-- Example combinatorial unit tests, plus unit tests that create reads and bam files, pileups, variant context (from scratch and from a file), and genome locs
-- Moved previously inner class to MRUCachingSAMSequenceDictionary, and unit test to 100% coverage
-- Fully document all functions in GenomeLocParser
-- Unit tests for things like parsePosition (shocking it wasn't tested!)
-- Removed function to specifically create GenomeLocs for VariantContexts. The fact that you must incorporate END attributes in the context means that createGenomeLoc(Feature) works correctly
-- Depreciated (and moved functionality) of setStart, setStop, and incPos to GenomeLoc
-- Unit test coverage at like 80%, moving to 100% with next commit
-- The new version is roughly 2x faster than the previous version. The key here was to cleanup the workflow for validateGenomeLoc and remove the now unnecessary synchronization blocks from the CachingSequencingDictionary, since these are now thread local variables
-- #resolves https://jira.broadinstitute.org/browse/GSA-724
-- All functions tested. In the testing / review I discovered several bugs in the ActiveRegion routines that manipulate reads. New version should be correct
-- Enforce correct ordering of supporting states in constructor
-- Enforce read ordering when adding reads to an active region in add
-- Fix bug in HaplotypeCaller map with new updating read spans. Now get the full span before clipping down reads in map, so that variants are correctly placed w.r.t. the full reference sequence
-- Encapsulate isActive field with an accessor function
-- Make sure that all state lists are unmodifiable, and that the docs are clear about this
-- ActiveRegion equalsExceptReads is for testing only, so make it package protected
-- ActiveRegion.hardClipToRegion must resort reads as they can become out of order
-- Previous version of HC clipped reads but, due to clipping, these reads could no longer overlap the active region. The old version of HC kept these reads, while the enforced contracts on the ActiveRegion detected this was a problem and those reads are removed. Has a minor impact on PLs and RankSumTest values
-- Updating HaplotypeCaller MD5s to reflect changes to ActiveRegions read inclusion policy
Please check that your commit hook is properly pointing at ../../private/shell/pre-commit
Conflicts:
public/java/test/org/broadinstitute/variant/VariantBaseTest.java
-Moved some of the more specialized / complex VariantContext and VCF utility
methods back to the GATK.
-Due to this re-shuffling, was able to return things like the Pair class back
to the GATK as well.
a) Add option to stratify CalibrateGenotypeLikelihoods by repeat - will add integration test in next push.
b) Simulator to produce BAM files with given error profile - for now only given SNP/indel error rate can be given. A bad context can be specified and if such context is present then error rate is increased to given value.
c) Rewrote RepeatLength covariate to do the right thing - not fully working yet, work in progress.
d) Additional experimental covariates to log repeat unit and combined repeat unit+length. Needs code refactoring/testing
With LegacyLocusIteratorByState deleted, the legacy downsampling implementation
was already non-functional. This commit removes all remaining code in the
engine belonging to the legacy implementation.
-- Helped ID more bugs in the ActivityProfile, necessitating a new algorithm for popping off active regions. This new algorithm requires that at least maxRegionSize + prob. propagation distance states have been examined. This ensures that the incremental results are the same as you get reading in an entire profile and running getRegions on the full profile
-- TODO is to remove incremental search start algorithm, as this is no longer necessary, and nicely eliminates a state variable I was always uncomfortable with
-- GATKSAMRecords now cache the result of the getAdapterBoundary, allowing us to avoid repeating a lot of work in LIBS
-- Added unittests to cover adapter clipping
-- This new algorithm is essential to properly handle activity profiles that have many large active regions generated from lots of dense variant events. The new algorithm passes unit tests and passes visualize visual inspection of both running on 1000G and NA12878
-- Misc. commenting of the code
-- Updated ActiveRegionExtension to include a min active region size
-- Renamed ActiveRegionExtension to ActiveRegionTraversalParameters, as it carries more than just the traversal extension now
-- Previously we allowed band pass filter size to be specified along with the sigma. But now that sigma is controllable from walkers and from the command line, we instead compute the filter size given the kernel from the sigma, including all kernel points with p > 1e-5 in the kernel. This means that if you use a smaller kernel you get a small band size and therefore faster ART
-- Update, as discussed with Ryan, the sigma and band size to 17 bp for HC (default ART wide) and max band size of 50 bp
-- Based on the new incremental activity profile
-- Unit Tested! Fixed a few bugs with the old band pass filter
-- Expand IncrementalActivityProfileUnitTest to test the band pass filter as well for basic properties
-- Add new UnitTest for BandPassIncrementalActivityProfile
-- Added normalizeFromRealSpace to MathUtils
-- Cleanup unused code in new activity profiles
-- The incremental version now processes active regions as soon as they are ready to be processed, instead of waiting until the end of the shard as in the previous version. This means that ART walkers will now take much less memory than previously. On chr20 of NA12878 the majority of regions are processed with as few as 500 reads in memory. Over the whole chr20 only 5K reads were ever held in ART at one time.
-- Fixed bug in the way active regions worked with shard boundaries. The new implementation no longer see shard boundaries in any meaningful way, and that uncovered a problem that active regions were always being closed across shard boundaries. This behavior was actually encoded in the unit tests, so those needed to be updated as well.
-- Changed the way that preset regions work in ART. The new contract ensures that you get exactly the regions you requested. the isActive function is still called, but its result has no impact on the regions. With this functionality is should be possible to use the HC as a generic assembly by forcing it to operate over very large regions
-- Added a few misc. useful functions to IncrementalActivityProfile
-- Required before I jump in an redo the entire activity profile so it's can be run imcrementally
-- This restructuring makes the differences between the two functionalities clearer, as almost all of the functionality is in the base class. The only functionality provided by the BandPassActivityProfile is isolated to a finalizeProfile function overloaded from the base class.
-- Renamed ActivityProfileResult to ActivityProfileState, as this is a clearer indication of its actual functionality. Almost all of the misc. walker changes are due to this name update
-- Code cleanup and docs for TraverseActiveRegions
-- Expanded unit tests for ActivityProfile and ActivityProfileState
-- UnitTests now include combinational tiling of reads within and spanning shard boundaries
-- ART now properly handles shard transitions, and does so efficiently without requiring hash sets or other collections of reads
-- Updating HC and CountReadsInActiveRegions integration tests
-- Allows us to make a stream of reads or an index BAM file with read having the following properties (coming from n samples, of fixed read length and aligned to the genome with M operator, having N reads per alignment start, skipping N bases between each alignment start, starting at a given alignment start)
-- This stream can be handed back to the caller immediately, or written to an indexed BAM file
-- Update LocusIteratorByStateUnitTest to use this functionality (which was refactored from LIBS unit tests and ArtificialSAMUtils)
Out of curiosity, why does Picard's IndexedFastaSequenceFile allow one to query for start position 0? When doing so, that base is a line feed (-1 offset to the first base in the contig) which is an illegal base (and which caused me no end of trouble)...
Refactored interval specific arguments out of GATKArgumentCollection into InvtervalArgumentCollection such that it can be used in other CommandLinePrograms.
Updated SelectHeaders to print out full interval arguments.
Added RemoteFile.createUrl(Date expiration) to enable creation of presigned URLs for download over http: or file:.
This way walkers won't see anything except the standard bases plus Ns in the reference.
Added option to turn off this feature (to maintain backwards compatibility).
As part of this commit I cleaned up the BaseUtils code by adding a Base enum and removing all of the static indexes for
each of the bases. This uncovered a bug in the way the DepthOfCoverage walker counts deletions (it was counting Ns instead!) that isn't covered by tests. Fortunately that walker is being deprecated soon...
-- Run an iterator with 100Ks of reads, each carrying MBs of byte[] data, through LIBS, all starting at the same position. Will crash with an out-of-memory error if we're holding reads anywhere in the system.
-- Is there a better way to test this behavior?
-- No longer update the total counts in each per-sample state manager, but instead return delta counts that are updated by the overall ReadStateManager
-- One step on the way to improving the underlying representation of the data in PerSampleReadStateManager
-- Make LocusIteratorByState final
-- Made LIBSPerformance a full featured CommandLineProgram, and it can be used to assess the LIBS performance by reading a provided BAM
-- ReadStateManager now provides a clean interface to iterate in sample order the per-sample read states, allowing us to avoid many map.get calls
-- Moved updateReadStates to ReadStateManager
-- Removed the unnecessary wrapping of an iterator in ReadStateManager
-- readStatesBySample is now a LinkedHashMap so that iteration occurs in LIBS sample order, allowing us to avoid many unnecessary calls to map.get iterating over samples. Now those are just map native iterations
-- Restructured collectPendingReads for simplicity, removing redundant and consolidating common range checks. The new piece is code is much clearer and avoids several unnecessary function calls
David Roazen
Mark DePristo
Eric Banks
Mauricio Carneiro
Guillermo del Angel
Ami Levy-Moonshine
Khalid Shakir