Please note that GATK-Lite was retired in February 2013 when version 2.4 was released. See the announcement here.
You probably know by now that GATK-Lite is a free-for-everyone and completely open-source version of the GATK (licensed under the original MIT license).
But what's in the box? What can GATK-Lite do -- or rather, what can it not do that the full version (let's call it GATK-Full) can? And what does that mean exactly, in terms of functionality, reliability and power?
To really understand the differences between GATK-Lite and GATK-Full, you need some more information on how the GATK works, and how we work to develop and improve it.
As explained here, the engine handles all the common work that's related to data access, conversion and traversal, as well as high-performance computing features. The engine is supported by an infrastructure of software libraries. If the GATK was a car, that would be the engine and chassis. What we call the **tools* are attached on top of that, and they provide the various analytical and processing functionalities like variant calling and base or variant recalibration. On your car, that would be headlights, airbags and so on.
We do all our development work on a single codebase. This means that everything --the engine and all tools-- is on one common workbench. There are not different versions that we work on in parallel -- that would be crazy to manage! That's why the version numbers of GATK-Lite and GATK-Full always match: if the latest GATK-Full version is numbered 2.1-13, then the latest GATK-Lite is also numbered 2.1-13.
The most important consequence of this setup is that when we make improvements to the infrastructure and engine, the same improvements will end up in GATK Lite and in GATK Full. So for the purposes of power, speed and robustness of the GATK that is determined by the engine, there is no difference between them.
For the tools, it's a little more complicated -- but not much. When we "build" the GATK binaries (the
.jar files), we put everything from the workbench into the Full build, but we only put a subset into the Lite build. Note that this Lite subset is pretty big -- it contains all the tools that were previously available in GATK 1.x versions, and always will. We also reserve the right to add previews or not-fully-featured versions of the new tools that are in Full, at our discretion, to the Lite build.
We have a new tool that performs a brand new function (which wasn't available in GATK 1.x), and we only include it in the Full build.
We have a tool that has some new add-on capabilities (which weren't possible in GATK 1.x); we put the tool in both the Lite and the Full build, but the add-ons are only available in the Full build.
Reprising the car analogy, GATK-Lite and GATK-Full are like two versions of the same car -- the basic version and the fully-equipped one. They both have the exact same engine, and most of the equipment (tools) is the same -- for example, they both have the same airbag system, and they both have headlights. But there are a few important differences:
The GATK-Full car comes with a GPS (sat-nav for our UK friends), for which the Lite car has no equivalent. You could buy a portable GPS unit from a third-party store for your Lite car, but it might not be as good, and certainly not as convenient, as the Full car's built-in one.
Both cars have windows of course, but the Full car has power windows, while the Lite car doesn't. The Lite windows can open and close, but you have to operate them by hand, which is much slower.
The underlying engine is exactly the same in both GATK-Lite and GATK-Full. Most functionalities are available in both builds, performed by the same tools. Some functionalities are available in both builds, but they are performed by different tools, and the tool in the Full build is better. New, cutting-edge functionalities are only available in the Full build, and there is no equivalent in the Lite build.
We hope this clears up some of the confusion surrounding GATK-Lite. If not, please leave a comment and we'll do our best to clarify further!
We support three types of interval lists, as mentioned here. Interval lists should preferentially be formatted as Picard-style interval lists, with an explicit sequence dictionary, as this prevents accidental misuse (e.g. hg18 intervals on an hg19 file). Note that this file is 1-based, not 0-based (first position in the genome is position 1).
One relatively easy way to combine your intervals is to use the online tool Galaxy, using the
Get Data -> Upload command to upload your intervals, and the
Operate on Genomic Intervals command to compute the intersection or union of your intervals (depending on your needs).
We support the Variant Call Format (VCF) for variant callsets. No other file formats are supported.
No, we like VCF and we think it's important to have a good standard format. Multiplying formats just makes life hard for everyone, both developers and analysts.
The GATK supports the BAM format for reads, quality scores, alignments, and metadata (e.g. the lane of sequencing, center of origin, sample name, etc.). No other file formats are supported.
The GATK doesn't have any tools for getting data into BAM format, but many other toolkits exist for this purpose. We recommend you look at Picard and Samtools for creating and manipulating BAM files. Also, many aligners are starting to emit BAM files directly. See BWA for one such aligner.
All BAM files must satisfy the following requirements:
See the BAM specification for more information.
It depends on whether you're using the NCBI/GRC build 36/build 37 version of the human genome, or the UCSC hg18/hg19 version of the human genome. While substantially equivalent, the naming conventions are different. The canonical ordering of contigs for these genomes is as follows:
Human genome reference consortium standard ordering and names (b3x): 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, Y, MT...
UCSC convention (hg1x): chrM, chr1, chr2, chr3, chr4, chr5, chr6, chr7, chr8, chr9, chr10, chr11, chr12, chr13, chr14, chr15, chr16, chr17, chr18, chr19, chr20, chr21, chr22, chrX, chrY...
The easiest way to do it is to download Samtools and run the following command to examine the header of your file:
$ samtools view -H /path/to/my.bam @HD VN:1.0 GO:none SO:coordinate @SQ SN:1 LN:247249719 @SQ SN:2 LN:242951149 @SQ SN:3 LN:199501827 @SQ SN:4 LN:191273063 @SQ SN:5 LN:180857866 @SQ SN:6 LN:170899992 @SQ SN:7 LN:158821424 @SQ SN:8 LN:146274826 @SQ SN:9 LN:140273252 @SQ SN:10 LN:135374737 @SQ SN:11 LN:134452384 @SQ SN:12 LN:132349534 @SQ SN:13 LN:114142980 @SQ SN:14 LN:106368585 @SQ SN:15 LN:100338915 @SQ SN:16 LN:88827254 @SQ SN:17 LN:78774742 @SQ SN:18 LN:76117153 @SQ SN:19 LN:63811651 @SQ SN:20 LN:62435964 @SQ SN:21 LN:46944323 @SQ SN:22 LN:49691432 @SQ SN:X LN:154913754 @SQ SN:Y LN:57772954 @SQ SN:MT LN:16571 @SQ SN:NT_113887 LN:3994 ...
If the order of the contigs here matches the contig ordering specified above, and the
SO:coordinate flag appears in your header, then your contig and read ordering satisfies the GATK requirements.
Picard offers a tool called SortSam that will sort a BAM file properly. A similar utility exists in Samtools, but we recommend the Picard tool because SortSam will also set a flag in the header that specifies that the file is correctly sorted, and this flag is necessary for the GATK to know it is safe to process the data. Also, you can use the ReorderSam command to make a BAM file SQ order match another reference sequence.
A quick Unix command using Samtools will do the trick:
$ samtools view -H /path/to/my.bam | grep '^@RG' @RG ID:0 PL:solid PU:Solid0044_20080829_1_Pilot1_Ceph_12414_B_lib_1_2Kb_MP_Pilot1_Ceph_12414_B_lib_1_2Kb_MP LB:Lib1 PI:2750 DT:2008-08-28T20:00:00-0400 SM:NA12414 CN:bcm @RG ID:1 PL:solid PU:0083_BCM_20080719_1_Pilot1_Ceph_12414_B_lib_1_2Kb_MP_Pilot1_Ceph_12414_B_lib_1_2Kb_MP LB:Lib1 PI:2750 DT:2008-07-18T20:00:00-0400 SM:NA12414 CN:bcm @RG ID:2 PL:LS454 PU:R_2008_10_02_06_06_12_FLX01080312_retry LB:HL#01_NA11881 PI:0 SM:NA11881 CN:454MSC @RG ID:3 PL:LS454 PU:R_2008_10_02_06_07_08_rig19_retry LB:HL#01_NA11881 PI:0 SM:NA11881 CN:454MSC @RG ID:4 PL:LS454 PU:R_2008_10_02_17_50_32_FLX03080339_retry LB:HL#01_NA11881 PI:0 SM:NA11881 CN:454MSC ...
The presence of the
@RG tags indicate the presence of read groups. Each read group has a
SM tag, indicating the sample from which the reads belonging to that read group originate.
In addition to the presence of a read group in the header, each read must belong to one and only one read group. Given the following example reads,
$ samtools view /path/to/my.bam | grep '^@RG' EAS139_44:2:61:681:18781 35 1 1 0 51M = 9 59 TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAA B<>;==?=?<==?=?=>>?>><=<?=?8<=?>?<:=?>?<==?=>:;<?:= RG:Z:4 MF:i:18 Aq:i:0 NM:i:0 UQ:i:0 H0:i:85 H1:i:31 EAS139_44:7:84:1300:7601 35 1 1 0 51M = 12 62 TAACCCTAAGCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAA G<>;==?=?&=>?=?<==?>?<>>?=?<==?>?<==?>?1==@>?;<=><; RG:Z:3 MF:i:18 Aq:i:0 NM:i:1 UQ:i:5 H0:i:0 H1:i:85 EAS139_44:8:59:118:13881 35 1 1 0 51M = 2 52 TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAA @<>;<=?=?==>?>?<==?=><=>?-?;=>?:><==?7?;<>?5?<<=>:; RG:Z:1 MF:i:18 Aq:i:0 NM:i:0 UQ:i:0 H0:i:85 H1:i:31 EAS139_46:3:75:1326:2391 35 1 1 0 51M = 12 62 TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAA @<>==>?>@???B>A>?>A?A>??A?@>?@A?@;??A>@7>?>>@:>=@;@ RG:Z:0 MF:i:18 Aq:i:0 NM:i:0 UQ:i:0 H0:i:85 H1:i:31 ...
membership in a read group is specified by the
RG:Z:* tag. For instance, the first read belongs to read group 4 (sample NA11881), while the last read shown here belongs to read group 0 (sample NA12414).
Yes! Many algorithms in the GATK need to know that certain reads were sequenced together on a specific lane, as they attempt to compensate for variability from one sequencing run to the next. Others need to know that the data represents not just one, but many samples. Without the read group and sample information, the GATK has no way of determining this critical information.
For technical details, see the SAM specification on the Samtools website.
|Tag||Importance||SAM spec definition||Meaning|
||Required||Read group identifier. Each
||Ideally, this should be a globally unique identify across all sequencing data in the world, such as the Illumina flowcell + lane name and number. Will be referenced by each read with the
||Sample. Use pool name where a pool is being sequenced.||Required. As important as
||The name of the sample sequenced in this read group. GATK tools treat all read groups with the same
||Platform/technology used to produce the read. Valid values: ILLUMINA, SOLID, LS454, HELICOS and PACBIO.||Important. Not currently used in the GATK, but was in the past, and may return. The only way to known the sequencing technology used to generate the sequencing data .||It's a good idea to use this field.|
||DNA preparation library identify||Essential for MarkDuplicates||MarkDuplicates uses the LB field to determine which read groups might contain molecular duplicates, in case the same DNA library was sequenced on multiple lanes.|
We do not require value for the
A concrete example may be instructive. Suppose I 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, I 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).
Use Picard's AddOrReplaceReadGroups tool to add read group information.
You can use the GATK to do the following:
GATK -I full.bam -T PrintReads -L chr1:10-20 -o subset.bam
and you'll get a BAM file containing only reads overlapping those points. This operation retains the complete BAM header from the full file (this was the reference aligned to, after all) so that the BAM remains easy to work with. We routinely use these features for testing and high-performance analysis with the GATK.
Scala is a combination of an object oriented framework and a functional programming language. For a good introduction see the free online book Programming Scala.
The following are extremely brief answers to frequently asked questions about Scala which often pop up when first viewing or editing QScripts. For more information on Scala there a multitude of resources available around the web including the Scala home page and the online Scala Doc.
var is a value you can later modify, while
val is similar to
final in Java.
Because the GATK and Queue are a mix of Scala and Java sometimes you'll run into problems when you need a Scala collection and instead a Java collection is returned.
MyQScript.scala:39: error: type mismatch; found : java.util.List[java.lang.String] required: scala.List[String] val wrapped: List[String] = TextFormattingUtils.wordWrap(text, width)
Use the implicit definitions in
JavaConversions to automatically convert the basic Java collections to and from Scala collections.
Scala has a very rich collections framework which you should take the time to enjoy. One of the first things you'll notice is that the default Scala collections are immutable, which means you should treat them as you would a String. When you want to 'modify' an immutable collection you need to capture the result of the operation, often assigning the result back to the original variable.
var str = "A" str + "B" println(str) // prints: A str += "C" println(str) // prints: AC var set = Set("A") set + "B" println(set) // prints: Set(A) set += "C" println(set) // prints: Set(A, C)
:+ operator for a single value.
var myList = List.empty[String] myList :+= "a" myList :+= "b" myList :+= "c"
++ for appending a list.
var myList = List.empty[String] myList ++= List("a", "b", "c")
var mySet = Set.empty[String] mySet += "a" mySet += "b" mySet += "c"
var myMap = Map.empty[String,Int] myMap += "a" -> 1 myMap += "b" -> 2 myMap += "c" -> 3
Option is a Scala generic type that can either be some generic value or
None. Queue often uses it to represent primitives that may be null.
var myNullableInt1: Option[Int] = Some(1) var myNullableInt2: Option[Int] = None
François Armand's slide deck is a good introduction: http://www.slideshare.net/normation/scala-dreaded
To quote from his slides:
Give me a variable name but - I don't care of what it is - and/or - don't want to pollute my namespace with it
This Java snippet:
String formatted = String.format("%s %i", myString, myInt);
In Scala would be:
val formatted = "%s %i".format(myString, myInt)
No. Currently Scala's
Enumeration class does not interact with the Java reflection API in a way that could be used for Queue command line arguments. You can use Java
enums if for example you are importing a Java based walker's
If/when we find a workaround for Queue we'll update this entry. In the meantime try using a String.
This document describes the resource datasets and arguments to use in the two steps of VQSR (i.e. the successive application of VariantRecalibrator and ApplyRecalibration), based on our work with human genomes.
Note that VQSR must be run twice in succession in order to build a separate error model for SNPs and INDELs (see the VQSR documentation for more details).
These recommendations are valid for use with calls generated by both the UnifiedGenotyper and HaplotypeCaller. In the past we made a distinction in how we processed the calls from these two callers, but now we treat them the same way. These recommendations will probably not work properly on calls generated by other (non-GATK) callers.
The human genome training, truth and known resource datasets mentioned in this document are all available from our resource bundle.
If you are working with non-human genomes, you will need to find or generate at least truth and training resource datasets with properties corresponding to those described below. To generate your own resource set, one idea is to first do an initial round of SNP calling and only use those SNPs which have the highest quality scores. These sites which have the most confidence are probably real and could be used as truth data to help disambiguate the rest of the variants in the call set. Another idea is to try using several SNP callers in addition to the UnifiedGenotyper or HaplotypeCaller, and use those sites which are concordant between the different methods as truth data. In either case, you'll need to assign your set a prior likelihood that reflects your confidence in how reliable it is as a truth set. We recommend Q10 as a starting value, which you can then experiment with to find the most appropriate value empirically. There are many possible avenues of research here. Hopefully the model reporting plots that are generated by the recalibration tools will help facilitate this experimentation.
True sites training resource: HapMap
This resource is a SNP call set that has been validated to a very high degree of confidence. The program will consider that the variants in this resource are representative of true sites (truth=true), and will use them to train the recalibration model (training=true). We will also use these sites later on to choose a threshold for filtering variants based on sensitivity to truth sites. The prior likelihood we assign to these variants is Q15 (96.84%).
True sites training resource: Omni
This resource is a set of polymorphic SNP sites produced by the Omni geno- typing array. The program will consider that the variants in this resource are representative of true sites (truth=true), and will use them to train the recalibration model (training=true). The prior likelihood we assign to these variants is Q12 (93.69%).
Non-true sites training resource: 1000G
This resource is a set of high-confidence SNP sites produced by the 1000 Genomes Project. The program will consider that the variants in this re- source may contain true variants as well as false positives (truth=false), and will use them to train the recalibration model (training=true). The prior likelihood we assign to these variants is Q10 (%). 17
Known sites resource, not used in training: dbSNP
This resource is a call set that has not been validated to a high degree of confidence (truth=false). The program will not use the variants in this resource to train the recalibration model (training=false). However, the program will use these to stratify output metrics such as Ti/Tv ratio by whether variants are present in dbsnp or not (known=true). The prior likelihood we assign to these variants is Q2 (36.90%).
The variant quality score recalibrator builds an adaptive error model using known variant sites and then applies this model to estimate the probability that each variant is a true genetic variant or a machine artifact. One major improvement from previous recommended protocols is that hand filters do not need to be applied at any point in the process now. All filtering criteria are learned from the data itself.
java -Xmx4g -jar GenomeAnalysisTK.jar \ -T VariantRecalibrator \ -R path/to/reference/human_g1k_v37.fasta \ -input raw.input.vcf \ -recalFile path/to/output.recal \ -tranchesFile path/to/output.tranches \ -nt 4 \ [SPECIFY TRUTH AND TRAINING SETS] \ [SPECIFY WHICH ANNOTATIONS TO USE IN MODELING] \ [SPECIFY WHICH CLASS OF VARIATION TO MODEL] \
For SNPs we use both HapMap v3.3 and the Omni chip array from the 1000 Genomes Project as training data. In addition we take the highest confidence SNPs from the project's callset. These datasets are available in the GATK resource bundle.
Arguments for VariantRecalibrator command:
-resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap_3.3.b37.sites.vcf \ -resource:omni,known=false,training=true,truth=true,prior=12.0 1000G_omni2.5.b37.sites.vcf \ -resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G_phase1.snps.high_confidence.vcf \ -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.b37.vcf \ -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an DP -an InbreedingCoeff \ -mode SNP \
Please note that these recommendations are formulated for whole-genome datasets. For exomes, we do not recommend using DP for variant recalibration (see below for details of why).
Note also that, for the above to work, the input vcf needs to be annotated with the corresponding values (QD, FS, DP, etc.). If any of these values are somehow missing, then VariantAnnotator needs to be run first so that VariantRecalibration can run properly.
Also, using the provided sites-only truth data files is important here as parsing the genotypes for VCF files with many samples increases the runtime of the tool significantly.
You may notice that these recommendations no longer include the
--numBadVariants argument. That is because we have removed this argument from the tool, as the VariantRecalibrator now determines the number of variants to use for modeling "bad" variants internally based on the data.
Some of these annotations might not be the best for your particular dataset.
Depth of coverage (the DP annotation invoked by Coverage) should not be used when working with exome datasets since there is extreme variation in the depth to which targets are captured! In whole genome experiments this variation is indicative of error but that is not the case in capture experiments.
Additionally, the UnifiedGenotyper produces a statistic called the HaplotypeScore which should be used for SNPs. This statistic isn't necessary for the HaplotypeCaller because that mathematics is already built into the likelihood function itself when calling full haplotypes.
The InbreedingCoeff is a population level statistic that requires at least 10 samples in order to be computed. For projects with fewer samples please omit this annotation from the command line.
In our testing we've found that in order to achieve the best exome results one needs to use an exome SNP and/or indel callset with at least 30 samples. For users with experiments containing fewer exome samples there are several options to explore:
When modeling indels with the VQSR we use a training dataset that was created at the Broad by strictly curating the (Mills, Devine, Genome Research, 2011) dataset as as well as adding in very high confidence indels from the 1000 Genomes Project. This dataset is available in the GATK resource bundle.
Arguments for VariantRecalibrator:
--maxGaussians 4 \ -resource:mills,known=false,training=true,truth=true,prior=12.0 Mills_and_1000G_gold_standard.indels.b37.sites.vcf \ -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.b37.vcf \ -an QD -an DP -an FS -an ReadPosRankSum -an MQRankSum -an InbreedingCoeff \ -mode INDEL \
Note that indels use a different set of annotations than SNPs. Most annotations related to mapping quality have been removed since there is a conflation with the length of an indel in a read and the degradation in mapping quality that is assigned to the read by the aligner. This covariation is not necessarily indicative of being an error in the same way that it is for SNPs.
You may notice that these recommendations no longer include the
--numBadVariants argument. That is because we have removed this argument from the tool, as the VariantRecalibrator now determines the number of variants to use for modeling "bad" variants internally based on the data.
The power of the VQSR is that it assigns a calibrated probability to every putative mutation in the callset. The user is then able to decide at what point on the theoretical ROC curve their project wants to live. Some projects, for example, are interested in finding every possible mutation and can tolerate a higher false positive rate. On the other hand, some projects want to generate a ranked list of mutations that they are very certain are real and well supported by the underlying data. The VQSR provides the necessary statistical machinery to effectively apply this sensitivity/specificity tradeoff.
java -Xmx3g -jar GenomeAnalysisTK.jar \ -T ApplyRecalibration \ -R reference/human_g1k_v37.fasta \ -input raw.input.vcf \ -tranchesFile path/to/input.tranches \ -recalFile path/to/input.recal \ -o path/to/output.recalibrated.filtered.vcf \ [SPECIFY THE DESIRED LEVEL OF SENSITIVITY TO TRUTH SITES] \ [SPECIFY WHICH CLASS OF VARIATION WAS MODELED] \
For SNPs we used HapMap 3.3 and the Omni 2.5M chip as our truth set. We typically seek to achieve 99.5% sensitivity to the accessible truth sites, but this is by no means universally applicable: you will need to experiment to find out what tranche cutoff is right for your data. Generally speaking, projects involving a higher degree of diversity in terms of world populations can expect to achieve a higher truth sensitivity than projects with a smaller scope.
--ts_filter_level 99.5 \ -mode SNP \
For indels we use the Mills / 1000 Genomes indel truth set described above. We typically seek to achieve 99.0% sensitivity to the accessible truth sites, but this is by no means universally applicable: you will need to experiment to find out what tranche cutoff is right for your data. Generally speaking, projects involving a higher degree of diversity in terms of world populations can expect to achieve a higher truth sensitivity than projects with a smaller scope.
--ts_filter_level 99.0 \ -mode INDEL \
JEXL stands for Java EXpression Language. It's not a part of the GATK as such; it's a software library that can be used by Java-based programs like the GATK. It can be used for many things, but in the context of the GATK, it has one very specific use: making it possible to operate on subsets of variants from VCF files based on one or more annotations, using a single command. This is typically done with walkers such as VariantFiltration and SelectVariants.
In this context, a JEXL expression is a string (in the computing sense, i.e. a series of characters) that tells the GATK which annotations to look at and what selection rules to apply.
JEXL expressions contain three basic components: keys and values, connected by operators. For example, in this simple JEXL expression which selects variants whose quality score is greater than 30:
"QUAL > 30.0"
QUALis a key: the name of the annotation we want to look at
30.0is a value: the threshold that we want to use to evaluate variant quality against
>is an operator: it determines which "side" of the threshold we want to select
The complete expression must be framed by double quotes. Within this, keys are strings (typically written in uppercase or CamelCase), and values can be either strings, numbers or booleans (TRUE or FALSE) -- but if they are strings the values must be framed by single quotes, as in the following example:
"MY_STRING_KEY == 'foo'"
You can build expressions that calculate a metric based on two separate annotations, for example if you want to select variants for which quality (QUAL) divided by depth of coverage (DP) is below a certain threshold value:
"QUAL / DP < 10.0"
You can also join multiple conditional statements with logical operators, for example if you want to select variants that have both sufficient quality (QUAL) and a certain depth of coverage (DP):
"QUAL > 30.0 && DP == 10"
&& is the logical "AND".
Or if you want to select variants that have at least one of several conditions fulfilled:
"QD < 2.0 || ReadPosRankSum < -20.0 || FS > 200.0"
|| is the logical "OR".
Currently, VCF INFO field keys are case-sensitive. That means that if you have a
QUAL field in uppercase in your VCF record, the system will not recognize it if you write it differently (
qual or whatever) in your JEXL expression.
The types (i.e. string, integer, non-integer or boolean) used in your expression must be exactly the same as that of the value you are trying to evaluate. In other words, if you have a QUAL field with non-integer values (e.g. 45.3) and your filter expression is written as an integer (e.g. "QUAL < 50"), the system will throw a hissy fit (aka a Java exception).
We highly recommend that complex expressions involving multiple AND/OR operations be split up into separate expressions whenever possible to avoid confusion. If you are using complex expressions, make sure to test them on a panel of different sites with several combinations of yes/no criteria.
Note that this last part is fairly advanced and not for the faint of heart. To be frank, it's also explained rather more briefly than the topic deserves. But if there's enough demand for this level of usage (click the "view in forum" link and leave a comment) we'll consider producing a full-length tutorial.
If you are familiar with the VariantContext, Genotype and its associated classes and methods, you can directly access the full range of capabilities of the underlying objects from the command line. The underlying VariantContext object is available through the
For example, suppose I want to use SelectVariants to select all of the sites where sample NA12878 is homozygous-reference. This can be accomplished by assessing the underlying VariantContext as follows:
java -Xmx4g -jar GenomeAnalysisTK.jar -T SelectVariants -R b37/human_g1k_v37.fasta --variant my.vcf -select 'vc.getGenotype("NA12878").isHomRef()'
Groovy, right? Now here's a more sophisticated example of JEXL expression that finds all novel variants in the total set with allele frequency > 0.25 but not 1, is not filtered, and is non-reference in 01-0263 sample:
! vc.getGenotype("01-0263").isHomRef() && (vc.getID() == null || vc.getID().equals(".")) && AF > 0.25 && AF < 1.0 && vc.isNotFiltered() && vc.isSNP() -o 01-0263.high_freq_novels.vcf -sn 01-0263
The classic way of evaluating a boolean goes like this:
java -Xmx4g -jar GenomeAnalysisTK.jar -T SelectVariants -R b37/human_g1k_v37.fasta --variant my.vcf -select 'DB'
But you can also use the VariantContext object like this:
java -Xmx4g -jar GenomeAnalysisTK.jar -T SelectVariants -R b37/human_g1k_v37.fasta --variant my.vcf -select 'vc.hasAttribute("DB")'
Sometimes you might want to write a JEXL expression to evaluate e.g. the AD (allelic depth) field in the FORMAT column. However, the AD is technically not an integer; rather it is a list (array) of integers. One can evaluate the array data using the "." operator. Here's an example:
java -Xmx4g -jar GenomeAnalysisTK.jar -T SelectVariants -R b37/human_g1k_v37.fasta --variant my.vcf -select 'vc.getGenotype("NA12878").getAD().0 > 10'
Just because something looks like a SNP in IGV doesn't mean that it is of high quality. We are extremely confident in the genotype likelihoods calculations in the Unified Genotyper (especially for SNPs) and in the HaplotypeCaller (for all variants including indels). So, before you post this issue in our support forum, please do a little bit of investigation on your own, as follows.
To diagnose what is happening, you should take a look at the pileup of bases at the position in question. It is very important for you to look at the underlying data here.
Here is a checklist of questions you should ask yourself:
The genotyper ignores sites if there are too many overlapping deletions. This value can be set using the
--max_deletion_fraction argument (see the UG's documentation page to find out what is the default value for this argument), but be aware that increasing it could affect the reliability of your results.
Remember that there is a minimum base quality threshold and that low base qualities mean that the sequencer assigned a low confidence to that base. If your would-be SNP is only supported by low-confidence bases, it is probably a false positive.
Keep in mind that the depth reported in the VCF is the unfiltered depth. You may think you have good coverage at that site, but the Unified Genotyper ignores bases if they don't look good, so actual coverage seen by the UG may be lower than you think.
A base's quality is capped by the mapping quality of its read. The reason for this is that low mapping qualities mean that the aligner had little confidence that the read is mapped to the correct location in the genome. You may be seeing mismatches because the read doesn't belong there -- you may be looking at the sequence of some other locus in the genome!
Keep in mind also that reads with mapping quality 255 ("unknown") are ignored.
By default the UG will only consider a certain number of alternate alleles. This value can be set using the
--max_alternate_alleles argument (see the UG's documentation page to find out what is the default value for this argument). Note however that genotyping sites with many alternate alleles is both CPU and memory intensive and it scales exponentially based on the number of alternate alleles. Unless there is a good reason to change the default value, we highly recommend that you not play around with this parameter.
SOLiD alignments tend to have reference bias and it can be severe in some cases. Do the SOLiD reads have a lot of mismatches (no-calls count as mismatches) around the the site? If so, you are probably seeing false positives.