What it does

This Galaxy tool wraps the bwa-mem module of the BWA read mapping tool. For more details about the different modules of the BWA package see the BWA manual.

The Galaxy implementation takes fastq files as input and produces output in BAM format, which can be further processed using various BAM utilities existing in Galaxy (BAMTools, SAMTools, Picard).

From http://arxiv.org/abs/1303.3997:

BWA-MEM is an alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases.

Indices: Selecting reference genomes for BWA

The Galaxy wrapper for BWA allows you to select between precomputed and user-defined indices for reference genomes using the Will you select a reference genome from your history or use a built-in index? select box.

This select box has two options:

1. Use a built-in genome index

   With this option (which is the default), Galaxy provides you with a dropdown select menu populated with genomes that have been pre-indexed with the bwa index utility and are ready to map sequenced reads against.

   The collection of pre-indexed genomes is managed by the administrators of your Galaxy instance. If your genome of interest is missing and its impractical to use the second option below to work with it, consider contacting the support team for the Galaxy server you are working on to let them know that you would like to have an additional genome indexed.

2. Use a genome from history and build index

   With this option, Galaxy provides you with a dropdown select menu populated with all FASTA formatted files listed in your current history. If you have uploaded your genome of interest into your history it will be shown there.

   Selecting a genome from this dropdown will cause Galaxy to index it transparently first using the bwa index command, and then map against it with bwa aln.

Analysis modes

The tool supports different preconfigured analysis modes optimized for different types of input data. Alternatively, it allows you to take full control over all available options.

The preconfigured modes are:

1. Simple Illumina mode

   This corresponds to the simplest possible and standard bwa mem application in which it aligns single or paired-end data to a reference using default parameters. It is equivalent to the following command: bwa mem <reference index> <fastq dataset1> [fastq dataset2]

2. PacBio mode

   This mode is adjusted specifically for mapping of long PacBio subreads. It is running bwa mame with the -x pacbio option.

3. Nanopore 2D-reads mode

   This mode is running bwa mem with the -x ont2d option.

4. Intra-sepcies contigs mode

   This mode is running bwa mem with the -x intractg option.

Please note: minimap2 is recommended over and outperforms BWA-MEM for most types of input data except for Illumina short reads. For Illumina short-read mapping you may also consider using BWA-MEM2, which is about twice as fast as BWA-MEM.

Bam sorting mode

The generated bam files can be sorted according to three criteria: coordinates, names and input order.

In coordinate sorted mode the reads are sorted by coordinates. It means that the reads from the beginning of the first chromosome are first in the file.

When sorted by read name, the file is sorted by the reference ID (i.e., the QNAME field).

Finally, the No sorted (sorted as input) option yield a BAM file in which the records are sorted in an order corresponding to the order of the reads in the original input file. This option requires using a single thread to perform the conversion from SAM to BAM format, so the runtime is extended.

Read Groups are Important!

One of the recommended best practices in NGS analysis is adding read group information to BAM files. You can do this directly in BWA interface using the Specify read group information? widget. If you are not familiar with read groups you shold know that this is effectively a way to tag reads with an additional ID. This allows you to combine BAM files from, for example, multiple BWA runs into a single dataset. This significantly simplifies downstream processing as instead of dealing with multiple datasets you only have to handle only one. This is possible because the read group information allows you to identify data from different experiments even if they are combined in one file. Many downstream analysis tools such as variant callers (e.g., FreeBayes or Naive Variant Caller present in Galaxy) are aware of read groups and will automatically generate calls for each individual sample even if they are combined within a single file.

Description of read groups fields

(from GATK FAQ webpage):

Example of Read Group usage

Suppose we have a trio of samples: MOM, DAD, and KID. Each has two DNA libraries prepared, one with 400 bp inserts and another with 200 bp inserts. Each of these libraries is run on two lanes of an illumina hiseq, requiring 3 x 2 x 2 = 12 lanes of data. When the data come off the sequencer, we would create 12 BAM files, with the following @RG fields in the header:

Dad's data:
@RG     ID:FLOWCELL1.LANE1      PL:illumina     LB:LIB-DAD-1 SM:DAD      PI:200
@RG     ID:FLOWCELL1.LANE2      PL:illumina     LB:LIB-DAD-1 SM:DAD      PI:200
@RG     ID:FLOWCELL1.LANE3      PL:illumina     LB:LIB-DAD-2 SM:DAD      PI:400
@RG     ID:FLOWCELL1.LANE4      PL:illumina     LB:LIB-DAD-2 SM:DAD      PI:400

Mom's data:
@RG     ID:FLOWCELL1.LANE5      PL:illumina     LB:LIB-MOM-1 SM:MOM      PI:200
@RG     ID:FLOWCELL1.LANE6      PL:illumina     LB:LIB-MOM-1 SM:MOM      PI:200
@RG     ID:FLOWCELL1.LANE7      PL:illumina     LB:LIB-MOM-2 SM:MOM      PI:400
@RG     ID:FLOWCELL1.LANE8      PL:illumina     LB:LIB-MOM-2 SM:MOM      PI:400

Kid's data:
@RG     ID:FLOWCELL2.LANE1      PL:illumina     LB:LIB-KID-1 SM:KID      PI:200
@RG     ID:FLOWCELL2.LANE2      PL:illumina     LB:LIB-KID-1 SM:KID      PI:200
@RG     ID:FLOWCELL2.LANE3      PL:illumina     LB:LIB-KID-2 SM:KID      PI:400
@RG     ID:FLOWCELL2.LANE4      PL:illumina     LB:LIB-KID-2 SM:KID      PI:400

Note the hierarchical relationship between read groups (unique for each lane) to libraries (sequenced on two lanes) and samples (across four lanes, two lanes for each library).