-- Has the overall effect that the GATK user AWS keys are no longer visible in the gatk source as plain text. This will stop AWS from emailing me (they crawl the web looking for keys)
-- Added utility EncryptAWSKeys that takes as command line arguments the GATK user AWS access and secret keys, encrypts them with the GATK private key, and writes out the resulting file to resources in phonehome.
-- GATKRunReport now decrypts as needed these keys using the GATK public key as resources in the GATK bundle
-- Refactored the essential function of Resource (reading the resource) from IOUtils into the class itself. Now how to get the data in the resouce is straightforward
-- Refactored md5 calculation code from a byte[] into Utils. Added unit tests
-- Committing the encrypted AWS keys
-- #resolves https://jira.broadinstitute.org/browse/GSA-730
-- 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
-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.
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
-- 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:.
-- 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
-- Only ReadBackedPileupImpl (concrete class) and ReadBackedPileup (interface) live, moved all functionality of AbstractReadBackedPileup into the impl
-- ReadBackedPileupImpl was literally a shell class after we removed extended events. A few bits of code cleanup and we reduced a bunch of class complexity in the gatk
-- ReadBackedPileups no longer accept pre-cached values (size, nMapQ reads, etc) but now lazy load these values as needed
-- Created optimized calculation routines to iterator over all of the reads in the pileup in whatever order is most efficient as well.
-- New LIBS no longer calculates size, n mapq, and n deletion reads while making pileups.
-- Added commons-collections for IteratorChain
-- function to create pileup elements in AlignmentStateMachine and LIBS
-- Cleanup pileup element constructors, directing users to LIBS.createPileupFromRead() that really does the right thing
-- Optimizations to AlignmentStateMachine
-- Properly count deletions. Added unit test for counting routines
-- AlignmentStateMachine.java is no longer recursive
-- Traversals now use new LIBS, not the old one
-- AlignmentStateMachine does what SAMRecordAlignmentState should really do. It's correct in that it's more accurate than the LIB_position tests themselves. This is a non-broken, correct implementation. Needs cleanup, contracts, etc.
-- This version is like 6x slower than the original implementation (according to the google caliper benchmark here). Obvious optimizations for future commit
-- This capability is essential to provide an ordered set of used reads to downstream users of LIBS, such as ART, who want an efficient way to get the reads used in LIBS
-- Vastly expanded the multi-read, multi-sample LIBS unit tests to make sure this capability is working
-- Added createReadStream to ArtificialSAMUtils that makes it relatively easy to create multi-read, multi-sample read streams for testing
Mark DePristo
David Roazen
Eric Banks
Mauricio Carneiro
Khalid Shakir