What's SparkBWA about?
SparkBWA is a tool that integrates the Burrows-Wheeler Aligner--BWA on a Apache Spark framework running on the top of Hadoop. The current version of SparkBWA (v0.2, October 2016) supports the following BWA algorithms:
All of them work with single-reads and paired-end reads.
If you use SparkBWA, please cite this article:
José M. Abuin, Juan C. Pichel, Tomás F. Pena and Jorge Amigo. "SparkBWA: Speeding Up the Alignment of High-Throughput DNA Sequencing Data". PLoS ONE 11(5), pp. 1-21, 2016.
A version for Hadoop is available here.
Since version 0.2 the project keeps a standard Maven structure. The source code is in the src/main folder. Inside it, we can find two subfolders:
- java - Here is where the Java code is stored.
- native - Here the BWA native code (C) and the glue logic for JNI is stored.
Requirements to build SparkBWA are the same than the ones to build BWA, with the only exception that the JAVA_HOME environment variable should be defined. If not, you can define it in the /src/main/native/Makefile.common file.
It is also needed to include the flag -fPIC in the Makefile of the considered BWA version. To do this, the user just need to add this option to the end of the CFLAGS variable in the BWA Makefile. Considering bwa-0.7.15, the original Makefile contains:
CFLAGS= -g -Wall -Wno-unused-function -O2
and after the change it should be:
CFLAGS= -g -Wall -Wno-unused-function -O2 -fPIC
Additionaly, and as SparkBWA is built with Maven since version 0.2, also have it in the user computer is needed.
The default way to build SparkBWA is:
git clone https://github.com/citiususc/SparkBWA.git
This will create the target folder, which will contain the jar file needed to run SparkBWA:
- SparkBWA-0.2.jar - jar file to launch with Spark.
Since version 0.2 there is no need of configuring any Spark parameter. The only requirement is that the YARN containers need to have at least 10GB of memory available (for the human genome case).
SparkBWA requires a working Hadoop cluster. Users should take into account that at least 10 GB of memory per map/YARN container are required (each map loads into memory the bwa index - refrence genome). Also, note that SparkBWA uses disk space in the /tmp directory or in the configured Hadoop or Spark temporary folder.
Here it is an example of how to execute SparkBWA using the BWA-MEM algorithm with paired-end reads. The example assumes that our index is stored in all the cluster nodes at /Data/HumanBase/ . The index can be obtained from BWA using "bwa index".
First, we get the input FASTQ reads from the 1000 Genomes Project ftp:
Next, the downloaded files should be uncompressed:
gzip -d ERR000589_1.filt.fastq.gz
gzip -d ERR000589_2.filt.fastq.gz
and uploaded to HDFS:
hdfs dfs -copyFromLocal ERR000589_1.filt.fastq ERR000589_1.filt.fastq
hdfs dfs -copyFromLocal ERR000589_2.filt.fastq ERR000589_2.filt.fastq
Finally, we can execute SparkBWA on the cluster. Again, we assume that Spark is stored at spark_dir:
spark_dir/bin/spark-submit --class com.github.sparkbwa.SparkBWA --master yarn-cluster
--driver-memory 1500m --executor-memory 10g --executor-cores 1 --verbose
--num-executors 32 SparkBWA-0.2.jar -m -r -p --index /Data/HumanBase/hg38 -n 32
-w "-R @RG\tID:foo\tLB:bar\tPL:illumina\tPU:illumina\tSM:ERR000589"
ERR000589_1.filt.fastq ERR000589_2.filt.fastq Output_ERR000589
- -m - Sequence alignment algorithm.
- -p - Use paired-end reads.
- -w "args" - Can be used to pass arguments directly to BWA (ex. "-t 4" to
specify the amount of threads to use per instance of BWA).
- --index index_prefix - Index prefix is specified. The index must be available in all the cluster nodes at the same location.
- The last three arguments are the input and output HDFS files.
After the execution, in order to move the output to the local filesystem use:
hdfs dfs -copyToLocal Output_ERR000589/* ./
In case of not using a reducer, the output will be split into several pieces (files). If we want to put it together we can use "samtools merge".
If you want to check all the available options, execute the command:
spark_dir/bin/spark-submit --class com.github.sparkbwa.SparkBWA SparkBWA-0.2.jar -h
The result is:
SparkBWA performs genomic alignment using bwa in a Hadoop/YARN cluster
usage: spark-submit --class com.github.sparkbwa.SparkBWA SparkBWA-0.2.jar
[-a | -b | -m] [-f | -k] [-h] [-i <Index prefix>] [-n <Number of
partitions>] [-p | -s] [-r] [-w <"BWA arguments">]
<FASTQ file 1> [FASTQ file 2] <SAM file output>
-h, --help Shows this help
Input FASTQ reads options:
-p, --paired Paired reads will be used as input FASTQ reads
-s, --single Single reads will be used as input FASTQ reads
-f, --hdfs The HDFS is used to perform the input FASTQ reads sort
-k, --spark the Spark engine is used to perform the input FASTQ reads sort
BWA algorithm options:
-a, --aln The ALN algorithm will be used
-b, --bwasw The bwasw algorithm will be used
-m, --mem The MEM algorithm will be used
-i, --index <Index prefix> Prefix for the index created by bwa to use - setIndexPath(string)
-n, --partitions <Number of partitions> Number of partitions to divide input - setPartitionNumber(int)
-r, --reducer The program is going to merge all the final results in a reducer phase
BWA arguments options:
-w, --bwa <"BWA arguments"> Arguments passed directly to BWA
SparkBWA should be as accurate as running BWA normally. Below are GCAT
alignment benchmarks which proves this.
* Single-reads (400 bp)
* Pair-ended reads (100 bp)
* Pair-ended reads (150 bp)
* Single-reads (100 bp)
* Pair-ended reads (250 bp)
* Single-reads (400 bp)
* Pair-ended reads (250 bp)
Frequently asked questions (FAQs)
- I can not build the tool because jni_md.h or jni.h is missing.
1. I can not build the tool because jni_md.h or jni.h is missing.
You need to set correctly your JAVA_HOME environment variable or you can set it in Makefile.common.