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This article is part of the Best Practices workflow document. See http://www.broadinstitute.org/gatk/guide/best-practices for the full workflow.

The Best Practices variant discovery workflow depends on having sequence data in the form of reads that are aligned to a reference genome. So the very first step is of course to map your reads to the reference to produce a file in SAM/BAM format. We recommend using BWA, but depending on your data and how it was sequenced, you may need to use a different aligner. Once you have mapped the reads, you'll need to make sure they are sorted in the proper order (by coordinate).

Then you can proceed to mark duplicates. The rationale here is that during the sequencing process, the same DNA molecules can be sequenced several times. The resulting duplicate reads are not informative and should not be counted as additional evidence for or against a putative variant. The duplicate marking process (sometimes called **dedupping** in bioinformatics slang) identifies these reads as such so that the GATK tools know they should ignore them.

These steps are performed with tools such as Samtools and Picard that are not part of GATK, so we don't provide detailed documentation of all the options available. For more details, please see those tools' respective documentations.

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Objective

Map the read data to the reference and mark duplicates.

Prerequisites

  • TBD

Steps

  1. Identify read group information
  2. Generate a SAM file containing aligned reads
  3. Convert to BAM, sort and mark duplicates

1. Identify read group information

The read group information is key for downstream GATK functionality. The GATK will not work without a read group tag. Make sure to enter as much metadata as you know about your data in the read group fields provided. For more information about all the possible fields in the @RG tag, take a look at the SAM specification.

Action

Compose the read group identifier in the following format:

@RG\tID:group1\tSM:sample1\tPL:illumina\tLB:lib1\tPU:unit1 

where the \t stands for the tab character.


2. Generate a SAM file containing aligned reads

Action

Run the following BWA command:

In this command, replace read group info by the read group identifier composed in the previous step.

bwa mem -M -R ’<read group info>’ -p reference.fa raw_reads.fq > aligned_reads.sam 

replacing the <read group info> bit with the read group identifier you composed at the previous step.

The -M flag causes BWA to mark shorter split hits as secondary (essential for Picard compatibility).

Expected Result

This creates a file called aligned_reads.sam containing the aligned reads from all input files, combined, annotated and aligned to the same reference.

Note that here we are using a command that is specific for pair ended data in an interleaved fastq file, which is what we are providing to you as a tutorial file. To map other types of datasets (e.g. single-ended or pair-ended in forward/reverse read files) you will need to adapt the command accordingly. Please see the BWA documentation for exact usage and more options for these commands.


3. Convert to BAM, sort and mark duplicates

These initial pre-processing operations format the data to suit the requirements of the GATK tools.

Action

Run the following Picard command to sort the SAM file and convert it to BAM:

java -jar SortSam.jar \ 
    INPUT=aligned_reads.sam \ 
    OUTPUT=sorted_reads.bam \ 
    SORT_ORDER=coordinate 

Expected Results

This creates a file called sorted_reads.bam containing the aligned reads sorted by coordinate.

Action

Run the following Picard command to mark duplicates:

java -jar MarkDuplicates.jar \ 
    INPUT=sorted_reads.bam \ 
    OUTPUT=dedup_reads.bam \
    METRICS_FILE=metrics.txt

Expected Result

This creates a sorted BAM file called dedup_reads.bam with the same content as the input file, except that any duplicate reads are marked as such. It also produces a metrics file called metrics.txt containing (can you guess?) metrics.

Action

Run the following Picard command to index the BAM file:

java -jar BuildBamIndex.jar \ 
    INPUT=dedup_reads.bam 

Expected Result

This creates an index file for the BAM file called dedup_reads.bai.

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Hi all,

My question is on bwa software when one want to map RNA-seq data on the entire human genome. What should be the specific settings to use to get maximum mapping? Should it be effective if no options are used in the command line?

Thank you for your time

Comments (4)

Hi, Does GATK2 provide a walker/option to summarize the read alignment in a given BAM file? The summary including total reads, reads mapped/%, reads uniquely mapped/%, reads uniquely mapped with 0mm/%, reads mapped on-target/%, reads uniquely mapped on-target%, etc is of great use to assess the mapping quality for whole genome or targeted analysis. Please advice me on how I can obtain this using any of the walkers available. Thanks, Raj