-- Right now the state of the AFCaclulationResult can be corrupt (ie, log10 likelihoods can be -Infinity). Forced me to disable reasonable contracts. Needs to be thought through
-- exactCallsLog should be optional
-- Update UG integration tests as the calculation of the normalized posteriors is done in a marginally different way so the output is rounded slightly differently.
-- UnifiedGenotyperEngine no longer keeps a thread local double[2] array for the normalized posteriors array. This is way heavy-weight compared to just making the array each time.
-- Added getNormalizedPosteriorOfAFGTZero and getNormalizedPosteriorOfAFzero to AFResult object. That's the place it should really live
-- Add tests for priors, uncovering bugs in the contracts of the tri-allelic priors w.r.t. the AC of the MAP. Added TODOs
-- AFResult now tracks the number of evaluations (turns through the model calculation) so we can now compute the scaling of exact model itself as a function of n samples
-- Added unittests for priors (flat and human)
-- Discovered nasty general ploidy bug (enabled with Guillermo_FIXME)
-- Added combinatorial unit tests for both Diploid and General (in diploid-case) for 2 and 3 alleles in all combinations of sample types (i.e., AA, AB, BB and equiv. for tri-allelic). More assert statements to ensure quality of the result.
-- Added docs (DOCUMENT YOUR CODE!) to AlleleFrequencyCalculationResult, with proper input error handling and contracts. Made mutation functions all protected
-- No longer need to call reset on your AlleleFrequencyCalculationResult -- it'd done for you in the calculation function. reset is a protected method now, so it's all cleaner and nicer this way
-- TODO still -- need to add edge-case tests for non-informative samples (0,0,0), for the impact of priors, and I need to add some way to test the result of the pNonRef
-- Added a true base class that only does truly common tasks (like manage call logging)
-- This base class provides the only public method (getLog10PNonRef) and calls into a protected compute function that's abstract
-- Split ExactAF into superclass ExactAF with common data structures and two subclasses: DiploidExact and GeneralPloidyExact
-- Added an abstract reduceScope function that manages the simplification of the input VariantContext in the case where there are too many alleles or other constraints require us to only attempt a smaller computation
-- All unit tests pass
-- This allows us to log all of the information about the exact model call (alleles, priors, PLs, result, and runtime) to a file for later debugging / optimization
1) GATKArgumentCollection has a command to turn off randomization if setting the seed isn't enough. Right now it's only hooked into RankSumTest.
2) RankSumTest now can be passed a boolean telling it whether to use a dithering or non-randomizing comparator. Unit tested.
3) VariantsToBinaryPed can now output in both individual-major and SNP-major mode. Integration test.
4) Updates to PlinkBed-handling python scripts and utilities.
5) Tool for calculating (LD-corrected) GRMs put under version control. This is analysis for T2D, but I don't want to lose it should something happen to my computer.
Sometimes the GATK engine creates a single monolithic FilePointer representing all regions
in all BAM files. In such cases, the monolithic FilePointer is the only FilePointer emitted
by the BAMScheduler, and it's safe to allow it to contain regions and intervals from multiple
contigs.
This fixes support for reading unindexed BAM files (since an unindexed BAM is one case
in which the engine creates a monolithic FilePointer).
Pipeline runs end-to-end using example metadata and has been tested only for cases where everything is ideal.
Next step is to bring this to the cloud, test all different scenario (multiple tumors, single ended, missing parameters etc).
Parallel next step is to add QC metrics.
Nasty, nasty bug -- if we were extremely unlucky with shard boundaries, we might
end up with a shard containing only unmapped mates of mapped reads. In this case,
ReadShard.getReadsSpan() would not behave correctly, since the shard as a whole would
be marked "mapped" (since it refers to mapped intervals) yet consist only of unmapped
mates of mapped reads located within those intervals.
1) ValidateVariants removed in favor of direct validation VariantContexts. Integration test added to test broken contexts.
2) Enabling indel and SV output. Still bi-allelic sites only. Integration tests added for these cases.
3) Found a bug where GQ recalculation (if a genotype has PLs but no GQ) would only happen for flipped encoding. Fixed. Integration test added.
Merge all FilePointers for each contig into a single, merged, optimized FilePointer
representing all regions to visit in all BAM files for a given contig.
This helps us in several ways:
-It allows us to create a single, persistent set of iterators for each contig,
finally and definitively eliminating all Shard/FilePointer boundary issues for
the new experimental ReadWalker downsampling
-We no longer need to track low-level file positions in the sharding system (which
was no longer possible anyway given the new experimental downsampling system)
-We no longer revisit BAM file chunks that we've visited in the past -- all BAM
file access is purely sequential
-We no longer need to constantly recreate our full chain of read iterators
There are also potential dangers:
-We hold more BAM index data in memory at once. Given that we merge and optimize
the index data during the merge, and only hold one contig's worth of data at a
time, this does not appear to be a major issue. TODO: confirm this!
-With a huge number of samples and intervals, the FilePointer merge operation
might become expensive. With the latest implementation, this does not
appear to be an issue even with a huge number of intervals (for one sample, at least),
but if it turns out to be a problem for > 1 sample there are things we can do.
Still TODO: unit tests for the new FilePointer.union() method
-- Calls NA12878 with and without the expt. downsampler on chr1
-- Creates combined vcf, annotating sites as overlapping omni SNPs and Mills indels
-- Creates simple combined.table that has chr, pos, set, and type to easily ID missed good sites with the new downsampler
Christopher Hartl
Scott Frazer
Kristian Cibulskis
Eric Banks
Guillermo del Angel
Mark DePristo
David Roazen
Mauricio Carneiro
Ryan Poplin