This may seem crazy considering we released the big 3.0 version not two weeks ago, but yes, we have a new version for you already! It's a bit of a special case because this release is all about the hardware-based optimizations we had previously announced. What we hadn't announced yet was that this is the fruit of a new collaboration with a team at Intel (which you can read more about here), so we were waiting for everyone to be ready for the big reveal.
So basically, the story is that we've started collaborating with the Intel Bio Team to enable key parts of the GATK to run more efficiently on certain hardware configurations. For our first project together, we tackled the PairHMM algorithm, which is responsible for a large proportion of the runtime of HaplotypeCaller analyses. The resulting optimizations, which are the main feature in version 3.1, produce significant speedups for HaplotypeCaller runs on a wide range of hardware.
We will continue working with Intel to further improve the performance of GATK tools that have historically been afflicted with performance issues and long runtimes (hello BQSR). As always, we hope these new features will make your life easier, and we welcome your feedback in the forum!
Note that these optimizations currently work on Linux systems only, and will not work on Mac or Windows operating systems. In the near future we will add support for Mac OS. We have no plans to add support for Windows since the GATK itself does not run on Windows.
Please note also that to take advantage of these optimizations, you need to opt-in by adding the following flag to your GATK command:
Here is a handy little table of the speedups you can expect depending on the hardware and operating system you are using. The configurations given here are the minimum requirements for benefiting from the expected speedup ranges shown in the third column. Keep in mind that these numbers are based on tests in controlled conditions; in the wild, your mileage may vary.
|Linux kernel version||Architecture / Processor||Expected speedup||Instruction set|
|Any 64-bit Linux||Any x86 64-bit||1-1.5x||Non-vector|
|Linux 2.6 or newer||Penryn (Core 2 or newer)||1.3-1.8x||SSE 4.1|
|Linux 2.6.30 or newer||SandyBridge (i3, i5, i7, Xeon E3, E5, E7 or newer)||2-2.5x||AVX|
To find out exactly which processor is in your machine, you can run this command in the terminal:
$ cat /proc/cpuinfo | grep "model name" model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz model name : Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
In this example, the machine has 4 cores (8-threads), so you see the answer 8 times. With the model name (here i7-2600) you can look up your hardware's relevant capabilities in the Wikipedia page on vector extensions.
Alternatively, Intel has provided us with some links to lists of processors categorized by architecture, in which you can look up your hardware:
Finally, a few notes to clarify some concepts regarding Linux kernels vs. distributions and processors vs. architectures:
SandyBridge and Penryn are microarchitectures; essentially, these are sets of instructions built into the CPU. Core 2, core i3, i4, i7, Xeon e3, e5, e7 are the processors that will implement a specific architecture to make use of the relevant improvements (see table above).
The Linux kernel has no connection with Linux distribution (e.g. Ubuntu, RedHat etc). Any distribution can use any kernel they want. There are "default kernels" shipped with each distribution, but that's beyond the scope of this article to cover (there are at least 300 Linux distributions out there). But you can always install whatever kernel version you want.
The kernel version 2.6.30 was released in 2009, so we expect every sane person or IT out there to be using something better than this.
Better late than never, here is the now-traditional "Highlights" document for GATK version 3.0, which was released two weeks ago. It will be a very short one since we've already gone over the new features in detail in separate articles --but it's worth having a recap of everything in one place. So here goes.
We are delighted to present our new Best Practices workflow for variant calling in which multisample calling is replaced by a winning combination of single-sample calling in gVCF mode and joint genotyping analysis. This allows us to both bypass performance issues and solve the so-called "N+1 problem" in one fell swoop. For full details of why and how this works, please see this document. In the near future, we will update our Best Practices page to make it clear that the new workflow is now the recommended way to go for calling variants on cohorts of samples. We've already received some pretty glowing feedback from early adopters, so be sure to try it out for yourself!
All the cool kids were doing it, so we had to join the party. It took a few months of experimentation, a couple of new tools and some tweaks to the HaplotypeCaller, but you can now call variants on RNAseq with GATK! This document details our Best Practices recommendations for doing so, along with a non-trivial number of caveats that you should keep in mind as you go.
Nice try, but no. This tool is obsolete now that we have the gVCF/reference model pipeline (see above). Note that this means that GATK 3.0 will not support BAM files that were processed using ReduceReads!
We've switched the build system from Ant to Maven, which should make it much easier to use GATK as a library against which you can develop your own tools. And on a related note, we're also making significant changes to the internal structure of the GATK codebase. Hopefully this will not have too much impact on external projects, but there will be a doc very shortly describing how the new build system works and how the codebase is structured.
For reasons that will be made clear in the near future, we decided to hold the previously announced hardware optimizations until version 3.1, which will be released very soon. Stay tuned!
Better late than never, here are the highlights of the most recent version release, GATK 2.8. This should be short and sweet because as releases go, 2.8 is light on new features, and is best described as a collection of bug fixes, which are all* dutifully listed in the corresponding release notes document. That said, two of the changes we've made deserve some additional explanation.
* Up to now (this release included) we have not listed updates/patches to Queue in the release notes, but will start doing so from the next version onward.
In the last release (2.7, for those of you keeping score at home) we trumpeted that the old
-percentBad argument of VariantRecalibrator had been replaced by the shiny new
-numBad argument, and that this was going to be awesome for all sorts of good reasons, improve stability and whatnot. Weeeeeeell it turned out that wasn't quite the case. It worked really well on the subset of analyses that we tested it on initially, but once we expanded to different datasets (and the complaints started rolling in on the forum) we realized that it actually made things worse in some cases because the default value was less appropriate than what
-percentBad would have produced. This left people guessing as to what value would work for their particular dataset, with a great big range to choose from and very little useful information to assist in the choice.
So, long story short, we (and by "we" I mean Ryan) built in a new function that allows the VariantRecalibrator to determine for itself the amount of variants that is appropriate to use for the "bad" model depending on the data. So the short-lived
-numBad argument is gone too, replaced by... nothing. No new argument to specify; just let the VariantRecalibrator do its thing.
Of course if you really want to, you can override the default behavior and tweak the internal thresholds. See the tool doc here; and remember that a good rule of thumb is that if you can't figure out which arguments are involved based on that doc, you probably shouldn't be messing with this advanced functionality.
This is still a rather experimental feature, so we're still making changes as we go. The two big changes worth mentioning here are that you can now run this on reduced reads, and that we've changed the indexing routine to optimize the compression level. The latter shouldn't have any immediate impact on normal users, but it was necessary for a new feature project we've been working on behind the scenes (the single-sample-to-joint-discovery pipeline we have been alluding to in recent forum discussions). The reason we're mentioning it now is that if you use
-ERC GVCF output, you'll need to specify a couple of new arguments as well (
-variant_index_type LINEAR and
-variant_index_parameter 128000, with those exact values). This useful little fact didn't quite make it into the documentation before we released, and not specifying them leads to an error message, so... there you go. No error message for you!
That's all for tool changes. In addition to those, we have made a number of corrections in the tool documentation pages, updated the Best Practices (mostly layout, tiny bit of content update related to the VQSR -numBad deprecation) and made some minor changes to the website, e.g. updated the list of publications that cite the GATK and improved the Guide index somewhat (but that's still a work in progress).
Yay, August is over! Goodbye steamy hot days, hello mild temperatures and beautiful leaf-peeping season. We hope you all had a great summer (in the Northern hemisphere at least) and caught a bit of a vacation. For our part, we've been chained to our desks the whole time!
Well, not really, but we've got a feature-rich release for you nonetheless. Lots of new things; not all of them fully mature, so heed the caveats on the experimental features! We've also made some key improvements to VQSR that we're very excited about, some bug fixes to various tools of course, and a new way to boost calling performance. Full list in the release notes as usual, and highlights below.
When UnifiedGenotyper and HaplotypeCaller emit variant calls, they tell you how confident you can be that the variants are real. But how do you know how confident to be that the rest are reference, i.e. non-variant? It's actually a pretty hard problem… and this is our answer:
For HaplotypeCaller, we’ve developed a full-on reference model that produces reference confidence scores. To use it, you need to enable the
--emitRefConfidence mode. This mode is a little bit complicated so be sure you read the method article before you try to use it.
For UnifiedGenotyper, we don’t have a completely fleshed-out model, but we’ve added the
-allSitePLs argument which, in combination with the
EMIT_ALL_SITES output mode, will enable calculation of PLs for all sites, including reference. This will give a measure of reference confidence and a measure of which alt alleles are more plausible (if any). Note that this only works with the SNP calling model. Again, this is not as good or as complete as the reference model in HaplotypeCaller, so we urge you to use HaplotypeCaller for this unless you really need to use UnifiedGenotyper.
These are two highly experimental features; they work in our tests, but your mileage may vary, so please examine your results carefully. We welcome your feedback!
A common problem in calling indels is that you get false positives that are associated with PCR slippage around short tandem repeats (especially homopolymers). Until we can all switch to PCR-free amplification, we're stuck with this issue. So we thought it would be nice to be able to model this type of error and mitigate its impact on our indel calls. The new
--pcr_indel_model argument allows the HaplotypeCaller to use a new feature called the PCR indel model to weed out false positive indels more or less aggressively depending on how much you care about sensitivity vs. specificity.
This feature too is highly experimental, so play with it at your own risk. And stay tuned, because we've already got some ideas on how to improve it further.
Variant recalibration is one of the most challenging parts of the Best Practices workflow, and not just for users! We've been wrestling with some of its internal machinery to produce better, more consistent modeling results, especially with call sets that are on the lower end of the size scale.
One of the breakthroughs we made was separating the parameters for the positive and negative training models. You know (or should know) that the VariantRecalibrator builds two separate models: one to model what "good" variants (i.e. true positives) look like (the positive model), and one to model what "bad" variants (i.e. false positives) look like (the negative model). Until now, we applied parameters the same way to both, but we've now realized that it makes more sense to treat them differently.
Because of how relative amounts of good and bad variants tend to scale differently with call set size, we also realized it was a bad idea to have the selection of bad variants be based on a percentage (as it has been until now) and instead switched it to a hard number. You can change this setting with the
--numBadVariants argument, which replaces the now-deprecated
Finally, we also found that the order of annotations matters. Now, instead of applying the annotation dimensions to the training model in the order that they were specified at the command line, VariantRecalibrator first reorders them based on their standard deviation. This stabilizes the training model and produces much more consistent results.
Some of you have been clamoring for more flexibility in handling individual BAM files and samples without losing the convenience of processing them in batches. In response, we've added the following:
For general GATK use, the
-sample_rename_mapping_file engine argument allows you to rename samples on-the-fly at runtime. It takes a file that maps bam files to sample names. Note that this does require that your BAM files contain single samples only, although multiple read groups are allowed.
For variant calling, the
-onlyEmitSamples argument allows you to tell the UnifiedGenotyper to only emit calls for specific samples among a cohort that you're calling in multisample mode, without emitting the calls for the rest of the cohort. Keep in mind however that the calculations will still be made on the entire cohort, and the annotation values emitted for those calls will reflect that.
For VQSR, the
--excludeFiltered flag tells the ApplyRecalibration tool not to emit sites that are filtered out by recalibration (i.e. do not write them to file).
And some of you went ahead and added the features you wanted yourselves!
Yossi Farjoun contributed a patch to enable allele-biased downsampling with different per-sample values for the HaplotypeCaller, emulating the equivalent functionality that was already available in the UnifiedGenotyper.
Louis Bergelson contributed a new read filter, LibraryReadFilter, which allows you to use only reads from a specific library in your analysis. This is the opposite (and somewhat more specific) functionality compared to the existing engine argument, --read_group_black_list , which allows you to exclude read groups based on specific tags (including but not limited to LB).
We have a new diagnostic tool, QualifyMissingIntervals, that allows you to collect metrics such as GC content, mapping quality etc. for a list of intervals of interest. This is something you'd typically want to use if you found (through other tools) that you're missing calls in certain intervals, and you want to find out what's going wrong in those regions.
Finally, those of you who have access to more sophisticated computing platforms, heads up! Version 2.7 comes with a version of the PairHMM algorithm (aka the bit that takes forever to run in HaplotypeCaller) that is optimized for running on FPGA chips. Credit goes to the fine folks at Convey Computer and Green Mountain Computing Systems who teamed up to develop this optimized version of the PairHMM, with a little help from our very own Tech Dev team. We're told further optimizations may be in store; in the meantime, they're seeing up to 300-fold speedups of HaplotypeCaller runs on Convey's platform. Not bad!
It's finally summer here in New England -- time for cave-dwelling developers to hit the beach and do the lobster dance (those of us who don't tan well anyway). We leave you with a new version of the GATK that includes a new(ish) plotting tool, some more performance improvements to the callers, a lot of feature tweaks and quite a few bug fixes. Be sure to check out the full list in the 2.6 Release Notes.
Highlights are below as usual, enjoy. There's one thing that we need to point out with particular emphasis: we have moved to Java 7, so you may need to update your system's Java version. Full explanation at the end of this document because it's a little long, but be sure to read it.
GATK old-timers may remember a tool called AnalyzeCovariates, which was part of the BQSR process in 1.x versions, many moons ago. Well, we've resurrected it to take over the plotting functionality of the BaseRecalibrator, to make it easier and faster to plot and compare the results of base recalibration. This also prevents issues with plot generation in scatter-gather mode. We'll update our docs on the BQSR workflow in the next few days, but in the meantime you can find full details of how to use this tool here.
We know you don't want to miss a single true variant, so for this release, we've put a lot of effort into making the HaplotypeCaller more sensitive. And it's paying off: in our tests, the HaplotypeCaller is now more sensitive than the UnifiedGenotyper for calling both SNPs and indels when run over whole genome datasets.
[graph to illustrate, coming soon]
You might think all our focus is on improving the HaplotypeCaller these days; you would be wrong. The UnifiedGenotyper is still essential for calling large numbers of samples together, for dealing with exotic ploidies, and for calling pooled samples. So we've given it a turbo boost that makes it go twice as fast for calling indels on multiple samples.
The key change here is the updated Hidden Markov Model used by the UG. You can see on the graph that as the number of exomes being called jointly increases, the new HMM keeps runtimes down significantly compared to the old HMM.
Don’t you hate it when you go back to a VCF you generated some months ago, and you have no idea which version of GATK you used at the time? (And yes, versions matter. Sometimes a lot.) We sure do, so we added a function to add the GATK version number in the header of the VCFs generated by GATK.
Speaking of software versions... As you probably know, the GATK runs on Java -- specifically, until now, version 6 of the Runtime Environment (which translates to version 1.6 if you ask
java -version at the command prompt). But the Java language has been evolving under our feet; version 7 has been out and stable for some time now, and version 8 is on the horizon. We were happy as clams with Java 6… but now, newer computers with recent OS versions ship with Java 7, and on MacOS X once you update the system it is difficult to go back to using Java 6. And since Java 7 is not fully backwards compatible, people have been running into version problems.
So, we have made the difficult but necessary decision to follow the tide, and migrate the GATK to Java 7. Starting with this release, GATK will now require Java 7 to run. If you try to run with Java 6, you will probably get an error like this:
Exception in thread "main" java.lang.UnsupportedClassVersionError: org/broadinstitute/sting/gatk/CommandLineGATK : Unsupported major.minor version 51.0
If you're not sure what version of Java you are currently using, you can find out very easily by typing the following command:
which should return something like this:
java version "1.7.0_17" Java(TM) SE Runtime Environment (build 1.7.0_17-b02) Java HotSpot(TM) 64-Bit Server VM (build 23.7-b01, mixed mode)
If not, you'll need to update your java version. If you have any difficulty doing this, please don’t ask us in the forum -- you’ll get much better, faster help if you ask your local IT department.
This is going to be a short one, folks. The 2.5 release is pretty much all about bug fixes, with a couple of exceptions that we'll cover below.
Remember how we said that version 2.4 was going to be the least buggy ever? Well, that might have been a bit optimistic. We had a couple of stumpers in there -- and a flurry of little ones that were probably not novel (i.e. not specific to version 2.5) but finally bubbled up to the surface. We're not going to go over the bug fixes in detail, since the release notes include a comprehensive list. Basically, those are all fixed.
Well, not exactly new features, but noteworthy improvements to existing tools.
In addition to countless bug fixes, we've made drastic improvements to ReduceReads' compression algorithm, so you can now achieve much better compression rates without compromising on the retention of informative data. Keep in mind of course that as always, you'll see much bigger gains on certain types of data sets -- the higher the coverage in your original BAM files, the bigger the savings in file size and performance of the downstream tools.
We say this every time, and every time it's true: we've made some more improvements to the HaplotypeCaller that make it faster and more accurate. Well, it's still slower than the UnifiedGenotyper, in case you were going to ask (of course you were). But on the accuracy front, we say this without reservation or caveat: HC is now just as accurate as the UG for calling SNPs, and it is in a league of its own for calling indels. If you are even remotely interested in indels you should absolutely take it out for a spin. Go. Now.
Say goodbye to the mood swings and the pimples; it looks like this tool's awkward teenager phase is finally over. We've entirely reworked how DiagnoseTargets functions so it now uses a plugin system, which we think is much more convenient. This plugin system will be explained in detail in a forthcoming documentation article.
You may be aware that we had imposed a freeze of sorts on the annotation database version that could be used with the snpEff annotation. Well, we're happy to report that the author of the snpEff software package has made some significant upgrades, including a feature called GATK compatibility mode. As a result there is no longer any version constraint. We'll be updating our documentation on using snpEff with GATK soon (-ish), but in the meantime, feel free to go forth and annotate away. Just make sure to consult the snpEff manual for relevant information on using it with GATK.
Even as the dev team giveth, the dev team taketh away.
A few annotations were removed from the VariantAnnotator stables (as listed in the release notes), mainly because they didn't work properly. With all the caveats about how GATK is research software, we're still committed to providing quality tools that do something close to what they're advertised to do, at the bare minimum. If something doesn't fulfill that requirement, it's out.
We've also disabled the auto-generation of fai/dict files for fasta references. I can hear some of you groaning all the way from here. Yes, it was convenient -- but far too buggy. Come on people, it's a one-liner using Picard. Oh, and we're no longer allowing the use of compressed (.gz) references either -- also too buggy. The space savings were simply not worth the headaches.
We are very proud (and more than a little relieved) to finally present version 2.4 of the GATK! It's been a long time coming, but we're certain you'll find it well worth the wait. This release is bursting at the seams with new features and improvements, as you'll read below. It is also very probably going to be our least-buggy initial release yet, thanks to the phenomenal effort that went into adding extensive automated tests to the codebase.
Important note: Keep in mind that this new release comes with a brand new license, as we announced a few weeks ago here. Be sure to at least check out the figure that explains the different packages we (and our commercial partner Appistry) offer, and get the one that is appropriate for your use of the GATK.
With that disclaimer out of the way, here are the feature highlights of version 2.4!
Let's start with what everyone wants to hear about: improvements in speed and accuracy. There are in fact far more improvements in accuracy than are described here, again because of the extensive test coverage we've added to the codebase. But here are the ones that we believe will have the most impact on your work.
We realized that even though BaseRecalibrator was doing a fabulous job in general, the calculation for the empirical quality of a bin (e.g. all bases at the 33rd cycle of a read) was not always accurate. Specifically, we would draw the same conclusions from bins with many or few observations -- but in the latter case that was not necessarily correct (we were seeing some Q6s get recalibrated up to Q30s, for example). We changed this behavior so that the BaseRecalibrator now calculates a proper Bayesian estimate of the empirical quality. As a result, for bins with very little data, the likelihood is dwarfed by a prior probability that tends towards the original quality; there is no effect on large bins, which were already fine. This brings noticeable improvements in the genotype likelihoods being produced from the genotypes, in particular for the heterozygous state (as expected).
You may remember that in the highlights for version 2.2, we were excited to announce that the HaplotypeCaller was no longer operating on geological time scales. Well, now the HC has made another big leap forward in terms of speed -- and it is now almost as fast as the UnifiedGenotyper. If you were reluctant to move from the UG to the HC based on runtime, that shouldn't be an issue anymore! Or, if you were unconvinced by the merits of the new calling algorithm, you'll be interested to know that our internal tests show that the HaplotypeCaller is now more accurate in calling variants (SNPs as well as Indels) than the UnifiedGenotyper.
How did we make this happen? There are too many changes to list here, but one of the key modifications that makes the HaplotypeCaller much faster (without sacrificing any accuracy!) is that we've greatly optimized how local Smith-Waterman re-assembly is applied. Previously, when the HC encountered a region where reassembly was needed, it performed SW re-assembly on the entire region, which was computationally very demanding. In the new implementation, the HC generates a "bubble" (yes, that's the actual technical term) around each individual haplotype, and applies the SW re-assembly only within that bubble. This brings down the computational challenge by orders of magnitude.
We're not just fluffing up the existing tools -- we're also adding new tools to extend the capabilities of our toolkit.
A new Read Filter, ReassignOneMappingQualityFilter, allows you to -- well, it's in the name -- reassign one mapping quality. This is useful for example to process data output by programs like TopHat which use MAPQ = 255 to convey meaningful information. The GATK would normally ignore any reads with that mapping quality. With the new filter, you can selectively reassign that quality to something else so that those reads will get utilized, without affecting the rest of your dataset.
In addition, the recently introduced contamination filter gets upgraded with the option to apply decontamination individually per sample.
Version 2.4 includes several new tools that grew out of existing tool options. The rationale for making them standalone tools is that they represent particularly useful capabilities that merit expansion, and expanding them within their "mother tool" was simply too cumbersome.
GenotypeConcordance graduates from being a module of VariantEval, to being its own fully-fledged tool. This comes with many bug fixes and an overhaul of how the concordance results are tabulated, which we hope will cause less confusion than it has in the past!
RegenotypeVariants takes over -- and improves upon -- the functionality previously provided by the
--regenotype option of SelectVariants. This tool allows you to refresh the genotype information in a VCF file after samples have been added or removed.
And we're also adding CatVariants, a tool to quickly combine multiple VCF files whose records are non-overlapping (e.g. as produced during scatter-gather using Queue). This should be a useful alternative to CombineVariants, which is primarily meant for more complex combination operations.
Going forward, we have decided to provide nightly automated builds from our development tree. This means that you can get the very latest development version -- no need to wait weeks for bug fixes or new features anymore! However, this comes with a gigantic caveat emptor: these are bleeding-edge versions that are likely to contain bugs, and features that have never been tested in the wild. And they're automatically generated at night, so we can't even guarantee that they'll run. All we can say of any of them is that the code was able to compile -- beyond that, we're off the hook. We won't answer support questions about the new stuff. So in short: you want to try the nightlies, you do so at your own risk.
If any of the above scares or confuses you, no problem -- just stay well clear of the owl and you won't get bitten.
But hey, if you're feeling particularly brave or lucky, have fun :)
The release of version 2.4 also coincides with some upgrades to the documentation that are significant enough to merit a brief mention.
From here on, every release (including minor releases, such as 2.3-9) will be accompanied by the generation of a PDF Guide Book that contains the online documentation articles as they are at that time. It will not only allow you to peruse the documentation offline, but it will also serve as versioned documentation. This way, if in the future you need to go back and examine results you obtained with an older version of the GATK, you can find easily find the documentation that was valid at that time. Note that the Technical Documentation (which contains the exhaustive lists of arguments for each tool) is not included in the Guide Book since it can be generated directly from the source code.
Speaking of the Technical Documentation, we are happy to announce that we've enriched those pages with additional information, including available parallelization options and default read filters for each tool, where applicable. We've also reorganized the main categories in the Technical Documentation index to make it easier to browse tools and find what you need.
Finally, a few words for developers who have previous experience with the GATK codebase. The VariantContext and related classes have been moved out of the GATK codebase and into the Picard public repository. The GATK now uses the resulting Variant.jar as an external library (currently version 1.85.1357). We've also updated the Picard and Tribble jars to version 1.84.1337.
Release version 2.3 is the last before the winter holidays, so we've done our best not to put in anything that will break easily. Which is not to say there's nothing important - this release contains a truckload of feature tweaks and bug fixes (see the release notes in the next tab for full list). And we do have one major new feature for you: a brand-spanking-new downsampler to replace the old one.
It has recently come to our attention that some datasets are not encoded in the standard format (Q0 == ASCII 33 according to the SAM specification, whereas Illumina encoding starts at ASCII 64). This is a problem because the GATK assumes that it can use the quality scores as they are. If they are in fact encoded using a different scale, our tools will make an incorrect estimation of the quality of your data, and your analysis results will be off. To prevent this from happening, we've added a sanity check of the quality score encodings that will abort the program run if they are not standard. If this happens to you, you'll need to run again with the flag
-fixMisencodedQuals). What will happen is that the engine will simply subtract 31 from every quality score as it is read in, and proceed with the corrected values. Output files will include the correct scores where applicable.
Good news on the performance front: we eliminated a bottleneck in the GATK engine that increased the runtime of many tools by as much as 10x, depending on the exact details of the data being fed into the GATK. The problem was caused by the internal timing code invoking expensive system timing resources far too often. Imagine you looked at your watch every two seconds -- it would take you ages to get anything done, right? Anyway, if you see your tools running unusually quickly, don't panic! This may be the reason, and it's a good thing.
You can now co-reduce separate BAM files by passing them in with multiple
-I or as an input list. The motivation for this is that samples that you plan to analyze together (e. g. tumor-normal pairs or related cohorts) should be reduced together, so that if a disagreement is triggered at a locus for one sample, that locus will remain unreduced in all samples. You will therefore conserve the full depth of information for later analysis of that locus.
The downsampler is the component of the GATK engine that handles downsampling, i. e. the process of removing a subset of reads from a pileup. The goal of this process is to speed up execution of the desired analysis, particularly in genome regions that are covered by excessive read depth.
In this release, we have replaced the old downsampler with a brand new one that extends some options and performs much better overall.
The GATK offers two different options for downsampling:
-dcov) enables you to set the maximum amount of coverage to keep at any position
-dfrac) enables you to remove a proportional amount of the reads at any position (e. g. take out half of all the reads)
Until now, it was not possible to use the
-dcov) option with read walkers; you were limited to using
-dfrac). In the new release, you will be able to downsample to coverage for read walkers.
However, please note that the process is a little different. The normal way of downsampling to coverage (e. g. for locus walkers) involves downsampling over the entire pileup of reads in one take. Due to technical reasons, it is still not possible to do that exact process for read walkers; instead the read-walker-compatible way of doing it involves downsampling within subsets of reads that are all aligned at the same starting position. This different mode of operation means you shouldn't use the same range of values; where you would use
-dcov 100 for a locus walker, you may need to use
-dcov 10 for a read walker. And these are general estimates - your mileage may vary depending on your dataset, so we recommend testing before applying on a large scale.
One important property of the downsampling process is that it should be as random as possible to avoid introducing biases into the selection of reads that will be kept for analysis. Unfortunately our old downsampler - specifically, the part of the downsampler that performed the downsampling to coverage - suffered from some biases. The most egregious problem was that as it walked through the data, it tended to privilege more recently encountered reads and displaced "older" reads. The new downsampler no longer suffers from these biases.
The old downsampler was embedded in the engine code in a way that made it hard to test in a systematic way. So when we implemented the new downsampler, we reorganized the code to make it a standalone engine component - the equivalent of promoting it from the cubicle farm to its own corner office. This has allowed us to cover it much better with systematic tests, so we have better assessment of whether it's working properly.
The new downsampler is enabled by default and we are confident that it works much better than the old one. BUT as with all brand-spanking-new features, early adopters may run into unexpected rough patches. So we're providing a way to disable it and use the old one, which is still in the box for now: just add
-use_legacy_downsampler to your command line. Obviously if you use this AND
-dcov with a read walker, you'll get an error, since the old downsampler can't downsample to coverage for read walkers.
We're very excited to present release version 2.2 to the public. As those of you who have been with us for a while know, it's been a much longer time than usual since the last minor release (v 2.1). Ah, but don't let the "minor" name fool you - this release is chock-full of major improvements that are going to make a big difference to pretty much everyone's use of the GATK. That's why it took longer to put together; we hope you'll agree it was worth the wait!
The biggest changes in this release fall in two categories: enhanced performance and improved accuracy. This is rounded out by a gaggle of bug fixes and updates to the resource bundle.
We know y'all have variants to call and papers to publish, so we've pulled out all the stops to make the GATK run faster without costing 90% of your grant in computing hardware. First, we're introducing a new multi-threading feature called Nanoscheduler that we've added to the GATK engine to expand your options for parallel processing. Thanks to the Nanoscheduler, we're finally able to bring multi-threading back to the BaseRecalibrator. We've also made some seriously hard-core algorithm optimizations to ReduceReads and the two variant callers, UnifiedGenotyper and HaplotypeCaller, that will cut your runtimes down so much you won't know what to do with all the free time. Or, you'll actually be able to get those big multisample analyses done in a reasonable amount of time…
This new multi-threading feature of the GATK engine allows you to take advantage of having multiple cores per machine, whether in your desktop computer or on your server farm. Basically, the Nanoscheduler creates clones of the GATK, assigns a subset of the job to each and runs it on a different core of the machine. Usage is similar to the
-nt mode you may already be familiar with, except you call this one with the new
-nct argument. Note that the Nanoscheduler currently reserves one thread for itself, which acts like a manager (it bosses the other threads around but doesn't get much work done itself) so to see any real performance gain you'll need to use at least
-nct 3, which yields two "worker" threads. This is a limitation of the current implementation which we hope to resolve soon. See the updated document on Parallelism with the GATK (v2) (link coming soon) for more details of how the Nanoscheduler works, as well as recommendations on how to optimize parallelization for each of the main GATK tools.
Many of you have complained that the rebooted BaseRecalibrator in GATK2 takes forever to run. Rightly so, because until now, you couldn't effectively run it in multi-threaded mode. The reason for that is fairly technical, but in essence, whenever a thread started working on a chunk of data it locked down access to the rest of the dataset, so any other threads would have to wait for it to finish working before they could begin. That's not really multi-threading, is it? No, we didn't think so either. So we rewrote the BaseRecalibrator to not do that anymore, and we gave it a much saner and effective way of handling thread safety: each thread locks down just the chunk of data it's assigned to process, not the whole dataset. The graph below shows the performance gains of the new system over the old one. Note that in practice, this is operated by the Nanoscheduler (see above); so remember, if you want to parallelize BaseRecalibrator, use
-nt, and be sure to assign three or more threads.
Without going into the gory technical details, we optimized the underlying compression algorithm that powers ReduceReads, and we're seeing some very significant improvements in runtime. For a "best-case scenario" BAM file, i.e. a well-formatted BAM with no funny business, the average is about a three-fold decrease in runtime. Yes, it's three times faster! And if that doesn't impress you, you may be interested to know that for "worst-case scenario" BAM files (which are closer to what we see in the wild, so to speak, than in our climate-controlled test facility) we see orders of magnitude of difference in runtimes. That's tens to hundreds of times faster. To many of you, that will make the difference between being able to reduce reads or not. Considering how reduced BAMs can help bring down storage needs and runtimes in downstream operations as well -- it's a pretty big deal.
Ah, another algorithm optimization that makes things go faster. This one affects the EXACT model that underlies how the UG calls variants. We've modified it to use a new approach to multiallelic discovery, which greatly improves scalability of joint calling for multi-sample projects. Previously, the relationship between the number of possible alternate alleles and the difficulty of the calculation (which directly impacts runtime) was exponential. So you had to place strict limits on the number of alternate alleles allowed (like 3, tops) if you wanted the UG run to finish during your lifetime. With the updated model, the relationship is linear, allowing the UG to comfortably handle around 6 to 10 alternate alleles without requiring some really serious hardware to run on. This will mostly affect projects with very diverse samples (as opposed to more monomorphic ones).
The last algorithm optimization for this release, but certainly not the least (there is no least, and no parent ever has a favorite child), this one affects the likelihood model used by the HaplotypeCaller. Previously, the HaplotypeCaller's HMM required calculations to be made in logarithmic space in order to maintain precision. These log-space calculations were very costly in terms of performance, and took up to 90% of the runtime of the HaplotypeCaller. Everyone and their little sister has been complaining that it operates on a geological time scale, so we modified it to use a new approach that gets rid of the log-space calculations without sacrificing precision. Words cannot express how well that worked, so here's a graph.
This graph shows runtimes for HaplotypeCaller and UnifiedGenotyper before (left side) and after (right side) the improvements described above. Note that the version numbers refer to development versions and do not map directly to the release versions.
Alright, going faster is great, I hear you say, but are the results any good? We're a little insulted that you asked, but we get it -- you have responsibilities, you have to make sure you get the best results humanly possible (and then some). So yes, the results are just as good with the faster tools -- and we've actually added a couple of features to make them even better than before. Specifically, the BaseRecalibrator gets a makeover that improves indel scores, and the UnifiedGenotyper gets equipped with a nifty little trick to minimize the impact of low-grade sample contamination.
When we brought multi-threading back to the BaseRecalibrator, we also revamped how the tool evaluates each read. Previously, the BaseRecalibrator accepted the read alignment/position issued by the aligner, and made all its calculations based on that alignment. But aligners make mistakes, so we've rewritten it to also consider other possible alignments and use a probabilistic approach to make its calculations. This delocalized approach leads to improved accuracy for indel quality scores.
In an ideal world, your samples would never get contaminated by other DNA. This is not an ideal world. Sample contamination happens more often than you'd think; usually at a low-grade level, but still enough to skew your results. To counteract this problem, we've added a contamination filter to the UnifiedGenotyper. Given an estimated level of contamination, the genotyper will downsample reads by that fraction for each allele group. By default, this number is set at 5% for high-pass data. So in other words, for each allele it detects, the genotyper throws out 5% of reads that have that allele.
We realize this may raise a few eyebrows, but trust us, it works, and it's safe. This method respects allelic proportions, so if the actual contamination is lower, your results will be unaffected, and if a significant amount of contamination is indeed present, its effect on your results will be minimized. If you see differences between results called with and without this feature, you have a contamination problem.
Note that this feature is turned ON by default. However it only kicks in above a certain amount of coverage, so it doesn't affect low-pass datasets.
We've added a lot of systematic tests to the new tools and features that were introduced in GATK 2.0 and 2.1 (Full versions), such as ReduceReads and the HaplotypeCaller. This has enabled us to flush out a lot of the "growing pains" bugs, in addition to those that people have reported on the forum, so all that is fixed now. We realize many of you have been waiting a long time for some of these bug fixes, so we thank you for your patience and understanding. We've also fixed the few bugs that popped up in the mature tools; these are all fixed in both Full and Lite versions of course.
Details will be available in the new Change log shortly.
Finally, we've updated the resource bundle with a variant callset that can be used as a standard for setting up your variant calling pipelines. Briefly, we generated this callset from the raw BAMs of our favorite trio (CEU Trio) according to our Best Practices (using the UnifiedGenotyper on unreduced BAMs). We additionally phased the calls using PhaseByTransmission. We've also updated the HapMap VCF.
Note that from now on, we plan to generate a new callset with each major and minor release, and the numbering of the bundle versions will follow the GATK version numbers to avoid any confusion.
Note: There are no version highlights available for versions earlier than 2.2.
GATK 3.2 was released on July 14, 2014. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
We also want to take this opportunity to thank super-user Phillip Dexheimer for all of his excellent contributions to the codebase, especially for this release.
optfunctions to work with recent versions of the ggplot2 R library.
optfunctions to work with recent versions of the ggplot2 R library.
GATK 3.1 was released on March 18, 2014. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
--pair_hmm_implementation VECTOR_LOGLESS_CACHING. Please see the 3.1 Version Highlights for more details about expected speed ups and some background on the collaboration that made these possible.
GATK 3.0 was released on March 5, 2014. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
One important change for those who prefer to build from source is that we now use maven instead of ant. See the relevant documentation for building the GATK with our new build system.
GATK 2.8 was released on December 6, 2013. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
Note that this release is relatively smaller than previous ones. We are working hard on some new tools and frameworks that we are hoping to make available to everyone for our next release.
GATK 2.7 was released on August 21, 2013. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
GATK 2.6 was released on June 20, 2013. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
Important note: with this release the GATK has officially moved to using Java 7.
GATK 2.5 was released on April 30, 2013. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
java -cp GenomeAnalysisTK.jar org.broadinstitute.sting.tools.ListAnnotations
GATK 2.4 was released on February 26, 2013. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
Important note 1 for this release: with this release comes an updated licensing structure for the GATK. Different files in our public repository are protected with different licenses, so please see the text at the top of any given file for details as to its particular license.
Important note 2 for this release: the GATK team spent a tremendous amount of time and engineering effort to add extensive tests for many of our core tools (a process that will continue into future releases). Unsurprisingly, as part of this process many small (and some not so small) bugs were uncovered during testing that we subsequently fixed. While we usually attempt to enumerate in our release notes all of the bugs fixed during a given release, that would entail quite a Herculean effort for release 2.4; so please just be aware that there were many smaller fixes that may be omitted from these notes.
GATK 2.3 was released on December 17, 2012. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
GATK release 2.2 was released on October 31, 2012. Highlights are listed below. Read the detailed version history overview here: http://www.broadinstitute.org/gatk/guide/version-history
The GATK 2.0 release includes both the addition of brand-new (and often still experimental) tools and updates to the existing stable tools.
Note: There are no release notes available for versions earlier than 2.0.
Note: There are no PDF files of the Guide Book available for versions earlier than 2.3-9.