-- 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
Instead of the GATK Engine creating a new BaseRecalibrator (not clean), it just keeps track of the arguments (clean).
There are still some dependency issues, but it looks like they are related to Ami's code. Need to look into it further.
-- 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.
-- With the newer, faster BQSR, scaling was limited by the NestedIntegerArray. The solution to this is to make the entire table thread-local, so that each nct thread has its own data and doesn't have any collisions.
-- Removed the previous partial solution of having a thread-local quality score table
-- Added a new argument -lowMemory
- Made few small modifications to code
- Replaced the two arguments in GATKReportTable constructor with an enum used to specify way of sorting the table
This isn't hooked up yet with BQSR; it's just a static method used in my testing walker. I'll hook this into BQSR after more testing and the addition of unit tests.
Most of the changes in this commit are actually documentation-related.
-- 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
-- Intermediate commit on the way to archiving SomaticIndelDetector and other tools.
-- SomaticIndelDetector, PairMaker and RemapAlignments tools have been refactored into the private andrey package. All utility classes refactored into here as well. At this point, the SomaticIndelDetector builds in this version of the GATK.
-- Subsequent commit will put this code into the archive so it no longer builds in the GATK
-- 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
-- Pre-read MapData into a list, which is actually faster than dealing with future lock contention issues with lots of map threads
-- Increase the ReadShard default size to 100K reads by default
-- Created a ReadRecalibrationInfo class that holds all of the information (read, base quality vectors, error vectors) for a read for the call to updateDataForRead in RecalibrationEngine. This object has a restrictive interface to just get information about specific qual and error values at offset and for event type. This restrict allows us to avoid creating an vector of byte 45 for each read to represent BI and BD values not in the reads. Shaves 5% of the runtime off the entire code.
-- Cleaned up code and added lots more docs
-- With this commit we no longer have much in the way of low-hanging fruit left in the optimization of BQSR. 95% of the runtime is spent in BAQing the read, and updating the RecalData in the NestedIntegerArrays.
-- Update SAMDataSource so that the merged header contains GATKSAMReadGroupRecord
-- Now getting the NGSPlatform for a GATKSAMRecord is actually efficient, instead of computing the NGS platform over and over from the PL string
-- Updated a few places in the code where the input argument is actually a GATKSAMRecord, not a SAMRecord for type safety
- 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
the function ls_getLicenseUsage() is not supported by LSF v8.x, comment the line:
public static native lsfLicUsage.ByReference ls_getLicenseUsage()
Signed-off-by: Eric Banks <ebanks@broadinstitute.org>
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.
-- Don't just read all inputs into a list, and then provide an iterator to that list, actually make a real iterator so NanoScheduler input thread can contribute meaningfully to the work load
-- Use NanoScheduler progress function, instead of home-grown updater
-- Refactor calculation so that upfront constant values are pre-computed, and cached, and their values just looked up during application
-- Trivial comment on how we might use BAQ better in BaseRecalibrator
-- Cleaned up code in updateDataForRead so that constant values where not computed in inner loops
-- BaseRecalibrator doesn't create it's own fasta index reader, it just piggy backs on the GATK one
-- ReadCovariates <init> now uses a thread local cache for it's int[][][] keys member variable. This stops us from recreating an expensive array over and over. In order to make this really work had to update recordValues in ContextCovariate so it writes 0s over base values its skipping because of low quality base clipping. Previously the values in the ReadCovariates keys were 0 because they were never modified by ContextCovariates. Now these values are actually zero'd out explicitly by the covariates.
-- No longer computes at each update the overall read group table. Now computes this derived table only at the end of the computation, using the ByQual table as input. Reduces BQSR runtime by 1/3 in my test
The indels are still annotated as before, but now all other variant types are annotated too.
I'm doing this because of requests on the forum but am not making it standard. If we find it to be useful we can turn it on by default later.
-- Uses high-performance local writer backed by byte array that writes the entire VCF line in some write operation to the underlying output stream.
-- Fixes problems with indexing of unflushed writes while still allowing efficient block zipping
-- Same (or better) IO performance as previous implementation
-- IndexingVariantContextWriter now properly closes the underlying output stream when it's closed
-- Updated compressed VCF output file
this introduced a bug in reduce reads by de-activating it's hard clipping of the out of bounds soft-clips (specially in the MT).
DEV-322 #resolve #time 4m
This reverts commit 42acfd9d0bccfc0411944c342a5b889f5feae736.
-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.
As reported by Menachem Fromer: a critical bug in AFCalcResult:
Specifically, the implementation:
public boolean isPolymorphic(final Allele allele, final double log10minPNonRef) {
return getLog10PosteriorOfAFGt0ForAllele(allele) >= log10minPNonRef;
}
seems incorrect and should probably be:
getLog10PosteriorOfAFEq0ForAllele(allele) <= log10minPNonRef
The issue here is that the 30 represents a Phred-scaled probability of *error* and it's currently being compared to a log probability of *non-error*.
Instead, we need to require that our probability of error be less than the error threshold.
This bug has only a minor impact on the calls -- hardly any sites change -- which is good. But the inverted logic effects multi-allelic sites significantly. Basically you only hit this logic with multiple alleles, and in that case it'\s including extra alt alleles incorrectly, and throwing out good ones.
Change was to create a new function that properly handles thresholds that are PhredScaled quality scores:
/**
* Same as #isPolymorphic but takes a phred-scaled quality score as input
*/
public boolean isPolymorphicPhredScaledQual(final Allele allele, final double minPNonRefPhredScaledQual) {
if ( minPNonRefPhredScaledQual < 0 ) throw new IllegalArgumentException("phredScaledQual " + minPNonRefPhredScaledQual + " < 0 ");
final double log10Threshold = Math.log10(QualityUtils.qualToProb(minPNonRefPhredScaledQual));
return isPolymorphic(allele, log10Threshold);
}
Mark DePristo
Mauricio Carneiro
Eric Banks
Ami Levy-Moonshine
Ryan Poplin
Tad Jordan
Chris Hartl
Joel Thibault
Chris Hartl
xingwei2012
David Roazen
Yossi Farjoun
Randal Moore
Menachem Fromer