# Tagged with #samtools 1 documentation article | 0 announcements | 2 forum discussions

#### Objective

Install all software packages required to follow the GATK Best Practices.

#### Prerequisites

To follow these instructions, you will need to have a basic understanding of the meaning of the following words and command-line operations. If you are unfamiliar with any of the following, you should consult a more experienced colleague or your systems administrator if you have one. There are also many good online tutorials you can use to learn the necessary notions.

• Basic Unix environment commands
• Binary / Executable
• Compiling a binary
• Command-line shell, terminal or console
• Software library

You will also need to have access to an ANSI compliant C++ compiler and the tools needed for normal compilations (make, shell, the standard library, tar, gunzip). These tools are usually pre-installed on Linux/Unix systems. On MacOS X, you may need to install the MacOS Xcode tools. See https://developer.apple.com/xcode/ for relevant information and software downloads.

Starting with version 2.6, the GATK requires Java Runtime Environment version 1.7. All Linux/Unix and MacOS X systems should have a JRE pre-installed, but the version may vary. To test your Java version, run the following command in the shell:

java -version


This should return a message along the lines of ”java version 1.7.0_25” as well as some details on the Runtime Environment (JRE) and Virtual Machine (VM). If you have a version other than 1.7.x, be aware that you may run into trouble with some of the more advanced features of the Picard and GATK tools. The simplest solution is to install an additional JRE and specify which you want to use at the command-line. To find out how to do so, you should seek help from your systems administrator.

#### Software packages

1. BWA
2. SAMtools
3. HTSlib (optional)
4. Picard
5. Genome Analysis Toolkit (GATK)
6. IGV
7. RStudio IDE and R libraries ggplot2 and gsalib

### 1. BWA

• Installation

Unpack the tar file using:

tar xvzf bwa-0.7.5a.tar.bz2


This will produce a directory called bwa-0.7.5a containing the files necessary to compile the BWA binary. Move to this directory and compile using:

cd bwa-0.7.5a
make


The compiled binary is called bwa. You should find it within the same folder (bwa-0.7.5a in this example). You may also find other compiled binaries; at time of writing, a second binary called bwamem-lite is also included. You can disregard this file for now. Finally, just add the BWA binary to your path to make it available on the command line. This completes the installation process.

• Testing

Open a shell and run:

bwa


This should print out some version and author information as well as a list of commands. As the Usage line states, to use BWA you will always build your command lines like this:

bwa <command> [options]


This means you first make the call to the binary (bwa), then you specify which command (method) you wish to use (e.g. index) then any options (i.e. arguments such as input files or parameters) used by the program to perform that command.

### 2. SAMtools

• Installation

Unpack the tar file using:

tar xvzf samtools-0.1.19.tar.bz2


This will produce a directory called samtools-0.1.19 containing the files necessary to compile the SAMtools binary. Move to this directory and compile using:

cd samtools-0.1.19
make


The compiled binary is called samtools. You should find it within the same folder (samtools-0.1.19 in this example). Finally, add the SAMtools binary to your path to make it available on the command line. This completes the installation process.

• Testing

Open a shell and run:

samtools


This should print out some version information as well as a list of commands. As the Usage line states, to use SAMtools you will always build your command lines like this:

samtools <command> [options]


This means you first make the call to the binary (samtools), then you specify which command (method) you wish to use (e.g. index) then any options (i.e. arguments such as input files or parameters) used by the program to perform that command. This is the same convention as used by BWA.

### 3. HTSlib (optional)

• Installation

Unpack the tar file using:

tar xjf htslib-master.zip


This will produce a directory called htslib-master containing the files necessary to compile the HTSlib binary. Move to this directory and compile using:

cd htslib-master
make


The compiled binary is called htscmd. You should find it within the same folder (htslib-master in this example). Finally, add the HTSlib binary to your path to make it available on the command line. This completes the installation process.

• Testing

Open a shell and run:

htscmd


This should print out some version information as well as a list of commands. As the Usage line states, to use HTSlib you will always build your command lines like this:

htscmd <command> [options]


This means you first make the call to the binary (htscmd), then you specify which command (method) you wish to use (e.g. index) then any options (i.e. arguments such as input files or parameters) used by the program to perform that command. This is the same convention as used by BWA and SAMtools.

### 4. Picard

• Installation

Unpack the zip file using:

tar xjf picard-tools-1.94.zip


This will produce a directory called picard-tools-1.94 containing the Picard jar files. Picard tools are distributed as pre-compiled Java executables (jar files) so there is no need to compile them. Finally, add the Picard directory to your path to make the tools available on the command line. This completes the installation process.

• Testing

Open a shell and run:

java -jar AddOrReplaceReadGroups.jar -h


This should print out some version and usage information about the AddOrReplaceReadGroups.jar tool. At this point you will have noticed an important difference between BWA and Picard tools. To use BWA, we called on the BWA program and specified which of its internal tools we wanted to apply. To use Picard, we called on Java itself as the main program, then specified which jar file to use, knowing that one jar file = one tool. This applies to all Picard tools; to use them you will always build your command lines like this:

java -jar <ToolName.jar> [options]


Next we will see that GATK tools are called in yet another way. The reasons for how tools in a given software package are organized and invoked are largely due to the preferences of the software developers. They generally do not reflect strict technical requirements, although they can have an effect on speed and efficiency.

### 5. Genome Analysis Toolkit (GATK)

In order to access the downloads, you need to register for a free account on the GATK support forum. You will also need to read and accept the license agreement before downloading the GATK software package. Note that if you intend to use the GATK for commercial purposes, you will need to purchase a license from our commercial partner, Appistry. See Appistry's GATK FAQ page for an overview of the commercial licensing conditions.

• Installation

Unpack the tar file using:

tar xjf GenomeAnalysisTK-2.6-4.tar.bz2


This will produce a directory called GenomeAnalysisTK-2.6-4-g3e5ff60 containing the GATK jar file, which is called GenomeAnalysisTK.jar, as well as a directory of example files called resources. GATK tools are distributed as a single pre-compiled Java executable so there is no need to compile them. Finally, add the GATK directory to your path to make the tools available on the command line. This completes the installation process.

• Testing

Open a shell and run:

java -jar GenomeAnalysisTK.jar -h


This should print out some version and usage information, as well as a list of the tools included in the GATK. As the Usage line states, to use GATK you will always build your command lines like this:

java -jar GenomeAnalysisTK.jar -T <ToolName> [arguments]


This means you first make the call to Java itself as the main program, then specify the GenomeAnalysisTK.jar file, then specify which tool you want, and finally you pass whatever other arguments (input files, parameters etc.) are needed for the analysis.

So this way of calling the program and selecting which tool to run is a little like a hybrid of how we called BWA and how we called Picard tools. To put it another way, if BWA is a standalone game device that comes preloaded with several games, Picard tools are individual game cartridges that plug into the Java console, and GATK is a single cartridge that also plugs into the Java console but contains many games.

### 6. IGV

The Integrated Genomics Viewer is a genome browser that allows you to view BAM, VCF and other genomic file information in context. It has a graphical user interface that is very easy to use, and can be downloaded for free (though registration is required) from this website.

### 7. RStudio IDE and R libraries ggplot2 and gsalib

• Installation

Follow the installation instructions provided. Binaries are provided for all major platforms; typically they just need to be placed in your Applications (or Programs) directory. Open RStudio and type the following command in the console window:

install.packages("ggplot2")


This will download and install the ggplot2 library as well as any other library packages that ggplot2 depends on for its operation. Note that some users have reported having to install one additional package themselves, called reshape, which you can do as follows:

install.packages("reshape")


Finally, do the same thing to install the gsalib library:

install.packages("gsalib")


Important note

If you are using a recent version of ggplot2 and a version of GATK older than 3.2, you may encounter an error when trying to generate the BQSR or VQSR recalibration plots. This is because until recently our scripts were still using an older version of certain ggplot2 functions. This has been fixed in GATK 3.2, so you should either upgrade your version of GATK (recommended) or downgrade your version of ggplot2. If you experience further issues generating the BQSR recalibration plots, please see this tutorial.

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Hello All,

I'm a new user of haplotypecaller. I am currently comparing it to unifiedgenotyper and samtools. When I do a count of SNPs for all DPs, it looks like the is a multiple of 3 issues. There seems to be a lot more SNPs with a DP of 3, 6, 9, ... compared to 1, 2, 4, 5, 7, 8 ....

DP #ofSNP
1 43
2 41
3 105
4 24
5 18
6 232
7 38
8 66
9 3749

I was wondering if this was a normal behavior and what caused it.

Yannick

Hi all,

I am comparing the variant calls from samtools and GATK. For samtools, I have been using quality score cut-offs 100 for SNP and 1000 for INDEL (quite stringent) and as a result, many variants are excluded after filtering. In case of GATK, I have been using our default setting (99% sensitivity for SNPs and 95% sensitivity for INDEL) and included only the variants with FILTER field "PASS". I was wondering if there is any more stringent filters that I can apply and that could be equivalent to samtools QS thresholds since it does not look like this is a fair comparison. Any of your suggestions will be appreciated.