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
amilev
MauricioCarneiro
Eric Banks
Valentin Ruano-Rubio
David Roazen
Geraldine Van der Auwera
Joel Thibault
Ryan Poplin
Chris Hartl
bradtaylor
Chris Hartl
Michael McCowan