-- 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
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).
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
-- Fixes monster bug in the way that traversal engines interacted with the NanoScheduler via the output tracker.
-- ThreadLocalOutputTracker is now a ThreadBasedOutputTracker that associates via a map from a master thread -> the storage map. Lookups occur by walking through threads in the same thread group, not just the thread itself (TBD -- should have a map from ThreadGroup instead)
-- Removed unnecessary debug statement in GenomeLocParser
-- nt and nct officially work together now
TestNG skips tests when an exception occurs in a data provider,
which is what was happening here.
This was due to an AWFUL AWFUL use of a non-final static for
ReadShard.MAX_READS. This is fine if you assume only one instance
of SAMDataSource, but with multiple tests creating multiple SAMDataSources,
and each one overwriting ReadShard.MAX_READS, you have a recipe for
problems. As a result of this the test ran fine individually, but not as
part of the unit test suite.
Quick fix for now to get the tests running -- this "mutable static"
interface should really be refactored away though, when I have time.
It's now possible to run with experimental downsampling enabled
using the --enable_experimental_downsampling engine argument.
This is scheduled to become the GATK-wide default next week after
diff engine output for failing tests has been examined.
Notify all downsamplers in our pool of the current global genomic position every
DOWNSAMPLER_POSITIONAL_UPDATE_INTERVAL position changes, not every single
positional change after that threshold is first reached.
-Only used when experimental downsampling is enabled
-Persists read iterators across shards, creating a new set only when we've exhausted
the current BAM file region(s). This prevents the engine from revisiting regions discarded
by the downsamplers / filters, as could happen in the old implementation.
-SAMDataSource no longer tracks low-level file positions in experimental mode. Can strip
out all related code when the engine fork is collapsed.
-Defensive implementation that assumes BAM file regions coming out of the BAM Schedule
can overlap; should be able to improve performance if we can prove they cannot possibly
overlap.
-Tests a bit on the extreme side (~8 minute runtime) for now; will scale these back
once confidence in the code is gained
-- See https://jira.broadinstitute.org/browse/GSA-573
-- Uses InheritedThreadLocal storage so that children threads created by the NanoScheduler see the parent stubs in the main thread.
-- Added explicit integration test that checks that -nt 1, 2 and -nct 1, 2 give the same results for GLM BOTH with the UG over 1 MB.
Doesn't actually fix the problem, and adds an unnecessary delay in closing down NanoScheduler, so reverting.
This reverts commit 66b820bf94ae755a8a0c71ea16f4cae56fd3e852.
1) Better documentation on the meta data file for VariantsToBinaryPed with examples of each file type
2) MannWhitneyU can now take an argument on creation to turn off dithering. This pertains to JIRA-GSA-571 but does not fix it,
as it isn't hooked up to the command line. Next step is to add an argument to the command line where it's accessible to the
annotation classes (e.g. from either UG or the VariantAnnotator).
3) Added some dumb python scripts to deal with Plink files, and a script to convert plink binaries to VCF to help sanity check. Basically if you want to do an analysis on genotype data stored in plink binary format, your choices are:
1) Add a new module to Plink [difficulty rating: Impossible -- code obfuscation]
2) Steal plink parsing code from software (Plink/PlinkSeq/GCTA/Emacks/etc) that readds the files [difficulty rating: Oppressive -- code not modularized at all)
3) Write your own dumb stuff [difficutly rating: Annoying]
What's been added is the result of 3. It's a library so nobody else has to do this, so long as they're comfortable with python.
-- Renamed TraversalErrorManager to the more general MultiThreadedErrorTracker
-- ErrorTracker is now used throughout the NanoScheduler. In order to properly handle errors, the work previously done by main thread (submit jobs, block on reduce) is now handled in a separate thread. The main thread simply wakes up peroidically and checks whether the reduce result is available or if an error has occurred, and handles each appropriately.
-- EngineFeaturesIntegrationTest checks that -nt and -nct properly throw errors in Walkers
-- Added NanoSchedulerUnitTest for input errors
-- ThreadEfficiencyMonitoring is now disabled by default, and can be enabled with a GATK command line option. This is because the monitoring doesn't differentiate between threads that are supposed to do work, and those that are supposed to wait, and therefore gives misleading results.
-- Build.xml no longer copies the unittest results verbosely
-- Refactored error handling from HMS into utils.TraversalErrorManager, which is now used by HMS and will be usable by NanoScheduler
-- Generalized EngineFeaturesIntegrationTest to test map / reduce error throwing for nt 1, nt 2 and nct 2 (disabled)
-- Added unit tests for failing input iterator in NanoScheduler (fails)
-- Made ErrorThrowing NanoScheduable
Mark DePristo
David Roazen
Christopher Hartl
Eric Banks
Guillermo del Angel
Ryan Poplin