adam-submit transform
Argument "INPUT" is required
INPUT : The ADAM, BAM or SAM file to apply the transforms to
OUTPUT : Location to write the transformed data in ADAM/Parquet format
-coalesce N : Set the number of partitions written to the ADAM output directory
-dump_observations VAL : Local path to dump BQSR observations to. Outputs CSV format.
-h (-help, --help, -?) : Print help
-known_indels VAL : VCF file including locations of known INDELs. If none is provided, default
consensus model will be used.
-known_snps VAL : Sites-only VCF giving location of known SNPs
-log_odds_threshold N : The log-odds threshold for accepting a realignment. Default value is 5.0.
-mark_duplicate_reads : Mark duplicate reads
-max_consensus_number N : The maximum number of consensus to try realigning a target region to. Default
value is 30.
-max_indel_size N : The maximum length of an INDEL to realign to. Default value is 500.
-max_target_size N : The maximum length of a target region to attempt realigning. Default length is
3000.
-parquet_block_size N : Parquet block size (default = 128mb)
-parquet_compression_codec [UNCOMPRESSED | SNAPPY | GZIP | LZO] : Parquet compression codec
-parquet_disable_dictionary : Disable dictionary encoding
-parquet_logging_level VAL : Parquet logging level (default = severe)
-parquet_page_size N : Parquet page size (default = 1mb)
-print_metrics : Print metrics to the log on completion
-qualityBasedTrim : Trims reads based on quality scores of prefix/suffixes across read group.
-qualityThreshold N : Phred scaled quality threshold used for trimming. If omitted, Phred 20 is used.
-realign_indels : Locally realign indels present in reads.
-recalibrate_base_qualities : Recalibrate the base quality scores (ILLUMINA only)
-repartition N : Set the number of partitions to map data to
-sort_fastq_output : Sets whether to sort the FASTQ output, if saving as FASTQ. False by default.
Ignored if not saving as FASTQ.
-sort_reads : Sort the reads by referenceId and read position
-trimBeforeBQSR : Performs quality based trim before running BQSR. Default is to run quality based
trim after BQSR.
-trimFromEnd N : Trim to be applied to end of read.
-trimFromStart N : Trim to be applied to start of read.
-trimReadGroup VAL : Read group to be trimmed. If omitted, all reads are trimmed.
-trimReads : Apply a fixed trim to the prefix and suffix of all reads/reads in a specific read
group.
If you followed along above, now try making your first .adam file like this:
adam-submit transform $ADAM_HOME/adam-core/src/test/resources/small.sam /tmp/small.adam
... and if you didn't obtain your copy of adam from github, you can grab small.sam from here.
Once you have data converted to ADAM, you can gather statistics from the ADAM file using flagstat.
This command will output stats identically to the samtools flagstat command.
If you followed along above, now try gathering some statistics:
$ adam-submit flagstat /tmp/small.adam
20 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 primary duplicates
0 + 0 primary duplicates - both read and mate mapped
0 + 0 primary duplicates - only read mapped
0 + 0 primary duplicates - cross chromosome
0 + 0 secondary duplicates
0 + 0 secondary duplicates - both read and mate mapped
0 + 0 secondary duplicates - only read mapped
0 + 0 secondary duplicates - cross chromosome
20 + 0 mapped (100.00%:0.00%)
0 + 0 paired in sequencing
0 + 0 read1
0 + 0 read2
0 + 0 properly paired (0.00%:0.00%)
0 + 0 with itself and mate mapped
0 + 0 singletons (0.00%:0.00%)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5)
In practice, you'll find that the ADAM flagstat command takes orders of magnitude less
time than samtools to compute these statistics. For example, on a MacBook Pro
flagstat NA12878_chr20.bam took 17 seconds to run while samtools flagstat NA12878_chr20.bam
took 55 seconds. On larger files, the difference in speed is even more dramatic. ADAM is faster
because it's multi-threaded and distributed and uses a columnar storage format (with a
projected schema that only materializes the read flags instead of the whole read).
You can also use ADAM to count all K-mers present across all reads in the
.adam file using count_kmers. Try this:
$ adam-submit count_kmers /tmp/small.adam /tmp/kmers.adam 10
$ head /tmp/kmers.adam/part-*
TTTTAAGGTT, 1
TTCCGATTTT, 1
GAGCAGCCTT, 1
CCTGCTGTAT, 1
AATTGGCACT, 1
GGCCAGGACT, 1
GCAGTCCCTC, 1
AACTTTGAAT, 1
GATGACGTGG, 1
CTGTCCCTGT, 1
Each line contains part-* file(s) with line-based records that contain two comma-delimited values. The first value is the K-mer itself and the second value is the number of times that K-mer occurred in the input file.
We provide the adam-submit and adam-shell commands under the bin directory. These can
be used to submit ADAM jobs to a spark cluster, or to run ADAM interactively.
ADAM allows users to create plugins via the ADAMPlugin
trait. These plugins are then imported using the Java classpath at runtime. To add to the classpath when
using appassembler, use the $CLASSPATH_PREFIX environment variable. For an example of how to use
the plugin interface, please see the adam-plugins repo.
The ADAM mailing list is a good
way to sync up with other people who use ADAM including the core developers. You can subscribe
by sending an email to [email protected] or just post using
the web forum page.
A lot of the developers are hanging on the #adamdev freenode.net channel. Come join us and ask questions.
ADAM is released under an Apache 2.0 license.
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