-- New -a argument in the VQSR for specifying additional data to be used in the clustering
-- New NA12878KB walker which creates ROC curves by partitioning the data along VQSLOD and calculating how many KB TP/FP's are called.
For example, this tool can be used for processing bowtie RNA-seq data.
Each read with k N-cigar elemments is plit to k+1 reads. The split is done by hard clipping the bases rest of the bases.
In order to do it, few changes were introduced to some other clipping methods:
- make a segnificant change in ClippingOp.hardClip() that prevent the spliting of read with cigar: 1M2I1N1M3I.
- change getReadCoordinateForReferenceCoordinate in ReadUtil to recognize Ns
create unitTests for that walker:
- change ReadClipperTestUtils to be more general in order to use its code and avoid code duplication
- move some useful methods from ReadClipperTestUtils to CigarUtils
create integration test for that class
small change in a comment in FullProcessingPipeline
last commit:
Address review comments:
- move to protected under walkers/rnaseq
- change the read splitting methods to be more readable and more efficiant
- change (minor changes) some methods in ReadClipper to allow the changes in split reads
- add (minor change) one method to CigarUtils to allow the changes in split reads
- change ReadUtils.getReadCoordinateForReferenceCoordinate to include possible N in the cigar
- address the rest of the review comments (minor changes)
- fix ReadUtilsUnitTest.testReadWithNs acoording to the defult behaviour of getReadCoordinateForReferenceCoordinate (in case of refernce index that fall into deletion, return the read index of the base before the deletion).
- add another test to ReadUtilsUnitTest.testReadWithNs
- Allow the user to print the split positions (not working proparly currently)
Basically, it does 3 things (as opposed to having to call into 3 separate walkers):
1. merge the records at any given position into a single one with all alleles and appropriate PLs
2. re-genotype the record using the exact AF calculation model
3. re-annotate the record using the VariantAnnotatorEngine
In the course of this work it became clear that we couldn't just use the simpleMerge() method used
by CombineVariants; combining HC-based gVCFs is really a complicated process. So I added a new
utility method to handle this merging and pulled any related code out of CombineVariants. I tried
to clean up a lot of that code, but ultimately that's out of the scope of this project.
Added unit tests for correctness testing.
Integration tests cannot be used yet because the HC doesn't output correct gVCFs.
Renamed it CalculateGenotypePosteriors.
Also, moved the utility code to a proper utility class instead of where Chris left it.
No actual code modifications made in this commit.
In general, test classes cannot use 3rd-party libraries that are not
also dependencies of the GATK proper without causing problems when,
at release time, we test that the GATK jar has been packaged correctly
with all required dependencies.
If a test class needs to use a 3rd-party library that is not a GATK
dependency, write wrapper methods in the GATK utils/* classes, and
invoke those wrapper methods from the test class.
Previously, we would strip out the PLs and AD values since they were no longer accurate. However, this is not ideal because
then that information is just lost and 1) users complain on the forum and post it as a bug and 2) it gives us problems in both
the current and future (single sample) calling pipelines because we subset samples/alleles all the time and lose info.
Now the PLs and AD get correctly selected down.
While I was in there I also refactored some related code in subsetDiploidAlleles(). There were no real changes there - I just
broke it out into smaller chunks as per our best practices.
Added unit tests and updated integration tests.
Addressed reviews.
To active this feature add '--likelihoodCalculationEngine GraphBased' to the HC command line.
New HC Options (both Advanced and Hidden):
==========================================
--likelihoodCalculationEngine PairHMM/GraphBased/Random (default PairHMM)
Specifies what engine should be used to generate read vs haplotype likelihoods.
PairHMM : standard full-PairHMM approach.
GraphBased : using the assembly graph to accelarate the process.
Random : generate random likelihoods - used for benchmarking purposes only.
--heterogeneousKmerSizeResolution COMBO_MIN/COMBO_MAX/MAX_ONLY/MIN_ONLY (default COMBO_MIN)
It idicates how to merge haplotypes produced using different kmerSizes.
Only has effect when used in combination with (--likelihooCalculationEngine GraphBased)
COMBO_MIN : use the smallest kmerSize with all haplotypes.
COMBO_MAX : use the larger kmerSize with all haplotypes.
MIN_ONLY : use the smallest kmerSize with haplotypes assembled using it.
MAX_ONLY : use the larger kmerSize with haplotypes asembled using it.
Major code changes:
===================
* Introduce multiple likelihood calculation engines (before there was just one).
* Assembly results from different kmerSies are now packed together using the AssemblyResultSet class.
* Added yet another PairHMM implementation with a different API in order to spport
local PairHMM calculations, (e.g. a segment of the read vs a segment of the haplotype).
Major components:
================
* FastLoglessPairHMM: New pair-hmm implemtation using some heuristic to speed up partial PairHMM calculations
* GraphBasedLikelihoodCalculationEngine: delegates onto GraphBasedLikelihoodCalculationEngineInstance the exectution
of the graph-based likelihood approach.
* GraphBasedLikelihoodCalculationEngineInstance: one instance per active-region, implements the graph traversals
to calcualte the likelihoods using the graph as an scafold.
* HaplotypeGraph: haplotype threading graph where build from the assembly haplotypes. This structure is the one
used by GraphBasedLikelihoodCalculationEngineInstance to do its work.
* ReadAnchoring and KmerSequenceGraphMap: contain information as how a read map on the HaplotypeGraph that is
used by GraphBasedLikelihoodCalcuationEngineInstance to do its work.
Remove mergeCommonChains from HaplotypeGraph creation
Fixed bamboo issues with HaplotypeGraphUnitTest
Fixed probrems with HaplotypeCallerIntegrationTest
Fixed issue with GraphLikelihoodVsLoglessAccuracyIntegrationTest
Fixed ReadThreadingLikelihoodCalculationEngine issues
Moved event-block iteration outside GraphBased*EngineInstance
Removed unecessary parameter from ReadAnchoring constructor.
Fixed test problem
Added a bit more documentation to EventBlockSearchEngine
Fixing some private - protected dependency issues
Further refactoring making GraphBased*Instance and HaplotypeGraph slimmer. Addressed last pull request commit comments
Fixed FastLoglessPairHMM public -> protected dependency
Fixed probrem with HaplotypeGraph unit test
Adding Graph-based likelihood ratio calculation to HC
To active this feature add '--likelihoodCalculationEngine GraphBased' to the HC command line.
New HC Options (both Advanced and Hidden):
==========================================
--likelihoodCalculationEngine PairHMM/GraphBased/Random (default PairHMM)
Specifies what engine should be used to generate read vs haplotype likelihoods.
PairHMM : standard full-PairHMM approach.
GraphBased : using the assembly graph to accelarate the process.
Random : generate random likelihoods - used for benchmarking purposes only.
--heterogeneousKmerSizeResolution COMBO_MIN/COMBO_MAX/MAX_ONLY/MIN_ONLY (default COMBO_MIN)
It idicates how to merge haplotypes produced using different kmerSizes.
Only has effect when used in combination with (--likelihooCalculationEngine GraphBased)
COMBO_MIN : use the smallest kmerSize with all haplotypes.
COMBO_MAX : use the larger kmerSize with all haplotypes.
MIN_ONLY : use the smallest kmerSize with haplotypes assembled using it.
MAX_ONLY : use the larger kmerSize with haplotypes asembled using it.
Major code changes:
===================
* Introduce multiple likelihood calculation engines (before there was just one).
* Assembly results from different kmerSies are now packed together using the AssemblyResultSet class.
* Added yet another PairHMM implementation with a different API in order to spport
local PairHMM calculations, (e.g. a segment of the read vs a segment of the haplotype).
Major components:
================
* FastLoglessPairHMM: New pair-hmm implemtation using some heuristic to speed up partial PairHMM calculations
* GraphBasedLikelihoodCalculationEngine: delegates onto GraphBasedLikelihoodCalculationEngineInstance the exectution
of the graph-based likelihood approach.
* GraphBasedLikelihoodCalculationEngineInstance: one instance per active-region, implements the graph traversals
to calcualte the likelihoods using the graph as an scafold.
* HaplotypeGraph: haplotype threading graph where build from the assembly haplotypes. This structure is the one
used by GraphBasedLikelihoodCalculationEngineInstance to do its work.
* ReadAnchoring and KmerSequenceGraphMap: contain information as how a read map on the HaplotypeGraph that is
used by GraphBasedLikelihoodCalcuationEngineInstance to do its work.
Remove mergeCommonChains from HaplotypeGraph creation
Fixed bamboo issues with HaplotypeGraphUnitTest
Fixed probrems with HaplotypeCallerIntegrationTest
Fixed issue with GraphLikelihoodVsLoglessAccuracyIntegrationTest
Fixed ReadThreadingLikelihoodCalculationEngine issues
Moved event-block iteration outside GraphBased*EngineInstance
Removed unecessary parameter from ReadAnchoring constructor.
Fixed test problem
Added a bit more documentation to EventBlockSearchEngine
Fixing some private - protected dependency issues
Further refactoring making GraphBased*Instance and HaplotypeGraph slimmer. Addressed last pull request commit comments
Fixed FastLoglessPairHMM public -> protected dependency
Fixed probrem with HaplotypeGraph unit test
-- For very large whole genome datasets with over 2M variants overlapping the training data randomly downsample the training set that gets used to build the Gaussian mixture model.
-- Annotations are ordered by the difference in means between known and novel instead of by their standard deviation.
-- Removed the training set quality score threshold.
-- Now uses 2 gaussians by default for the negative model.
-- Num bad argument has been removed and the cutoffs are now chosen by the model itself by looking at the LOD scores.
-- Model plots are now generated much faster.
-- Stricter threshold for determining model convergence.
-- All VQSR integration tests change because of these changes to the model.
-- Add test for downsampling of training data.
For reads with high MQs (greater than max byte) the MQ was being treated as negative and failing
the min MQ filter.
Added unit test.
Delivers PT#61567540.
His code was excessively clipping reads because it was looking at their cigar string instead of just
the read length. This meant that it was basically impossible to call large deletions in UG even with
perfect evidence in the reads (as reported by Craig D).
Integration tests change because (IMO after looking at sites in IGV) reads with indels similar to the one
being genotyped used to be given too much likelihood and now give less.
Added unit tests for new methods.
CalculatePosteriors enables the user to calculate genotype likelihood posteriors (and set genotypes accordingly) given one or more panels containing allele counts (for instance, calculating NA12878 genotypes based on 1000G EUR frequencies). The uncertainty in allele frequency is modeled by a Dirichlet distribution (parameters being the observed allele counts across each allele), and the genotype state is modeled by assuming independent draws (Hardy-Weinberg Equilibrium). This leads to the Dirichlet-Multinomial distribution.
Currently this is implemented only for ploidy=2. It should be straightforward to generalize. In addition there's a parameter for "EM" that currently does nothing but throw an exception -- another extension of this method is to run an EM over the Maximum A-Posteriori (MAP) allele count in the input sample as follows:
while not converged:
* AC = [external AC] + [sample AC]
* Prior = DirichletMultinomial[AC]
* Posteriors = [sample GL + Prior]
* sample AC = MLEAC(Posteriors)
This is more useful for large callsets with small panels than for small callsets with large panels -- the latter of these being the more common usecase.
Fully unit tested.
Reviewer (Eric) jumped in to address many of his own comments plus removed public->protected dependencies.
There was already a note in the code about how wrong the implementation was.
The bad code was causing a single-node graph to get cleaned up into nothing when pruning tails.
Delivers PT #61069820.
-- We use the RegenotypeVariants walker to recompute the qual field. (instead of the discussed idea of adding this functionality to CombineVariants)
-- QualByDepth will now be recomputed even if the stratified contexts are missing. This greatly improves the QD estimate for this pipeline. Doesn't work for multi-allelics since the qual can't be recomputed.
* add a new column to do what I have been doing manually for every project, understand why we got no usable coverage in that interval
* add unit tests -- this tool is now public, we need tests.
* slightly better docs -- in an effort to produce better docs for this tool
-- Adding changes to CombineVariants to work with the Reference Model mode of the HaplotypeCaller.
-- Added -combineAnnotations mode to CombineVariants to merge the info field annotations by taking the median
-- Added new StrandBiasBySample genotype annotation for use in computing strand bias from single sample input vcfs
-- Bug fixes to calcGenotypeLikelihoodsOfRefVsAny, used in isActive() as well as the reference model
-- Added active region trimming capabilities to the reference model mode, not perfect yet, turn off with --dontTrimActiveRegions
-- We only realign reads in the reference model if there are non-reference haplotypes, a big time savings
-- We only realign reads in the reference model if the read is informative for a particular haplotype over another
-- GVCF blocks will now track and output the minimum PLs over the block
-- MD5 changes!
-- HC tests: from bug fixes in calcGenotypeLikelihoodsOfRefVsAny
-- GVCF tests: from HC changes above and adding in active region trimming
PairHMMSyntheticBenchmark and PairHMMEmpirical benchmark were written to test the banded pairHMM, and were archived along with it. I returned them to the test directory for use in benchmarking the ArrayLoglessPairHMM. I commented out references to the banded pairHMM (which was left in archive), rather than removing those references entirely.
Renamed PairHMMEmpiricalBenchmark to PairHMMBandedEmpiricalBenchmark and returned it to the archive. It has a few problems for use as a general benchmark, including initializing the HMM too frequently and doing too much setup work in the 'time' method. However, since the size selection and debug printing are useful for testing the banded implementation, I decided to keep it as-is and archive it alongside with the other banded pairHMM classes. I did fix one bug that was causing the selectWorkingData function to return prematurely. As a result, the benchmark was only evaluating 4-40 pairHMM calls instead of the desired "maxRecords".
I wrote a new PairHMMEmpiricalBenchmark that simply works through a list of data, with setup work and hmm-initialization moved to its own function. This involved writing a new data read-in function in PairHMMTestData. The original was not maintaining the input data in order, the end result of which would be an over-estimate of how much caching we are able to do. The new benchmark class more closely mirrors real-world operation over large data.
It might be cleaner to fix some of the issues with the BandedEmpiricalBenchmark and use one read-in function. However, this would involve more extensive changes to:
PairHMMBandedEmpiricalBenchmark
PairHMMTestData
BandedLoglessPairHMMUnitTest
I decided against this as the banded benchmark and unit test are archived.
A new PairHMM implementation for read/haplotype likelihood calculations. Output is the same as the LOGLESS_CACHING version.
Instead of allocating an entire (read x haplotype) matrix for each HMM state, this version stores sub-computations in 1D arrays. It also accesses intersections of the (read x haplotype) alignment in a different order, proceeding over "diagonals" if we think of the alignment as a matrix.
This implementation makes use of haplotype caching. Because arrays are overwritten, it has to explicitly store mid-process information. Knowing where to capture this info requires us to look ahead at the subsequent haplotype to be analyzed. This necessitated a signature change in the primary method for all pairHMM implementations.
We also had to adjust the classes that employ the pairHMM:
LikelihoodCalculationEngine (used by HaplotypeCaller)
PairHMMIndelErrorModel (used by indel genotyping classes)
Made the array version the default in the HaplotypeCaller and the UnifiedArgumentCollection.
The latter affects classes:
ErrorModel
GeneralPloidyIndelGenotypeLikelihoodsCalculationModel
IndelGenotypeLikelihoodsCalculationModel
... all of which use the pairHMM via PairHMMIndelErrorModel
-This was a dependency of the test suite, but not the GATK proper,
which caused problems when running the test suite on the packaged
GATK jar at release time
-Use GATKVCFUtils.readVCF() instead
* Refactoring implementations of readHeader(LineReader) -> readActualHeader(LineIterator), including nullary implementations where applicable.
* Galvanizing fo generic types.
* Test fixups, mostly to pass around LineIterators instead of LineReaders.
* New rev of tribble, which incorporates a fix that addresses a problem with TribbleIndexedFeatureReader reading a header twice in some instances.
* New rev of sam, to make AbstractIterator visible (was moved from picard -> sam in Tribble API refactor).
There is now a command-line option to set the model to use in the HC.
Incorporated Ryan's current (unmerged) branch in for most of these changes.
Because small changes to the math can have drastic effects, I decided not to let users tweak
the calculations themselves. Instead they can select either NONE, CONSERVATIVE (the default),
or AGGRESSIVE.
Note that any base insertion/deletion qualities from BQSR are still used.
Also, note that the repeat unit x repeat length approach gave very poor results against the KB,
so it is not included as an option here.
-- When provided, this argument causes us to only emit the selected samples into the VCF. No INFO field annotations (AC for example) or other features are modified. It's current primary use is for efficiently evaluating joint calling.
-- Add integration test for onlyEmitSamples
-- The previous approach in VQSR was to build a GMM with the same max. number of Gaussians for the positive and negative models. However, we usually have many more positive sites than negative, so we'd prefer to use a more detailed GMM for the positive model and a less well defined model using few sites for the negative model.
-- Now the maxGaussians argument only applies to the positive model
-- This update builds a GMM for the negative model with a default 4 max gaussians (though this can be controlled via command line parameter)
-- Removes the percentBadVariants argument. The only way to control how many variants are included in the negative model is with minNumBad
-- Reduced the minNumBad argument default to 1000 from 2500
-- Update MD5s for VQSR. md5s changed significantly due to underlying changes in the default GMM model. Only sites with NEGATIVE_TRAINING_LABELs and the resulting VQSLOD are different, as expected.
-- minNumBad is now numBad
-- Plot all negative training points as well, since this significantly changes our view of the GMM PDF
-- In the case where there's some variation to assembly and evaluate but the resulting haplotypes don't result in any called variants, the reference model would exception out with "java.lang.IllegalArgumentException: calledHaplotypes must contain the refHaplotype". Now we detect this case and emit the standard no variation output.
-- [delivers #54625060]
Problem
-------
Caching strategy is incompatible with the current sorting of the haplotypes, and is rendering the cache nearly useless.
Before the PairHMM updates, we realized that a lexicographically sorted list of haplotypes would optimize the use of the cache. This was only true until we've added the initial condition to the first row of the deletion matrix, which depends on the length of the haplotype. Because of that, every time the haplotypes differ in length, the cache has to be wiped. A lexicographic sorting of the haplotypes will put different lengths haplotypes clustered together therefore wasting *tons* of re-compute.
Solution
-------
Very simple. Sort the haplotypes by LENGTH and then in lexicographic order.
1. Removing old legacy code that was capping the positional depth for reduced reads to 127.
Unfortunately this cap affectively performs biased down-sampling and throws off e.g. FS numbers.
Added end to end unit test that depth counts in RR can be higher than max byte.
Some md5s change in the RR tests because depths are now (correctly) no longer capped at 127.
2. Down-sampling in ReduceReads was not safe as it could remove het compressed consensus reads.
Refactored it so that it can only remove non-consensus reads.
droazen
Eric Banks
Menachem Fromer
Ryan Poplin
amilev
Valentin Ruano-Rubio
Joel Thibault
Mauricio Carneiro
Chris Hartl
Chris Hartl
Michael McCowan
Geraldine Van der Auwera
Louis Bergelson
bradtaylor
Mark DePristo
Yossi Farjoun