This fork of ADAM contains SHAs that are depended on by various versions of hammerlab/pageant.
To checkout and install a given commit, e.g. c547d9c:
$ ./install-sha c547d9c98ca5fe239de6cead18633c5257a26c5f
# sets all POM versions to the current SHA, and installs it.
$ ./install-sha
The install-sha script basically does:
$ git checkout c547d9c98ca5fe239de6cead18633c5257a26c5f
$ mvn versions:set -DgenerateBackupPoms=false -DnewVersion=c547d9c98ca5fe239de6cead18633c5257a26c5f
$ mvn install -Dmaven.test.skip -DskipTests -Plocal
- Follow our Twitter account at https://twitter.com/bigdatagenomics/
- Chat with ADAM developers at https://gitter.im/bigdatagenomics/adam
- Join our mailing list at http://bdgenomics.org/mail
- Checkout the current build status at https://amplab.cs.berkeley.edu/jenkins/
- Download official releases at https://github.com/bigdatagenomics/adam/releases
- View our software artifacts on Maven Central at http://search.maven.org/#search%7Cga%7C1%7Corg.bdgenomics
- See our snapshots at https://oss.sonatype.org/index.html#nexus-search;quick~bdgenomics
- Look at our CHANGES file at https://github.com/bigdatagenomics/adam/blob/master/CHANGES.md
ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
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.
For example, the following code snippet will print the top 10 21-mers in NA2114 from 1000 Genomes.
val ac = new ADAMContext(sc)
// Load alignments from disk
val reads = ac.loadAlignments(
"/data/NA21144.chrom11.ILLUMINA.adam",
predicate = Some(classOf[ExamplePredicate]),
projection = Some(Projection(
AlignmentRecordField.sequence,
AlignmentRecordField.readMapped,
AlignmentRecordField.mapq)))
// Generate, count and sort 21-mers
val kmers = reads.flatMap { read =>
read.getSequence.sliding(21).map(k => (k, 1L))
}.reduceByKey((k1: Long, k2: Long) => k1 + k2)
.map(_.swap)
.sortByKey(ascending = false)
// Print the top 10 most common 21-mers
kmers.take(10).foreach(println)Executing this Spark job will output the following:
(121771,TTTTTTTTTTTTTTTTTTTTT)
(44317,ACACACACACACACACACACA)
(44023,TGTGTGTGTGTGTGTGTGTGT)
(42474,CACACACACACACACACACAC)
(42095,GTGTGTGTGTGTGTGTGTGTG)
(33797,TAATCCCAGCACTTTGGGAGG)
(33081,AATCCCAGCACTTTGGGAGGC)
(32775,TGTAATCCCAGCACTTTGGGA)
(32484,CCTCCCAAAGTGCTGGGATTA)
You don't need to be Scala developer to use ADAM. You could also run the following ADAM CLI command for the same result:
$ adam-submit count_kmers \
/data/NA21144.chrom11.ILLUMINA.adam \
/data/results.txt 21Apache 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 (http://avro.apache.org)
- 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.
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
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
compare : Compare two ADAM files based on read name
findreads : Find reads that match particular individual or comparative criteria
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.
transform : Convert SAM/BAM to ADAM format and optionally perform read pre-processing transformations
adam2fastq : Convert BAM to FASTQ files
plugin : Executes an ADAMPlugin
CONVERSION OPERATIONS
bam2adam : Single-node BAM to ADAM converter (Note: the 'transform' command can take SAM or BAM as input)
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.
reads2ref : Convert an ADAM read-oriented file to an ADAM reference-oriented file
mpileup : Output the samtool mpileup text from ADAM reference-oriented data
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)
viz : Generates images from sections of the genome
print_tags : Prints the values and counts of all tags in a set of records
listdict : Print the contents of an ADAM sequence dictionary
summarize_genotypes : Print statistics of genotypes and variants in an ADAM file
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 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.
The ADAM transform command allows you to mark duplicates, run base quality score recalibration (BQSR) and other pre-processing steps on your data.
There are also 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)
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.
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.
ADAM is released under an Apache 2.0 license.