-- 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
-- Split out all of the inner classes of LIBS into separate independent classes
-- Split / add unit tests for many of these components.
-- Radically expand unit tests for SAMRecordAlignmentState (the lowest level piece of code) making sure at least some of it works
-- No need to change unit tests or integration tests. No change in functionality.
-- Added (currently disabled) code to track all submitted reads to LIBS, but this isn't accessible or tested
-- Added unit tests for combining RecalibrationTables. As a side effect now has serious tests for incrementDatumOrPutIfNecessary
-- Removed unnecessary enum.index system from RecalibrationTables.
-- Moved what were really static utility methods out of RecalibrationEngine and into RecalUtils.
-- Added unit tests for EventType and ReadRecalibrationInfo
-- Simplified interface of EventType. Previously this enum carried an index with it, but this is redundant with the enum.ordinal function. Now just using that function instead.
- Made few small modifications to code
- Replaced the two arguments in GATKReportTable constructor with an enum used to specify way of sorting the table
-- Underlying system now uses long nano times to be more consistent with standard java practice
-- Updated a few places in the code that were converting from nanoseconds to double seconds to use the new nanoseconds interface directly
-- Bringing us to 100% test coverage with clover with AutoFormattingTimeUnitTest
-- AdvancedRecalibrationEngine now uses a thread-local table for the quality score table, and in finalizeData merges these thread-local tables into the final table. Radically reduces the contention for RecalDatum in this very highly used table
-- Refactored the utility function to combine two tables into RecalUtils, and created UnitTests for this function, as well as all of RecalibrationTables. Updated combine in RecalibrationReport to use this table combiner function
-- Made several core functions in RecalDatum into final methods for performance
-- Added RecalibrationTestUtils, a home for recalibration testing utilities
-- The previous model was to enqueue individual map jobs (with a resolution of 1 map job per map call), to track the number of map calls submitted via a counter and a semaphore, and to use this information in each map job and reduce to control the number of map jobs, when reduce was complete, etc. All hideously complex.
-- This new model is vastly simply. The reducer basically knows nothing about the control mechanisms in the NanoScheduler. It just supports multi-threaded reduce. The NanoScheduler enqueues exactly nThread jobs to be run, which continually loop reading, mapping, and reducing until they run out of material to read, when they shut down. The master thread of the NS just holds a CountDownLatch, initialized to nThreads, and when each thread exits it reduces the latch by 1. The master thread gets the final reduce result when its free by the latch reaching 0. It's all super super simple.
-- Because this model uses vastly fewer synchronization primitives within the NS itself, it's naturally much faster at getting things done, without any of the overhead obvious in profiles of BQSR -nct 2.
-- reduceAsMuchAsPossible no longer blocks threads via synchronization, but instead uses an explicit lock to manage access. If the lock is already held (because some thread is doing reduce) then the thread attempting to reduce immediately exits the call and continues doing productive work. They removes one major source of blocking contention in the NanoScheduler
-- Created a separate, limited interface MapResultsQueue object that previously was set to the PriorityBlockingQueue.
-- The MapResultsQueue is now backed by a synchronized ExpandingArrayList, since job ids are integers incrementing from 0 to N. This means we avoid the n log n sort in the priority queue which was generating a lot of cost in the reduce step
-- Had to update ReducerUnitTest because the test itself was brittle, and broken when I changed the underlying code.
-- A few bits of minor code cleanup through the system (removing unused constructors, local variables, etc)
-- ExpandingArrayList called ensureCapacity so that we increase the size of the arraylist once to accommodate the upcoming size needs
- Added an optional argument to BaseRecalibrator to produce sorted GATKReport Tables
- Modified BSQR Integration Tests to include the optional argument. Tests now produce sorted tables
This is an intermediate commit so that there is a record of these changes in our
commit history. Next step is to isolate the test classes as well, and then move
the entire package to the Picard repository and replace it with a jar in our repo.
-Removed all dependencies on org.broadinstitute.sting (still need to do the test classes,
though)
-Had to split some of the utility classes into "GATK-specific" vs generic methods
(eg., GATKVCFUtils vs. VCFUtils)
-Placement of some methods and choice of exception classes to replace the StingExceptions
and UserExceptions may need to be tweaked until everyone is happy, but this can be
done after the move.
-- Now each map job reads a value, performs map, and does as much reducing as possible. This ensures that we scale performance with the nct value, so -nct 2 should result in 2x performance, -nct 3 3x, etc. All of this is accomplished using exactly NCT% of the CPU of the machine.
-- Has the additional value of actually simplifying the code
-- Resolves a long-standing annoyance with the nano scheduler.
-Switch back to the old implementation, if needed, with --use_legacy_downsampler
-LocusIteratorByStateExperimental becomes the new LocusIteratorByState, and
the original LocusIteratorByState becomes LegacyLocusIteratorByState
-Similarly, the ExperimentalReadShardBalancer becomes the new ReadShardBalancer,
with the old one renamed to LegacyReadShardBalancer
-Performance improvements: locus traversals used to be 20% slower in the new
downsampling implementation, now they are roughly the same speed.
-Tests show a very high level of concordance with UG calls from the previous
implementation, with some new calls and edge cases that still require more examination.
-With the new implementation, can now use -dcov with ReadWalkers to set a limit
on the max # of reads per alignment start position per sample. Appropriate value
for ReadWalker dcov may be in the single digits for some tools, but this too
requires more investigation.
-- The NanoSchedule timing code (in NSRuntimeProfile) was crazy expensive, but never showed up in the profilers. Removed all of the timing code from the NanoScheduler, the NSRuntimeProfile itself, and updated the unit tests.
-- For tools that largely pass through data quickly, this change reduces runtimes by as much as 10x. For the RealignerTargetCreator example, the runtime before this commit was 3 hours, and after is 30 minutes (6x improvement).
-- Took this opportunity to improve the GATK ProgressMeter. NotifyOfProgress now just keeps track of the maximum position seen, and a separate daemon thread ProgressMeterDaemon periodically wakes up and prints the current progress. This removes all inner loop calls to the GATK timers.
-- The history of the bug started here: http://gatkforums.broadinstitute.org/discussion/comment/2402#Comment_2402
-- The previous nanoscheduler would deadlock in the case where an Error, not an Exception, was thrown. Errors, like out of memory, would cause the whole system to die. This bugfix resolves that issue
The check is performed by a Read Transformer that samples (currently set to once
every 1000 reads so that we don't hurt overall GATK performance) from the input
reads and checks to make sure that none of the base quals is too high (> Q60). If
we encounter such a base then we fail with a User Error.
* Can be over-ridden with --allow_potentially_misencoded_quality_scores.
* Also, the user can choose to fix his quals on the fly (presumably using PrintReads
to write out a fixed bam) with the --fix_misencoded_quality_scores argument.
Added unit tests.
-- Multi-allelic variants are split into their bi-allelic version, trimmed, and we attempt to provide a meaningful genotype for NA12878 here. It's not perfect and needs some discussion on how to handle het/alt variants
-- Adding splitInBiallelic funtion to VariantContextUtils as well as extensive unit tests that also indirectly test reverseTrimAlleles (which worked perfectly FYI)
Mark DePristo
Mauricio Carneiro
Eric Banks
Chris Hartl
Joel Thibault
Chris Hartl
Tad Jordan
David Roazen
Yossi Farjoun
Ami Levy-Moonshine