* This is a shortcut for people who have multi-sample BAMs but would like to use GVCF mode. Rather than creating single-sample BAMs with PrintReads, one could use the --sample_name argument to HaplotypeCaller to specify the single sample to make calls on
* Completes PT 73075482
Story:
https://www.pivotaltracker.com/story/show/77250524
Changes:
- Remove the annotating code in GeneralPloidyExactAFCalc (GPEAFC) class.
- Added the asAlleleList to GenotypeAlleleCounts class and get (GPEAFC) to use that instead of implementing its own (nicer and more reusable code).
- Removed the explicit addition of AlleleCountBySample fields to the VCF header by the walker initialize
- Added utility methods in Utils to wrap and int[] array into a List<Integer>, and double[] array into a List<Double> efficiently.
Test:
- Added unit-testing for asAlleleList in GenotypeAlleleCountsUnitTest (within testFirst and testNext).
- Added unit-testing for new methods in Utils : asList(int[]) and asList(double[])
- Changed UG General Ploidy test to add explicitly those annotations.
- Non-trivial changes in integration tests involving non-diploid runs (namelly haploid and tetraploid) as they are not showing
those annotations anylonger, so the MD5s have been changed accordingly.
It turns out that there can be some really complex situations even with a single sample where
there are lots of unphasable hets around a hom. Previously we were trying to phase each of the
hets against the hom, but that wasn't correct. Instead we now detect that situation and don't
attempt to phase anything.
Added a unit test to cover this situation.
New annotation for low= and high-confidence de novos (only annotates biallelics)
FamilyLikelihoodsUtils now add joint likelihood and joint posterior annotations
Restrict population priors based on discovered allele count to be valid for 10 or more samples.
VariantAnnotator/FS behavior changes slightly: VA used to output zeros for FS if there was no strand bias info, now skips FS output (but will still show FS in header)
Changes in several walker to use new sample, allele closed lists and new GenotypingEngine constructors signatures
Rebase adoption of new calculation system in walkers
1. It is now turned on by default
2. It now phases homozygous variants
3. Most importantly, it also phases variants that are always on opposite haplotypes
Changed the INFO keys to be PID and PGT, as described in the header.
If any pair of variants occurs on all used haplotypes together, then we propagate that information into the gVCF.
Can be enabled with the --tryPhysicalPhasing argument.
Stories:
https://www.pivotaltracker.com/story/show/70222086https://www.pivotaltracker.com/story/show/67961652
Changes:
Done some changes that I missed in relation with making sure that all PairHMM implentations use the same interface; as a consequence we were running always the standard PairHMM.
Fixed some additional bugs detected when running it on full wgs single sample and exom multi sample data set.
Updated some integration test md5s.
Stories:
https://www.pivotaltracker.com/story/show/70222086https://www.pivotaltracker.com/story/show/67961652
Changes:
Done some changes that I missed in relation with making sure that all PairHMM implentations use the same interface; as a consequence we were running always the standard PairHMM.
Fixed some additional bugs detected when running it on full wgs single sample and exom multi sample data set.
Updated some integration test md5s.
Fixing GraphBased bugs with new master code
Fixed ReadLikelihoods.changeReads difficult to spot bug.
Changed PairHMM interface to fix a bug
Fixed missing changes for various PairHMM implementations to get them to use the new structure.
Fixed various bugs only detectable when running with full sample(s).
Believe to have fixed the lack of annotations in UG runs
Fixed integrationt test MD5s
Updating some md5s
Fixed yet another md5 probably left out by mistake
The array structure should be faster to populate and query (no properly benchmarked) and reduce memory footprint considerably.
Nevertheless removing PairHMM factor (using likelihoodEngine Random) it only achieves a speed up of 15% in some example WGS dataset
i.e. there are other bigger bottle necks in the system. Bamboo tests also seem to run significantly faster with this change.
Stories:
https://www.pivotaltracker.com/story/show/70222086https://www.pivotaltracker.com/story/show/67961652
Changes:
- ReadLikelihoods added to substitute Map<String,PerSampleReadLikelihoods>
- Operation that involve changes in full sets of ReadLikelihoods have been moved into that class.
- Simplified a bit the code that handles the downsampling of reads based on contamination
Caveats:
- Still we keep Map<String,PerReadAlleleLikelihoodsMap> around to pass to annotators..., didn't feel like change the interface of so many public classes in this pull-request.
In particular, it was possible to specify arguments for Files or Compound types without values
Added a special "none" value for annotations, since a bare "-A" is no longer allowed
Delivers PT 71792842 and 59360374
Story:
https://www.pivotaltracker.com/story/show/73440292
Changes:
- Just add the conditional in HaplotypeCaller#initialize
Testing:
- Nothing added, checked locally, trivial change that would eventually be removed anyway.
Don't expand out source nodes for tail merging, since that's a head merging action only.
This shows up as a bug only because we now allow merging tails against non-reference paths.
- Edited intervals merging docs for correctness & clarity
- Edited VQSR arg docs and made mode required (+added -mode SNP to VQSR tests)
- Moved PaperGenotyper to Toy Walkers to declutter the actually useful docs
- Moved GenotypeGVCFs to Variant Discovery category and clarified a few points
- Clarified that the -resource argument depends on using the -V:tag format
- Clarified how the pcr indel model works
- Added caveat for -U ALLOW_N_CIGAR_READS
- Added MathJax support for displaying equations in GATKDocs
- Updated HC example commands and caveats
This is useful for e.g. cases where there are SNPs on insertions. Before tails were forced to be merged
(incorrectly) only to a reference node, but now they can be merged to any path in the graph from which they
directly branch.
Also, I've transferred over Ryan's code to refuse to process kmer sizes such that there are non-unique kmers
in the reference sequence with them.
-- Global mismapping penalty was only applied to the reference haplotype. This led to problems with overlapping events, mostly STR haplotypes. Now the penalty is applied to every haplotype.
-- We subset the reads down to only those which overlap the event (after assembly based realignment) for likelihood calculations.
In these cases, where the alignment contains multiple indels, we output a single complex
variant instead of the multiple partial indels.
We also re-enable dangling tail recovery by default.
-- AD,DP will now correspond directly to the reads that were used to construct the PLs
-- RankSumTests, etc. will use the bases from the realigned reads instead of the original alignments
-- There is now no additional runtime cost to realign the reads when using bamout or GVCF mode
-- bamout mode no longer sets the mapping quality to zero for uninformative reads, instead the read will not be given an HC tag
(Right now it only works if all members of the trio are called.)
Takes posteriors as input, defaulting to PLs
Added annotations for possible de novos for us in full genotype refinement pipeline
Added family priors to CGP integration test.
Changed CGP to use PP tag instead of GP tag because posteriors are Phred-scaled. Updated CGP integration test md5s to reflect change.
- New arguments are nda, hets, indelHeterozygosity, stand_call_conf, stand_emit_conf, ploidy, and maxAltAlleles
- Addresses PT 70110918
- To do this, moved those arguments out of the StandardCallerArgumentCollection into a new GenotypeCalculationArgumentCollection, which is now included as a member of SCAC
-They are now only computed when necessary
-Log10Cache is dynamically resizable, either by calling get() on an out-of-range value or by calling ensureCacheContains
-Log10FactorialCache and JacobianLogTable are initialized to a fixed size on first access and are not resizable
-Addresses PT 69124396
-Make BaseTest.createTempFile() mark any possible corresponding index files for deletion on exit
-Make WalkerTest mark shadow BCF files and auxiliary for deletion on exit
-Make VariantRecalibrationWalkersIntegrationTest mark PDF files for deletion on exit
-- disabling HC+VA integration test because, as noted in the comments, it keeps switching PairHMM implementations and giving different results at a particular site used in that particular test
Stories:
- https://www.pivotaltracker.com/story/show/69577868
Changes:
- Added a epsilon difference tolerance in weight comparisons.
Tests:
- Added HaplotypeCallerIntegrationTest#testDifferentIndelLocationsDueToSWExactDoubleComparisonsFix
- Updated md5 due to minor likelihood changes.
- Disabled a test for PathUtils.calculateCigar since does not work and is unclear what is causing the error (needs original author input)
To reduce merge conflicts, this commit modifies contents of files, while file renamings are in previous commit.
See previous commit message for list of changes.
To reduce merge conflicts, this commit only renames files, while file modifications are in next commit.
Some updates/fixes here are actually included in the next commit.
= Maven updates
Moved artifacts to new package names:
* private/queue-private -> private/gatk-queue-private
* private/gatk-private -> private/gatk-tools-private
* public/gatk-package -> protected/gatk-package-distribution
* public/queue-package -> protected/gatk-queue-package-distribution
* protected/gatk-protected -> protected/gatk-tools-protected
* public/queue-framework -> public/gatk-queue
* public/gatk-framework -> public/gatk-tools-public
New poms for new artifacts and packages:
* private/gatk-package-internal
* private/gatk-queue-package-internal
* private/gatk-queue-extensions-internal
* protected/gatk-queue-extensions-distribution
* public/gatk-engine
Updated references to StingText.properties to GATKText.properties.
Updated ant-bridge.sh to use gatk.* properties instead of sting.*.
= Engine updates
Renaming files containing engine parts from o.b.gatk.tools to o.b.gatk.engine.
Changed package references from tools to engine for CommandLineGATK, GenomeAnalysisEngine, ReadMetrics, ReadProperties, and WalkerManager.
Changed package reference tools.phonehome to engine.phonehome.
Renamed classes *Sting* to *GATK*, such as ReviewedGATKException.
= Test updates
Moved gatk example resources.
Moved test engine files from tools to engine packages.
Moved resources for phonehome to proper package.
Moved test classes under o.b.gatk into packages:
* o.b.g.utils.{BaseTest,ExampleToCopyUnitTest,GATKTextReporter,MD5DB,MD5Mismatch,TestNGTestTransformer}
* o.b.g.engine.walkers.WalkerTest
Updated package names in DependencyAnalyzerOutputLoaderUnitTest's data.
= Queue updates
Moving queue scripts to location where generated extensions can be used.
Renamed *.q to *.scala, updating licenses previously missed by git hooks.
Moved queue extensions to new artifact gatk-queue-extensions.
Fixed import statments frequently merge-conflicting on FullProcessingPipeline.scala.
= BWA
Added README on how to obtain and include bwa as a library.
Updated libbwa build.
Fixed packaged names under bwa/java implementation.
Updated contents of BWCAligner native implementation.
= Other fixes
Don't duplicate the resource bundle entries by both unpacking *and* appending.
(partial fix) Staged engine and utils poms to build GATKText.properties, once Utils random generator dependency on GATK engine is fixed.
Re-enabled custom testng listeners/reporters and moved testng dependencies to the gatk-root.
Updated comments referencing Sting with GATK.
Moved a couple untangled classes from gatk-tools-public to gatk-utils and gatk-engine.
The JNI treats shared memory as critical memory and doesn't allow any
parallel reads or writes to it until the native code finishes. This is
not a problem *per se* it is the right thing to do, but we need to
enable **-nct** when running the haplotype caller and with it have
multiple native PairHMM running for each map call.
Move to a copy based memory sharing where the JNI simply copies the
memory over to C++ and then has no blocked critical memory when running,
allowing -nct to work.
This version is slightly (almost unnoticeably) slower with -nct 1, but
scales better with -nct 2-4 (we haven't tested anything beyond that
because we know the GATK falls apart with higher levels of parallelism
* Make VECTOR_LOGLESS_CACHING the default implementation for PairHMM.
* Changed version number in pom.xml under public/VectorPairHMM
* VectorPairHMM can now be compiled using gcc 4.8.x
* Modified define-* to get rid of gcc warnings for extra tokens after #undefs
* Added a Linux kernel version check for AVX - gcc's __builtin_cpu_supports function does not check whether the kernel supports AVX or not.
* Updated PairHMM profiling code to update and print numbers only in single-thread mode
* Edited README.md, pom.xml and Makefile for users to pass path to gcc 4.8.x if necessary
* Moved all cpuid inline assembly to single function Changed info message to clog from cinfo
* Modified version in pom.xml in VectorPairHMM from 3.1 to 3.2
* Deleted some unnecessary code
* Modified C++ sandbox to print per interval timing
Story:
https://www.pivotaltracker.com/story/show/68220438
Changes:
- PL-less input genotypes are now uncalled and so non-variant sites when combining GVCFs.
- HC GVCF/BP_RESOLUTION Mode now outputs non-variant sites in sites covered by deletions.
- Fixed existing tests
Test:
- HaplotypeCallerGVCFIntegrationTest
- ReferenceConfidenceModelUnitTest
- CombineGVCFsIntegrationTest
story:
https://www.pivotaltracker.com/story/show/69648104
description:
This read transformer will refactor cigar strings that contain N-D-N elements to one N element (with total length of the three refactored elements).
This is intended primarily for users of RNA-Seq data handling programs such as TopHat2.
Currently we consider that the internal N-D-N motif is illegal and we error out when we encounter it. By refactoring the cigar string of
those specific reads, users of TopHat and other tools can circumvent this problem without affecting the rest of their dataset.
edit: address review comments - change the tool's name and change the tool to be a readTransformer instead of read filter
CalculateGenotypePosteriors now only computes posterior probs for SNP sites with SNP priors
(other sites have flat priors applied)
CalibrateGenotypeLikelihoods had originally applied HOM_REF/HET/HOM_VAR frequencies in callset as priors before empirical quality analysis. Now has option (-noPriors) to not apply/apply flat priors. Also takes in new external probabilities files, such as those generated by CGP, from which the genotype posterior probability qualities will be read.
Integration test was changed to account for new SNP-only behavior and default behavior to not use missing priors.
(Also, new numRefIfMissing is 0, which should only matter in cases using few samples when you probably don't want to be doing that anyway!)
Description:
Transforms a delegation dependency from HC to UG genotyping engine into a reusage by inhertance where HC and UG engines inherit from a common superclass GenotyperEngine
that implements the common parts. A side-effect some of the code is now more clear and redundant code has been removed.
Changes have a few consequence for the end user. HC has now a few more user arguments, those that control the functionality that HC was borrowing directly from UGE.
Added -ploidy argument although it is contraint to be 2 for now.
Added -out_mode EMIT_ALL_SITES|EMIT_VARIANTS_ONLY ...
Added -allSitePLs flag.
Stories:
https://www.pivotaltracker.com/story/show/68017394
Changes:
- Moved (HC's) GenotyperEngine to HaplotypeCallerGenotyperEngine (HCGE). Then created a engine superclass class GenotypingEngine (GE) that contains common parts between HCGE and the UG counterpart 'UnifiedGenotypingEngine' (UGE). Simplified the code and applied the template pattern to accomodate for small diferences in behaviour between both caller
engines. (There is still room for improvement though).
- Moved inner classes and enums to top-level components for various reasons including making them shorter and simpler names to refer to them.
- Create a HomoSpiens class for Human specific constants; even if they are good default for most users we need to clearly identify the human assumption across the code if we want to make
GATK work with any species in general; i.e. any reference to HomoSapiens, except as a default value for a user argument, should smell.
- Fixed a bug deep in the genotyping calculation we were taking on fixed values for snp and indel heterozygisity to be the default for Human ignoring user arguments.
- GenotypingLikehooldCalculationCModel.Model to Gen.*Like.*Calc.*Model.Name; not a definitive solution though as names are used often in conditionals that perhaps should be member methods of the
GenLikeCalc classes.
- Renamed LikelihoodCalculationEngine to ReadLikelihoodCalculationEngine to distinguish them clearly from Genotype likelihood calculation engines.
- Changed copy by explicity argument listing to a clone/reflexion solution for casting between genotypers argument collection classes.
- Created GenotypeGivenAllelesUtils to collect methods needed nearly exclusively by the GGA mode.
Tests :
- StandardCallerArgumentCollectionUnitTest (check copy by cloning/reflexion).
- All existing integration and unit tests for modified classes.
Following reviewers comments the command line interface has been simplified.
All extra strict validations are performed by default (as before) and the
user has to indicate which one he/she does not want to use with --validationTypeToExclude.
Before he/she was able to indicate the only ones to apply with --validationType but that has been scrapped out.
Stories:
- https://www.pivotaltracker.com/story/show/68725164
Changes:
- Removed validateType argument.
- Improved documentation.
- Added some warnning log message on suspicious argument combinations.
Tests:
- ValidateVariantsIntegrationTest#*
-- This is needed so the ref model pipeline can cut down to sites-only files without losing these useful statistics.
-- Added new unit test to test this info field annotation.
-- GenotypeGVCF integration tests change because new annotations are present in the output
More concretelly Picard's strict VCF validation does not like that there is alternative alleles that are not participating in any genotype call across samples.
This is an issue with GVCF in the single-sample pipeline where this is certainly expected with <NON_REF> and other relative unlikely alleles.
To solve this issue we allow the user to exclude some of the strict validations using a new argument --validationTypeToExclude. In order to avoid the validation
issue with GVCF the user needs to add the following to the command line: '--validationTypeToExclude ALLELES'
Story:
https://www.pivotaltracker.com/story/show/68725164
Changes:
- Added validateTypeToExclude argument to ValidateVariants walker.
- Implemented the selective exclusion of validation types.
- Added new info and improved existing documentation of the ValidateVariants walker.
Tests:
- ValidateVariantsIntegrationTest#testUnusedAlleleError
- ValidateVariantsIntegrationTest#testUnusedAlleleFix
In some cases, the program records were being removed from the BAM headers by the GATK engine
before we applied the check for reduced reads (so we did not fail appropriately). Pushed up the
check to happen before the PG tags are modified and added a unit test to ensure it stays that way.
It turns out that some UG tests still used reduced bams so I switched to use different ones.
Based on reviewer feedback, made it more generic so that it's easy to add new unsupported tools.
Previously it required you to create a single sample VCF and then to pass that in to the tool, but
Geraldine convinced me that this was a pain for users (because they usually have multi-sample VCFs).
Instead now you can pass in a multi-sample VCF and specify which sample's genotypes should be used
for the IUPAC encoding. Therefore the argument changed from '--useIUPAC' to '--use_IUPAC_sample NA12878'.
Stories:
https://www.pivotaltracker.com/story/show/66263868
Bug:
The problem was due to the way we were calculating the fix penalty of a large deletion or insertion. In this case we calculate the alignment likelihood of the portion
or read or haplotype deletion as the penalty of that deletion/insertion without going through the full pair-hmm process. For large events this resulted in a 0 in
in linear scale computations that ins transformed into an infinity in log scale.
Changes:
- Change to use log10 scale for calculate those penalties.
- Minor addition of .gitignore to hide ./public/external-example/target which is generated by the building process.
- SamPairUtils migrated in Picard r1737
- Revert IndelRealigner changes made in commit 4f4b85
-- Those changes were based on Picard revision 1722 to net/sf/picard/sam/SamPairUtil.java
-- Picard revision 1723 reverts these changes, so we also revert to match
Story:
- https://www.pivotaltracker.com/story/show/67601310
Change:
- Unless recover-danging-heads is active, the threading starting location policy is the original one. i.e. just at already existing unique kmer vertices.
Tests:
- HaplotypeCallerIntegrationTest#testMissingKeyAlternativeHaplotypesBugFix
1. Enable on-the-fly indexing for vcf.gz.
2. Handle on-the-fly indexing where file to be indexed is not a regular file, thus index should not be created.
3. Add method setProgressLogger to all SAMFileWriter implementations.
4. Revved picard to 1.109.1722
5. IndelRealigner md5s change because the MC tag is added to records now.
Fixed up and signed off by ebanks.
Currently the best haplotypes are those that accumulate the largest ABSOLUTE edge *multiplicity* sum across their path in the assembly graph.
The edge *mulitplicity* is equal to the number of reads that expand through that edge, i.e. have a kmer that uniquely map to some vertex up-stream from the edge and the following base calls extend across that edge to vertices downstream from it.
Despite that it is obvious that higher multiplicties correlated with haplotype probability this criterion fails short in some regards of which the most relevant is:
As it is evaluated in condensed seq-graph (as supposed to uncompressed read-threading-graphs) it is bias to haplotypes that have more short-sequence vetices
( -> ATGC -> CA -> has worse score than -> A -> T -> G -> C -> C -> A ->). This is partly result of how we modify the edge multiplicities when we merge vertices from a linear chain.
This pull-request addresses the problem by changing to a new scoring schema based in likelihood estimates:
Each haplotype's likelihood can be calculated as the multiplication of the likelihood of "taking" its edges in the assembly graph. The likelihood of "taking" an edge in the assembly
graph is calculated as its multiplicity divide by the sum of multiplicity of edges that share the same source vertex.
This pull-request addresses the following stories:
https://www.pivotaltracker.com/story/show/66691418https://www.pivotaltracker.com/story/show/64319760
Change Summary:
1. Change to the new scoring schema.
2. Added a graph DOT printing code to KBestHaplotypeFinder in order to diagnose scoring.
3. Graph transformation have been modified in order to generate no 0-multiplicity edges. (Nevertheless the schema above should work with 0 edges assuming that they are in fact 0.5)
Enable it with the new --useIUPAC argument.
Added both unit and integration tests for the new functionality - and fixed up the
exising tests once I was in there.
-- All the provided alleles are added to the assembly graph as potential haplotypes but they aren't forcibly genotyped like in GGA mode.
-- Added integration test for this mode
-These tests are really integration tests for Queue rather than generalized
pipeline tests, so it makes sense to call them QueueTests.
-Rename test classes and maven build targets, and update shell scripts
to reflect new naming.
C++ code has PAPI calls for reading hardware counters
Followed Khalid's suggestion for packing libVectorLoglessCaching into
the jar file with Maven
Native library part of git repo
1. Renamed directory structure from public/c++/VectorPairHMM to
public/VectorPairHMM/src/main/c++ as per Khalid's suggestion
2. Use java.home in public/VectorPairHMM/pom.xml to pass environment
variable JRE_HOME to the make process. This is needed because the
Makefile needs to compile JNI code with the flag -I<JRE_HOME>/../include (among
others). Assuming that the Maven build process uses a JDK (and not just
a JRE), the variable java.home points to the JRE inside maven.
3. Dropped all pretense at cross-platform compatibility. Removed Mac
profile from pom.xml for VectorPairHMM
Moved JNI_README
1. Added the catch UnsatisfiedLinkError exception in
PairHMMLikelihoodCalculationEngine.java to fall back to LOGLESS_CACHING
in case the native library could not be loaded. Made
VECTOR_LOGLESS_CACHING as the default implementation.
2. Updated the README with Mauricio's comments
3. baseline.cc is used within the library - if the machine supports
neither AVX nor SSE4.1, the native library falls back to un-vectorized
C++ in baseline.cc.
4. pairhmm-1-base.cc: This is not part of the library, but is being
heavily used for debugging/profiling. Can I request that we keep it
there for now? In the next release, we can delete it from the
repository.
5. I agree with Mauricio about the ifdefs. I am sure you already know,
but just to reassure you the debug code is not compiled into the library
(because of the ifdefs) and will not affect performance.
1. Changed logger.info to logger.warn in PairHMMLikelihoodCalculationEngine.java
2. Committing the right set of files after rebase
Added public license text to all C++ files
Added license to Makefile
Add package info to Sandbox.java
Conflicts:
protected/gatk-protected/src/main/java/org/broadinstitute/sting/gatk/walkers/haplotypecaller/HaplotypeCaller.java
protected/gatk-protected/src/main/java/org/broadinstitute/sting/gatk/walkers/haplotypecaller/PairHMMLikelihoodCalculationEngine.java
protected/gatk-protected/src/main/java/org/broadinstitute/sting/utils/pairhmm/DebugJNILoglessPairHMM.java
protected/gatk-protected/src/main/java/org/broadinstitute/sting/utils/pairhmm/JNILoglessPairHMM.java
protected/gatk-protected/src/main/java/org/broadinstitute/sting/utils/pairhmm/VectorLoglessPairHMM.java
public/VectorPairHMM/src/main/c++/.gitignore
public/VectorPairHMM/src/main/c++/LoadTimeInitializer.cc
public/VectorPairHMM/src/main/c++/LoadTimeInitializer.h
public/VectorPairHMM/src/main/c++/Makefile
public/VectorPairHMM/src/main/c++/Sandbox.cc
public/VectorPairHMM/src/main/c++/Sandbox.h
public/VectorPairHMM/src/main/c++/Sandbox.java
public/VectorPairHMM/src/main/c++/Sandbox_JNIHaplotypeDataHolderClass.h
public/VectorPairHMM/src/main/c++/Sandbox_JNIReadDataHolderClass.h
public/VectorPairHMM/src/main/c++/baseline.cc
public/VectorPairHMM/src/main/c++/define-double.h
public/VectorPairHMM/src/main/c++/define-float.h
public/VectorPairHMM/src/main/c++/define-sse-double.h
public/VectorPairHMM/src/main/c++/define-sse-float.h
public/VectorPairHMM/src/main/c++/headers.h
public/VectorPairHMM/src/main/c++/jnidebug.h
public/VectorPairHMM/src/main/c++/org_broadinstitute_sting_utils_pairhmm_DebugJNILoglessPairHMM.cc
public/VectorPairHMM/src/main/c++/org_broadinstitute_sting_utils_pairhmm_DebugJNILoglessPairHMM.h
public/VectorPairHMM/src/main/c++/org_broadinstitute_sting_utils_pairhmm_VectorLoglessPairHMM.cc
public/VectorPairHMM/src/main/c++/org_broadinstitute_sting_utils_pairhmm_VectorLoglessPairHMM.h
public/VectorPairHMM/src/main/c++/pairhmm-template-kernel.cc
public/VectorPairHMM/src/main/c++/pairhmm-template-main.cc
public/VectorPairHMM/src/main/c++/run.sh
public/VectorPairHMM/src/main/c++/shift_template.c
public/VectorPairHMM/src/main/c++/utils.cc
public/VectorPairHMM/src/main/c++/utils.h
public/VectorPairHMM/src/main/c++/vector_function_prototypes.h
-- throws UserException; added tests in PosteriorLikelihoodsUtilsUnitTests
Add error handling to CalculateGenotypePosteriors for cases where MLEAC>AN; add tests in PosteriorLikelihoodsUtilsUnitTests
Add unit tests to confirm that CalculateGenotypePosteriors has the ability to switch genotypes for four cases
C++ code has PAPI calls for reading hardware counters
Followed Khalid's suggestion for packing libVectorLoglessCaching into
the jar file with Maven
Native library part of git repo
1. Renamed directory structure from public/c++/VectorPairHMM to
public/VectorPairHMM/src/main/c++ as per Khalid's suggestion
2. Use java.home in public/VectorPairHMM/pom.xml to pass environment
variable JRE_HOME to the make process. This is needed because the
Makefile needs to compile JNI code with the flag -I<JRE_HOME>/../include (among
others). Assuming that the Maven build process uses a JDK (and not just
a JRE), the variable java.home points to the JRE inside maven.
3. Dropped all pretense at cross-platform compatibility. Removed Mac
profile from pom.xml for VectorPairHMM
Moved JNI_README
1. Added the catch UnsatisfiedLinkError exception in
PairHMMLikelihoodCalculationEngine.java to fall back to LOGLESS_CACHING
in case the native library could not be loaded. Made
VECTOR_LOGLESS_CACHING as the default implementation.
2. Updated the README with Mauricio's comments
3. baseline.cc is used within the library - if the machine supports
neither AVX nor SSE4.1, the native library falls back to un-vectorized
C++ in baseline.cc.
4. pairhmm-1-base.cc: This is not part of the library, but is being
heavily used for debugging/profiling. Can I request that we keep it
there for now? In the next release, we can delete it from the
repository.
5. I agree with Mauricio about the ifdefs. I am sure you already know,
but just to reassure you the debug code is not compiled into the library
(because of the ifdefs) and will not affect performance.
1. Changed logger.info to logger.warn in PairHMMLikelihoodCalculationEngine.java
2. Committing the right set of files after rebase
Added public license text to all C++ files
Added license to Makefile
Add package info to Sandbox.java
Changes:
1. Addressed review comments on new K-best haplotype assembly graph finder.
2. Generalize KBestHaplotypeFinder to deal with multiple source and sink vertices.
3. Updated test to use KBestHaplotypeFinder instead of KBestPaths
4. Retired KBestPaths to the archive.
5. Small improvements to the code and documentation.
Story:
https://www.pivotaltracker.com/story/show/66238286
Changes:
1. Created a new k-best haplotype search implementation in class KBestHaplotypeFinder.
2. Changed HC code to use the new implementation.
This seems to fix the original problem without causing significant changes in outputs using some empirical data test cases
3. Moved haplotype's cigar calculation code from Path to CigarUtils; need that in order to gain independence from Path in some parts of the code.
In any case that seems like a more natural location for that functionality.
The purpose of this is to be able to call SNPs that fall at the beginning of a capture region (or exon).
Before, the read threading code would only start threading from the first kmer that matched the reference. But
that means that, in the case of a SNP at the beginning of an exome, it wouldn't start threading the read until
after the SNP position - so we'd lose the SNP.
For now, this is still very experimental. It works well for RNAseq data, but does introduce FPs in normal exomes.
I know why this is and how to fix it, but it requires a much larger fix to the HC: the HC needs to pass all reads
and bases to the annotation engine (like UG does) instead of just the high quality ones. So for now, the head
merging is disabled by default.
As per reviewer comments, I moved the head and tail merging code out into their own class.
We use a "manager" to keep track of observed splits and previous reads. This can be extended/modified in the
future to try to salvage those overhangs instead of hard-clipping them and/or try other possible strategies.
Added unit tests and more integration tests.
The GATK now fails with a user error if you try to run with a reduced bam.
(I added a unit test for that; everything else here is just the removal of all traces of RR)
PairHMMLikelihoodCalculationEngine.java to fall back to LOGLESS_CACHING
in case the native library could not be loaded. Made
VECTOR_LOGLESS_CACHING as the default implementation.
2. Updated the README with Mauricio's comments
3. baseline.cc is used within the library - if the machine supports
neither AVX nor SSE4.1, the native library falls back to un-vectorized
C++ in baseline.cc.
4. pairhmm-1-base.cc: This is not part of the library, but is being
heavily used for debugging/profiling. Can I request that we keep it
there for now? In the next release, we can delete it from the
repository.
5. I agree with Mauricio about the ifdefs. I am sure you already know,
but just to reassure you the debug code is not compiled into the library
(because of the ifdefs) and will not affect performance.
Re-added import java.io.File for BamGatherFunction.
Other cleanup to resolve scala syntax warnings from intellij.
Moved Example UG script to from protected to public.
This commit consists of 2 main changes:
1. When the strand table gets too large, we normalize it down to values that are more reasonable.
2. We don't include a particular sample's contribution unless the total ref and alt counts are at least 2 each;
this is a heuristic method for dealing only with hets.
MD5s change as expected.
Hopefully we'll have a more robust implementation for GATK 3.1.
The slicePrefix method functionality was broken.
Story:
https://www.pivotaltracker.com/story/show/64595624
Changes:
1. Fixed the bug.
2. Added unit test to check on the method functionality.
3. Added a integration test to verify the bug has been fixed in a empirical data reprudible case.
Story:
https://www.pivotaltracker.com/s/projects/1007536
Changes:
1. HC's GenotypingEngine now invokes reverseAlleleTrimming on GVCF variant output lines.
2. GenotypeGVCFs also reverse trim after regenotyping as some alt. alleles are dropped (observed in real-data).
The writer was never resetting the pointer to the end of the last non-ref VariantContext that it saw.
This was fine except when it jumped to a new contig - and a lower position on that contig - where it
thought that it was still part of that previous non-ref VariantContext so wouldn't emit a reference
block. Therefore, ref blocks were missing from the beginnings of all chromosomes (except chr1).
Added unit test to cover this case.
Bug uncovered by some untrimmed alleles in the single sample pipeline output.
Notice however does not fix the untrimmed alleles in general.
Story:
https://www.pivotaltracker.com/story/show/65481104
Changes:
1. Fixed the bug itself.
2. Fixed non-working tests (sliently skipped due to exception in dataProvider).
Note that this tool is still a work in progress and very experimental, so isn't 100% stable. Most of
the features are untested (both by people and by unit/integration tests) because Chris Hartl implemented
it right before he left, and we're going to need to add tests at some point soon. I added a first
integration test in this commit, but it's just a start.
The fixes include:
1. Stop having the genotyping code strip out AD values. It doesn't make sense that it should do this so
I don't know why it was doing that at all.
Updated GenotypeGVCFs so that it doesn't need to manually recover them anymore.
This also helps CalculateGenotypePosteriors which was losing the AD values.
Updated code in LeftAlignAndTrimVariants to strip out PLs and AD, since it wasn't doing that before.
Updated the integration test for that walker to include such data.
2. Chris was calling Math.pow directly on the normalized posteriors which isn't safe.
Instead, the normalization routine itself can revert back to log scale in a safe manner so let's use it.
Also, renamed the variable to posteriorProbabilities (and not likelihoods).
3. Have CGP update the AC/AF/AN counts after fixing GTs.
After extensive detective work, Joel determined that these tests were failing
due to changes in the implementation of Math.pow() in newer versions of
Java 1.7.
All GSA members should ensure that they're using a JDK that is at least
as current as the one in the Java-1.7 dotkit on the Broad servers
(build 1.7.0_51-b13).
1. updated QualByDepth not to use AD-restricted depth if it is zero.
Added unit test this change.
2. Fixed small bug in CombineGVCFs where spanning deletions were not being treated consistently throughout.
Added test for this situation.
3. Make sure GenotypeGVCFs puts in the required headers.
Updated test files to make sure this is covered.
4. Have GenotypeGVCFs propagate up the MLEAC/AF (which were getting clobbered out).
Tests updated to account for this.
when the AD annotation is present for a given genotype then we only use its depth for QD if the variant depth > 1.
Added new unit tests for QualByDepth.
Creating new VariantContexts each time we broke up a block was very expensive because we break up
blocks so often. Also, calling into GATKVariantContextUtils.simpleMerge was really hurting performance.
MD5 changes because we no longer propogate any INFO fields (except for END) for reference blocks; the tests
have the now unused BLOCK_SIZE field that now get dropped.
Story:
https://www.pivotaltracker.com/story/show/65388246
Additional changes and notes:
1. The fix consist in forcing the output of all PLs by setting the standard flag for that '-allSitePLs'.
2. BP_RESOLUTION was handled differently to GVCF in some aspect that should be common. That has been fixed.
The library is compiled using makefile and copied into the directory:
build/java/classes/org/broadinstitute/sting/utils/pairhmm/
2. Bundled the library into StingUtils.jar. Unpacked and loaded at
runtime without the need to set java.library.path
Caveats:
Platform independence has probably been thrown out of the window.
Assumptions:
a. make command exists at /usr/bin/make
b. rsync command exists at /usr/bin/rsync
c. icc is in the PATH of the user
1. AD values now propogate up (they weren't before).
2. MIN_DP gets transferred over to DP and removed.
3. SB gets removed after FS is calculated.
Also, added a bunch of new integration tests for GenotypeGVCFs.
AC,AF,AN,FS,QD - they'll all be recomputed later.
BLOCK_SIZE and MIN_GQ were not necessary.
I also made the StrandBiasBySample annotation forced on when in gVCF mode.
It turns out that its output wasn't compatible with BCF so I patched it (and the variant jar too).
This tool will take any number of gVCFs and create a merged gVCF (as opposed to
GenotypeGVCFs which produces a standard VCF).
Added unit/integration tests and fixed up GATK docs.
New properties to disable regenerating example resources artifact when each parallel test runs under packagetest.
Moved collection of packagetest parameters from shell scripts into maven profiles.
Fixed necessity of test-utils jar by removing incorrect dependenciesToScan element during packagetests.
When building picard libraries, run clean first.
Fixed tools jar dependency in picard pom.
Integration tests properly use the ant-bridge.sh test.debug.port variable, like unit tests.
Story:
https://www.pivotaltracker.com/story/show/65048706https://www.pivotaltracker.com/story/show/65116908
Changes:
ActiveRegionTrimmer in now an argument collection and it returns not only the trimmed down active region but also the non-variant containing flanking regions
HaplotypeCaller code has been simplified significantly pushing some functionality two other classes like ActiveRegion and AssemblyResultSet.
Fixed a problem with the way the trimming was done causing some gVCF non-variant records no have conservative 0,0,0 PLs
1. Throw a user error when the input data for a given genotype does not contain PLs.
2. Add VCF header line for --dbsnp input
3. Need to check that the UG result is not null
4. Don't error out at positions with no gVCFs (which is possible when using a dbSNP rod)
Joel is working on these failures in a separate branch. Since
maven (currently! we're working on this..) won't run the whole
test suite to completion if there's a failure early on, we need
to temporarily disable these tests in order to allow group members
to run tests on their branches again.
Here are the git moved directories in case other files need to be moved during a merge:
git-mv private/java/src/ private/gatk-private/src/main/java/
git-mv private/R/scripts/ private/gatk-private/src/main/resources/
git-mv private/java/test/ private/gatk-private/src/test/java/
git-mv private/testdata/ private/gatk-private/src/test/resources/
git-mv private/scala/qscript/ private/queue-private/src/main/qscripts/
git-mv private/scala/src/ private/queue-private/src/main/scala/
git-mv protected/java/src/ protected/gatk-protected/src/main/java/
git-mv protected/java/test/ protected/gatk-protected/src/test/java/
git-mv public/java/src/ public/gatk-framework/src/main/java/
git-mv public/java/test/ public/gatk-framework/src/test/java/
git-mv public/testdata/ public/gatk-framework/src/test/resources/
git-mv public/scala/qscript/ public/queue-framework/src/main/qscripts/
git-mv public/scala/src/ public/queue-framework/src/main/scala/
git-mv public/scala/test/ public/queue-framework/src/test/scala/
Changes:
-------
<NON_REF> likelihood in variant sites is calculated as the maximum possible likelihood for an unseen alternative allele: for reach read is calculated as the second best likelihood amongst the reported alleles.
When –ERC gVCF, stand_conf_emit and stand_conf_call are forcefully set to 0. Also dontGenotype is set to false for consistency sake.
Integration test MD5 have been changed accordingly.
Additional fix:
--------------
Specially after adding the <NON_REF> allele, but also happened without that, QUAL values tend to go to 0 (very large integer number in log 10) due to underflow when combining GLs (GenotypingEngine.combineGLs). To fix that combineGLs has been substituted by combineGLsPrecise that uses the log-sum-exp trick.
In just a few cases this change results in genotype changes in integration tests but after double-checking using unit-test and difference between combineGLs and combineGLsPrecise in the affected integration test, the previous GT calls were either border-line cases and or due to the underflow.
2. Split into DebugJNILoglessPairHMM and VectorLoglessPairHMM with base
class JNILoglessPairHMM. DebugJNILoglessPairHMM can, in principle,
invoke any other child class of JNILoglessPairHMM.
3. Added more profiling code for Java parts of LoglessPairHMM
Problem:
matchToMatch transition calculation was wrong resulting in transition probabilites coming out of the Match state that added more than 1.
Reports:
https://www.pivotaltracker.com/s/projects/793457/stories/62471780https://www.pivotaltracker.com/s/projects/793457/stories/61082450
Changes:
The transition matrix update code has been moved to a common place in PairHMMModel to dry out its multiple copies.
MatchToMatch transtion calculation has been fixed and implemented in PairHMMModel.
Affected integration test md5 have been updated, there were no differences in GT fields and example differences always implied
small changes in likelihoods that is what is expected.
2. Wrapped _mm_empty() with ifdef SIMD_TYPE_SSE
3. OpenMP disabled
4. Added code for initializing PairHMM's data inside initializePairHMM -
not used yet
SSE compilation warning.
2. Added code to dynamically select between AVX, SSE4.2 and normal C++ (in
that order)
3. Created multiple files to compile with different compilation flags:
avx_function_prototypes.cc is compiled with -xAVX while
sse_function_instantiations.cc is compiled with -xSSE4.2 flag.
4. Added jniClose() and support in Java (HaplotypeCaller,
PairHMMLikelihoodCalculationEngine) to call this function at the end of
the program.
5. Removed debug code, kept assertions and profiling in C++
6. Disabled OpenMP for now.
In unifying the arguments it was clear that the values were inconsistent throughout the code, so now there's a
single value that is intended to be more liberal in what it allows in (in an attempt to increase sensitivity).
Very little code actually changes here, but just about every md5 in the HC integration tests are different (as
expected). Added another integration test for the new argument.
To be used by David R to test his per-branch QC framework: does this commit make the HC look better against the KB?
1. Moved computeLikelihoods from PairHMM to native implementation
2. Disabled debug - debug code still left (hopefully, not part of
bytecode)
3. Added directory PairHMM_JNI in the root which holds the C++
library that contains the PairHMM AVX implementation. See
PairHMM_JNI/JNI_README first
It didn't completely work before (it was hard-coded for a particular long-lost data set) but it should work now.
Since I thought that it might prove useful to others, I moved it to protected and added integration tests.
GERALDINE: NEW TOOL ALERT!
The code comments very clearly state that INFO fields shouldn't be propagated into the output,
but someone must have accidentally changed it afterwards. This is just a simple one-line fix
to make sure the code adhered to the comments.
Delivers #63333488.
-Added docs for ERC mode in HC
-Move RecalibrationPerformance walker since to private since it is experimental and unsupported
-Updated VR docs and restored percentBad/numBad (but @Hidden) to enable deprecation alert if users try to use them
-Improved error msg for conflict between per-interval aggregation and -nt
-Minor clean up in exception docs
-Added Toy Walkers category for devs and dev supercat (to build out docs for developers)
-Added more detailed info to GenotypeConcordance doc based on Chris forum post
-Added system to include min/max argument values in gatkdocs (build gatkdocs with 'ant gatkdocs' to test it, see engine and DoC args for in situ examples)
-Added tentative min/max argument annotations to DepthOfCoverage and CommandLineGATK arguments (and improved docs while at it)
-Added gotoDev annotation to GATKDocumentedFeature to track who is the go-to person in GSA for questions & issues about specific walkers/tools (now discreetly indicated in each gatkdoc)
It is true that indels of length > 1 have higher QUALS than those of length = 1. But for the HC those
QUALS are not that much higher, and it doesn't continue scaling up as the indels get larger. So we no
longer normalize by indel length (which massively over-penalizes larger events and effectively drops their
QD to 0).
For the UG the previous normalization also wasn't perfect. Now we divide the indel length by a factor
of 3 to make sure that QD is consistent over the range of indel lengths.
Integration tests change because QD is different for indels.
Also, got permission from Valentin to archive a failing test that no longer applies.
Thanks to Kurt on the GATK forum for pointing this all out.
To do this I have added a RodBindingCollection which can represent either a VCF or a
file of VCFs. Note that e.g. SelectVariants allows a list of RodBindingCollections so
that one can intermix VCFs and VCF lists.
For VariantContext tags with a list, by default the tags for the -V argument are applied
unless overridden by the individual line. In other words, any given line can have either
one token (the file path) or two tokens (the new tags and the file path). For example:
foo.vcf
VCF,name=bar bar.vcf
Note that a VCF list file name must end with '.list'.
Added this functionality to CombineVariants, CombineReferenceCalculationVariants, and VariantRecalibrator.
-- 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)
This is a tool that we use internally validate the ReduceReads development. I think it should be
private. There is no need to improve docs.
[delivers #54703398]
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.
Motivation:
The API was different between the regular PairHMM and the FPGA-implementation
via CnyPairHMM. As a result, the LikelihoodCalculationEngine had
to use account for this. The goal is to change the API to be the same
for all implementations, and make it easier to access.
PairHMM
PairHMM now accepts a list of reads and a map of alleles/haplotpes and returns a PerReadAlleleLikelihoodMap.
Added a new primary method that loops the reads and haplotypes, extracts qualities,
and passes them to the computeReadLikelihoodGivenHaplotypeLog10 method.
Did not alter that method, or its subcompute method, at all.
PairHMM also now handles its own (re)initialization, so users don't have to worry about that.
CnyPairHMM
Added that same new primary access method to this FPGA class.
Method overrides the default implementation in PairHMM. Walks through a list of reads.
Individual-read quals and the full haplotype list are fed to batchAdd(), as before.
However, instead of waiting for every read to get added, and then walking through the reads
again to extract results, we just get the haplotype-results array for each read as soon as it
is generated, and pack it into a perReadAlleleLikelihoodMap for return.
The main access method is now the same no matter whether the FPGA CnyPairHMM is used or not.
LikelihoodCalculationEngine
The functionality to loop through the reads and haplotypes and get individual log10-likelihoods
was moved to the PairHMM, and so removed from here. However, this class does need to retain
the ability to pre-process the reads, and post-process the resulting likelihoods map.
Those features were separated from running the HMM and refactored into their own methods
Commented out the (unused) system for finding best N haplotypes for genotyping.
PairHMMIndelErrorModel
Similar changes were made as to the LCE. However, in this case the haplotypes are modified
based on each individual read, so the read-list we feed into the HMM only has one read.
-- 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.
Making the usage more clear since the parameter is being used over and over to define baited
regions. Updated the headers accordingly and made it more readable.
Quick fix the missing column header in the QualifyMissingIntervals
report.
Adding a QScript for the tool as well as a few minor updates to the
GATKReportGatherer.
* add a length of the overlaping interval metric as per CSER request
* standardized the distance metrics to be positive when fully overlapping and the longest off-target tail (as a negative number) when not overlapping
* add gatkdocs to the tool (finally!)
* 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
most people don't care about excessive coverage (unless you're very
particular about your analysis). Therefore the best possible default
value for this is Integer.maxValue so it doesn't get in the way.
Itemized Changes:
* change maximumCoverage threshold to Integer.maxValue
[delivers #57353620]
-- 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.
Returned Logless Caching implementation to the default in all cases. Changing to the array version will await performance benchmarking
Refactored many pieces of functionality in ArrayLoglessPairHMM into their own methods.
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.
- Make -rod required
- Document that contaminationFile is currently not functional with HC
- Document liftover process more clearly
- Document VariantEval combinations of ST and VE that are incompatible
- Added a caveat about using MVLR from HC and UG.
- Added caveat about not using -mte with -nt
- Clarified masking options
- Fixed docs based on Erics comments
-- 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.
Now only filtered reads are unstranded. All consensus reads have strand, so that we
emit 2 consensus reads in general now: one for each strand.
This involved some refactoring of the sliding window which cleaned it up a lot.
Also included is a bug fix:
insertions downstream of a variant region weren't triggering a stop to the compression.
So, compromise solution is to go back to having biallelic PLs but emit a new FORMAT field, called APL, which has the 10 values, but all other statistics and regular PLs are computed as before.
Note that integration test had to be disabled, as the BCF2 codec apparently doesn't support writing into genotype fields other than PL,DP,AD,GQ,FT and GT.
Problem
-------
Qualify Missing Intervals only accepted GATK formatted interval files for it's coding sequence and bait parameters.
Solution
-------
There is no reason for such limitation, I erased all the code that did the parsing and used IntervalUtils to parse it (therefore, now it handles any type of interval file that the GATK can handle).
ps: Also added an average depth column to the output
- Added integration test to show that providing a contamination value and providing same value via a file results in the same VCF
- overrode default contamination value in test
1. Some minor refactorings and claenup (e.g. removing unused imports) throughout.
2. Updates to the KB assessment functionality:
a. Exclude duplicate reads when checking to see whether there's enough coverage to make a call.
b. Lower the threshold on FS for FPs that would easily be filtered since it's only single sample calling.
3. Make the HC consistent in how it treats the pruning factor. As part of this I removed and archived
the DeBruijn assembler.
4. Improvements to the likelihoods for the HC
a. We now include a "tristate" correction in the PairHMM (just like we do with UG). Basically, we need
to divide e by 3 because the observed base could have come from any of the non-observed alleles.
b. We now correct overlapping read pairs. Note that the fragments are not merged (which we know is
dangerous). Rather, the overlapping bases are just down-weighted so that their quals are not more
than Q20 (or more specifically, half of the phred-scaled PCR error rate); mismatching bases are
turned into Q0s for now.
c. We no longer run contamination removal by default in the UG or HC. The exome tends to have real
sites with off kilter allele balances and we occasionally lose them to contamination removal.
5. Improved the dangling tail merging implementation.
Eric Banks
Valentin Ruano-Rubio
Phillip Dexheimer
Laura Gauthier
Ryan Poplin
Tim Fennell
David Roazen
Geraldine Van der Auwera
Khalid Shakir
EvolvedMicrobe
Nigel Delaney
Karthik Gururaj
MauricioCarneiro
Ami Levy-Moonshine
Kristian Cibulskis
Joel Thibault
Alec Wysoker
Bertrand Haas
Menachem Fromer
Chris Hartl
bradtaylor
Chris Hartl
Michael McCowan
Louis Bergelson
Mark DePristo
Yossi Farjoun
Joseph Rose
sathibault
Guillermo del Angel
Scott Thibault