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.
Laura Gauthier
MauricioCarneiro
Karthik Gururaj
Valentin Ruano-Rubio
Ryan Poplin
Ami Levy-Moonshine
Kristian Cibulskis
Khalid Shakir
Eric Banks
Joel Thibault
David Roazen
Alec Wysoker
Bertrand Haas