The answer depends on what tool we're talking about, and whether we're considering variant discovery or variant manipulation.
GATK variant manipulation tools are able to recognize the following types of alleles:
Of our two variant callers, UnifiedGenotyper is the more limited, as it only calls SNPs and indels, and does so separately (even if you run in calling mode BOTH, the program performs separate calling operations internally). The HaplotypeCaller is more sophisticated and calls different types of variants at the same time. So in addition to SNPs and indels, it is capable of emitting mixed records by default. It is also capable of emitting MNPs and symbolic alleles, but the modes to do so are not enabled by default and they are not part of our recommended best practices for the tool.
The GATK currently does not handle SVs (structural variations) or CNVs (copy number variations), but there are some third-party software packages built on top of GATK that provide this functionality. See GenomeSTRiP for SVs and XHMM for CNVs.
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.
In addition to the reasons above, Haplotype Caller has another reason why some variants do not get called when it seems obvious in the original bam file.
Haplotype Caller performs a local reassembly of the reads in the active region. Please refer here for more details: http://www.broadinstitute.org/gatk/guide/article?id=4148
This reassembly is important, because when mapping reads to the whole genome, it is easy to make an error. When reassembling reads in a much smaller region, such as the active region, the alignment is more likely to be accurate.
The reads you see in the alignment of the original bam file may suggest a variant should be called. However, due to the realignment, the reads may no longer support the variant. In order to see the new alignment of reads, you can use -bamout argument. You can then compare the aligned reads from the original bam file to the newly aligned reads in the -bamout file.
In the example below, you see the original bam file on the top, and on the bottom is the bam file after reassembly. In this case, there seem to be many SNPs present, however, after reassembly, we find there is really a large deletion!