ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark and Apache Parquet. Apache 2 licensed. Some quick links:
- Follow our Twitter account.
- Chat with ADAM developers in Gitter.
- Join our mailing list.
- Checkout the current build status.
- Download official releases.
- View our software artifacts on Maven Central (…including snapshots).
- Look at our CHANGES file.
Here's an example ADAM CLI command that will count 10-mers in a test .sam file that lives in this repository:
$ adam-submit count_kmers /tmp/small.adam /tmp/kmers.adam 10
$ head /tmp/kmers.adam/part-*
(AATTGGCACT,1)
(TTCCGATTTT,1)
(GAGCAGCCTT,1)
(CCTGCTGTAT,1)
(TTTTAAGGTT,1)
(GGCCAGGACT,1)
(GCAGTCCCTC,1)
(AACTTTGAAT,1)
(GATGACGTGG,1)
(CTGTCCCTGT,1)ADAM does much more than just k-mer counting. Running the ADAM CLI without arguments or with --help will display available commands, 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:
ADAM ACTIONS
depth : Calculate the depth from a given ADAM file, at each variant in a VCF
count_kmers : Counts the k-mers/q-mers from a read dataset.
count_contig_kmers : Counts the k-mers/q-mers from a read dataset.
transform : Convert SAM/BAM to ADAM format and optionally perform read pre-processing transformations
adam2fastq : Convert BAM to FASTQ files
plugin : Executes an ADAMPlugin
flatten : Convert a ADAM format file to a version with a flattened schema, suitable for querying with tools like Impala
CONVERSION OPERATIONS
vcf2adam : Convert a VCF file to the corresponding ADAM format
anno2adam : Convert a annotation file (in VCF format) to the corresponding ADAM format
adam2vcf : Convert an ADAM variant to the VCF ADAM format
fasta2adam : Converts a text FASTA sequence file into an ADAMNucleotideContig Parquet file which represents assembled sequences.
features2adam : Convert a file with sequence features into corresponding ADAM format
wigfix2bed : Locally convert a wigFix file to BED format
PRINT
print : Print an ADAM formatted file
print_genes : Load a GTF file containing gene annotations and print the corresponding gene models
flagstat : Print statistics on reads in an ADAM file (similar to samtools flagstat)
print_tags : Prints the values and counts of all tags in a set of records
listdict : Print the contents of an ADAM sequence dictionary
allelecount : Calculate Allele frequencies
buildinfo : Display build information (use this for bug reports)
view : View certain reads from an alignment-record file.
You can learn more about a command, by calling it without arguments or with --help, e.g.
$ 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.
-force_load_bam : Forces Transform to load from BAM/SAM.
-force_load_fastq : Forces Transform to load from unpaired FASTQ.
-force_load_ifastq : Forces Transform to load from interleaved FASTQ.
-force_load_parquet : Forces Transform to load from Parquet.
-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
-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
The ADAM transform command allows you to mark duplicates, run base quality score recalibration (BQSR) and other pre-processing steps on your data.
Bundled release binaries can be found on our releases page.
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=256m"
$ 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.
You'll need to have a Spark release on your system and the $SPARK_HOME environment variable pointing at it; prebuilt binaries can be downloaded from the
Spark website. Currently, our continuous builds use
Spark 1.1.0 built against Hadoop 2.3 (CDH5), but any more recent Spark distribution should also work.
You might want to add the following to your .bashrc to make running ADAM easier:
alias adam-submit="${ADAM_HOME}/bin/adam-submit"
alias adam-shell="${ADAM_HOME}/bin/adam-shell"
$ADAM_HOME should be the path to a binary release or a clone of this repository on your local filesystem.
These aliases call scripts that wrap the spark-submit and spark-shell commands to set up ADAM.Once they are in place, you can run adam by simply typing adam-submit at the command line, as demonstrated above.
Now you can try running some simple ADAM commands:
Make your first .adam file like this:
adam-submit transform $ADAM_HOME/adam-core/src/test/resources/small.sam /tmp/small.adam
If you didn't obtain your copy of adam from github, you can grab small.sam 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).
The adam-shell command opens an interpreter that you can run ad-hoc ADAM commands in.
For example, the following code snippet will generate a result similar to the k-mer-counting example above, but with the k-mers sorted in descending order of their number of occurrences:
$ adam-shell
…
scala> :paste
// Entering paste mode (ctrl-D to finish)
import org.bdgenomics.adam.rdd.ADAMContext
import org.bdgenomics.adam.projections.{AlignmentRecordField, Projection}
val ac = new ADAMContext(sc)
// Load alignments from disk
val reads = ac.loadAlignments(
"/data/NA21144.chrom11.ILLUMINA.adam",
projection = Some(
Projection(
AlignmentRecordField.sequence,
AlignmentRecordField.readMapped,
AlignmentRecordField.mapq
)
)
)
// Generate, count and sort 21-mers
val kmers =
reads
.flatMap(_.getSequence.sliding(21).map(k => (k, 1L)))
.reduceByKey(_ + _)
.map(_.swap)
.sortByKey(ascending = false)
// Print the top 10 most common 21-mers
kmers.take(10).foreach(println)
// Exiting paste mode, now interpreting.
(121771,TTTTTTTTTTTTTTTTTTTTT)
(44317,ACACACACACACACACACACA)
(44023,TGTGTGTGTGTGTGTGTGTGT)
(42474,CACACACACACACACACACAC)
(42095,GTGTGTGTGTGTGTGTGTGTG)
(33797,TAATCCCAGCACTTTGGGAGG)
(33081,AATCCCAGCACTTTGGGAGGC)
(32775,TGTAATCCCAGCACTTTGGGA)
(32484,CCTCCCAAAGTGCTGGGATTA)
…The adam-submit and adam-shell commands can also
be used to submit ADAM jobs to a Spark cluster, or to run ADAM interactively. Cluster mode can be enabled by passing the same flags you'd pass to Spark, e.g. --master yarn --deploy-mode client.
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.
ADAM relies on several open-source technologies to make genomic analyses fast and massively parallelizable…
Apache Spark allows developers to write algorithms in succinct code that can run fast locally, on an in-house cluster or on Amazon, Google or Microsoft clouds.
Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.
- Parquet compresses legacy genomic formats using standard columnar techniques (e.g. RLE, dictionary encoding). ADAM files are typically ~20% smaller than compressed BAM files.
- Parquet integrates with:
- Query engines: Hive, Impala, HAWQ, IBM Big SQL, Drill, Tajo, Pig, Presto
- Frameworks: Spark, MapReduce, Cascading, Crunch, Scalding, Kite
- Data models: Avro, Thrift, ProtocolBuffers, POJOs
- Parquet is simply a file format which makes it easy to sync and share data using tools like
distcp,rsync, etc - Parquet provides a command-line tool,
parquet.hadoop.PrintFooter, which reports useful compression statistics
In the counting k-mers example above, you can see there is a defined predicate and projection. The predicate allows rapid filtering of rows while a projection allows you to efficiently materialize only specific columns for analysis. For this k-mer counting example, we filter out any records that are not mapped or have a MAPQ less than 20 using a predicate and only materialize the Sequence, ReadMapped flag and MAPQ columns and skip over all other fields like Reference or Start position, e.g.
| Sequence | ReadMapped | MAPQ | ... | ||
|---|---|---|---|---|---|
| - | - | ... | |||
| TACTGAA | true | 30 | ... | ||
| ... |
- Apache Avro is a data serialization system.
- All Big Data Genomics schemas are published at https://github.com/bigdatagenomics/bdg-formats.
- Having explicit schemas and self-describing data makes integrating, sharing and evolving formats easier.
Our Avro schemas are directly converted into source code using Avro tools. 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.
There are a number of projects built on ADAM, e.g.
- RNAdam provides an RNA pipeline on top of ADAM with isoform quantification and fusion transcription detection
- Avocado is a variant caller built on top of ADAM for germline and somatic calling
- PacMin is an assembler for PacBio reads
- A
Mutectport is nearly feature complete - Read error correction
- a graphing and genome visualization library
- BDG-Services is a library for accessing a running Spark cluster through web-services or a Thrift- interface
- Short read assembly
- Variant filtration (train model via
MLlib)
ADAM is released under an Apache 2.0 license.