-User must provide a mapping file via new --sample_rename_mapping_file argument.
Mapping file must contain a mapping from absolute bam file path to new sample name
(format is described in the docs for the argument).
-Requires that each bam file listed in the mapping file contain only one sample
in their headers (they may contain multiple read groups for that sample, however).
The engine enforces this, and throws a UserException if on-the-fly renaming is
requested for a multi-sample bam.
-Not all bam files for a traversal need to be listed in the mapping file.
-On-the-fly renaming is done as the VERY first step after creating the SAMFileReaders
in SAMDataSource (before the headers are even merged), to prevent possible consistency
issues.
-Renaming is done ONCE at traversal start for each SAMReaders resource creation in the
SAMResourcePool; this effectively means once per -nt thread
-Comprehensive unit/integration tests
Known issues: -if you specify the absolute path to a bam in the mapping file, and then
provide a path to that same bam to -I using SYMLINKS, the renaming won't
work. The absolute paths will look different to the engine due to the
symlink being present in one path and not in the other path.
GSA-974 #resolve
-Two SAMReaderIDs that pointed at the same underlying bam file through
a relative vs. an absolute path were not being treated as equal, and
had different hash codes. This was causing problems in the engine, since
SAMReaderIDs are often used as the keys of HashMaps.
-Fix: explicitly use the absolute path to the encapsulated bam file in
hashCode() and equals()
-Added tests to ensure this doesn't break again
1. Some minor refactorings and claenup (e.g. removing unused imports) throughout.
2. Updates to the KB assessment functionality:
a. Exclude duplicate reads when checking to see whether there's enough coverage to make a call.
b. Lower the threshold on FS for FPs that would easily be filtered since it's only single sample calling.
3. Make the HC consistent in how it treats the pruning factor. As part of this I removed and archived
the DeBruijn assembler.
4. Improvements to the likelihoods for the HC
a. We now include a "tristate" correction in the PairHMM (just like we do with UG). Basically, we need
to divide e by 3 because the observed base could have come from any of the non-observed alleles.
b. We now correct overlapping read pairs. Note that the fragments are not merged (which we know is
dangerous). Rather, the overlapping bases are just down-weighted so that their quals are not more
than Q20 (or more specifically, half of the phred-scaled PCR error rate); mismatching bases are
turned into Q0s for now.
c. We no longer run contamination removal by default in the UG or HC. The exome tends to have real
sites with off kilter allele balances and we occasionally lose them to contamination removal.
5. Improved the dangling tail merging implementation.
-This test is failing intermittently for unexplained reasons (see GSA-943)
-In the interest of keeping the rest of the pipeline test suite running, it's
best to disable this one test until GSA-943 is resolved
-- Assembly graph building now returns an object that describes whether the graph was successfully built and has variation, was succesfully built but didn't have variation, or truly failed in construction. Fixing an annoying bug where you'd prefectly assembly the sequence into the reference graph, but then return a null graph because of this, and you'd increase your kmer because it null was also used to indicate assembly failure
--
-- Output format looks like:
20 10026072 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:3,0:3:9:0,9,120
20 10026073 . A <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:3,0:3:9:0,9,119
20 10026074 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:3,0:3:9:0,9,121
20 10026075 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:3,0:3:9:0,9,119
20 10026076 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:3,0:3:9:0,9,120
20 10026077 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:3,0:3:9:0,9,120
20 10026078 . C <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:5,0:5:15:0,15,217
20 10026079 . A <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:6,0:6:18:0,18,240
20 10026080 . G <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:6,0:6:18:0,18,268
20 10026081 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:7,0:7:21:0,21,267
We use a symbolic allele to indicate that the site is hom-ref, and because we have an ALT allele we can provide AD and PL field values. Currently these are calculated as ref vs. any non-ref value (mismatch or insertion) but doesn't yet account properly for alignment uncertainty.
-- Can we enabled for single samples with --emitRefConfidence (-ERC).
-- This is accomplished by realigning the each read to its most likley haplotype, and then evaluting the resulting pileups over the active region interval. The realignment is done by the HaplotypeBAMWriter, which now has a generalized interface that lets us provide a ReadDestination object so we can capture the realigned reads
-- Provide access to the more raw LocusIteratorByState constructor so we can more easily make them programmatically without constructing lots of misc. GATK data structures. Moved the NO_DOWNSAMPLING constant from LIBSDownsamplingInfo to LocusIteratorByState so clients can use it without making LIBSDownsamplingInfo a public class.
-- Includes GVCF writer
-- Add 1 mb of WEx data to private/testdata
-- Integration tests for reference model output for WGS and WEx data
-- Emit GQ block information into VCF header for GVCF mode
-- OutputMode from StandardCallerArgumentCollection moved to UnifiedArgumentCollection as its no longer relevant for HC
-- Control max indel size for the reference confidence model from the command line. Increase default to 10
-- Don't use out_mode in HaplotypeCallerComplexAndSymbolicVariantsIntegrationTest
-- Unittests for ReferenceConfidenceModel
-- Unittests for new MathUtils functions
-- The previous code would adapter clip before reverting soft clips, so because we only clip the adapter when it's actually aligned (i.e., not in the soft clips) we were actually not removing bases in the adapter unless at least 1 bp of the adapter was aligned to the reference. Terrible.
-- Removed the broken logic of determining whether a read adaptor is too long.
-- Doesn't require isProperPairFlag to be set for a read to be adapter clipped
-- Update integration tests for new adapter clipping code
-Explicitly state that -dcov does not produce an unbiased random sampling from all available reads
at each locus, and that instead it tries to maintain an even representation of reads from
all alignment start positions (which, of course, is a form of bias)
-Recommend -dfrac for users who want a true across-the-board unbiased random sampling
-- Because LocusWalkers have multiple filtering streams, each counting filtering independent, and the close() function set calling setFilter on the global result, not on the private counter, which is incorporated into the global (thereby incrementing the counts of each filter).
-- [delivers #52667213]
There are a few pipeline test classes that do not run Queue, but are
classified as pipeline tests because they submit farm jobs. Make these
unconventional pipeline tests respect the pipeline test dry run setting.
Previous fixes and tests only covered trailing soft-clips. Now that up front
hard-clipping is working properly though, we were failing on those in the tool.
Added a patch for this as well as a separate test independent of the soft-clips
to make sure that it's working properly.
-- Previous version emitted command lines that look like:
##HaplotypeCaller="analysis_type=HaplotypeCaller input_file=[private/testdata/reduced.readNotFullySpanningDeletion.bam] ..."
the new version provides additional information on when the GATK was run and the GATK version in a nicer format:
##GATKCommandLine=<ID=HaplotypeCaller,Version=2.5-206-gbc7be2b,Date="Thu Jun 20 11:09:01 EDT 2013",Epoch=1371740941197,CommandLineOptions="analysis_type=HaplotypeCaller input_file=[private/testdata/reduced.readNotFullySpanningDeletion.bam] read_buffer_size=null phone_home=AWS ...">
-- Additionally, the command line options are emitted sequentially in the file, so you can see a running record of how a VCF was produced, such as this example from the integration test:
##GATKCommandLine=<ID=HaplotypeCaller,Version=2.5-206-gbc7be2b,Date="Thu Jun 20 11:09:01 EDT 2013",Epoch=1371740941197,CommandLineOptions="lots of stuff">
##GATKCommandLine=<ID=SelectVariants,Version=2.5-206-gbc7be2b,Date="Thu Jun 20 11:16:23 EDT 2013",Epoch=1371741383277,CommandLineOptions="lots of stuff">
-- Removed the ProtectedEngineFeaturesIntegrationTest
-- Actual unit tests for these features!
Improved AnalyzeCovariates (AC) integration test.
Renamed AC test files ending with .grp to .table
Implementation:
* Removed RECAL_PDF/CSV_FILE from RecalibrationArgumentCollection (RAC). Updated rest of the code accordingly.
* Fixed BQSRIntegrationTest to work with new changes
Implemtation details:
* Added tool class *.AnalyzeCovariates
* Added convenient addAll method to Utils to be able to add elements of an array.
* Added parameter comparison methods to RecalibrationArgumentCollection class in order to verify that multiple imput recalibration report are compatible and comparable.
* Modified the BQSR.R script to handle up to 3 different recalibration tables (-BQSR, -before and -after) and removed some irrelevant arguments (or argument values) from the output.
* Added an integration test class.
-Collapses zero-length and repeated cigar elements, neither of which
can necessarily be handled correctly by downstream code (like LIBS).
-Consolidation is done before read filters, because not all read filters
behave correctly with non-consoliated cigars.
-Examined other uses of consolidateCigar() throughout the GATK, and
found them to not be redundant with the new engine-level consolidation
(they're all on artificially-created cigars in the HaplotypeCaller
and SmithWaterman classes)
-Improved comments in SAMDataSource.applyDecoratingIterators()
-Updated MD5s; differences were examined and found to be innocuous
-Two tests: -Unit test for ReadFormattingIterator
-Integration test for correct handling of zero-length
cigar elements by the GATK engine as a whole
-This argument was completely redundant with the engine-level -dfrac
argument.
-Could produce unintended consequences if used in conjunction with
engine-level downsampling arguments.
-- It was being applied in the wrong order (after the first call to the underlying MalformedReadFilter) so if your first read was malformed you'd blow up there instead of being fixed properly. Added integration tests to ensure this continues to work.
-- [delivers #49538319]
-- When doing cross-species comparisons and studying population history and ancient DNA data, having SOME measure of confidence is needed at every single site that doesn't depend on the reference base, even in a naive per-site SNP mode. Old versions of GATK provided GQ and some wrong PL values at reference sites but these were wrong. This commit addresses this need by adding a new UG command line argument, -allSitePLs, that, if enabled will:
a) Emit all 3 ALT snp alleles in the ALT column.
b) Emit all corresponding 10 PL values.
It's up to the user to process these PL values downstream to make sense of these. Note that, in order to follow VCF spec, the QUAL field in a reference call when there are non-null ALT alleles present will be zero, so QUAL will be useless and filtering will need to be done based on other fields.
-- Tweaks and fixes to processing pipelines for Reich lab.
-- VariantContextWriterStorage was gzipping the intermediate files that would be merged in, but the mergeInto function couldn't read those outputs, and we'd throw a very strange error. Now tmp. VCFs aren't compressed, even if the final VCF is. Added integrationtest to ensure this behavior works going forward.
-- [delivers #47399279]
-- Previous version created FILTERs for each possible alt allele when that site was set to monomorphic by BEAGLE. So if you had a A/C SNP in the original file and beagle thought it was AC=0, then you'd get a record with BGL_RM_WAS_A in the FILTER field. This obviously would cause problems for indels, as so the tool was blowing up in this case. Now beagle sets the filter field to BGL_SET_TO_MONOMORPHIC and sets the info field annotation OriginalAltAllele to A instead. This works in general with any type of allele.
-- Here's an example output line from the previous and current versions:
old: 20 64150 rs7274499 C . 3041.68 BGL_RM_WAS_A AN=566;DB;DP=1069;Dels=0.00;HRun=0;HaplotypeScore=238.33;LOD=3.5783;MQ=83.74;MQ0=0;NumGenotypesChanged=1;OQ=1949.35;QD=10.95;SB=-6918.88
new: 20 64062 . G . 100.39 BGL_SET_TO_MONOMORPHIC AN=566;DP=1108;Dels=0.00;HRun=2;HaplotypeScore=221.59;LOD=-0.5051;MQ=85.69;MQ0=0;NumGenotypesChanged=1;OQ=189.66;OriginalAltAllele=A;QD=15.81;SB=-6087.15
-- update MD5s to reflect these changes
-- [delivers #50847721]
-WalkerTest now deletes *.idx files on exit
-ArtificialBAMBuilder now deletes *.bai files on exit
-VariantsToBinaryPed walker now deletes its temp files on exit
Problem:
Classes in com.sun.javadoc.* are non-standard. Since we can't depend on their availability for
all users, the GATK proper should not have any runtime dependencies on this package.
Solution:
-Isolate com.sun.javadoc.* dependencies in a DocletUtils class for use only by doclets. The
only users who need to run our doclets are those who compile from source, and they
should be competent enough to figure out how to resolve a missing com.sun.* dependency.
-HelpUtils now contains no com.sun.javadoc.* dependencies and can be safely used by walkers/other
tools.
-Added comments with instructions on when it is safe to use DocletUtils vs. HelpUtils
[delivers #51450385]
[delivers #50387199]
-- Now table looks like:
Name VariantType AssessmentType Count
variant SNPS TRUE_POSITIVE 1220
variant SNPS FALSE_POSITIVE 0
variant SNPS FALSE_NEGATIVE 1
variant SNPS TRUE_NEGATIVE 150
variant SNPS CALLED_NOT_IN_DB_AT_ALL 0
variant SNPS HET_CONCORDANCE 100.00
variant SNPS HOMVAR_CONCORDANCE 99.63
variant INDELS TRUE_POSITIVE 273
variant INDELS FALSE_POSITIVE 0
variant INDELS FALSE_NEGATIVE 15
variant INDELS TRUE_NEGATIVE 79
variant INDELS CALLED_NOT_IN_DB_AT_ALL 2
variant INDELS HET_CONCORDANCE 98.67
variant INDELS HOMVAR_CONCORDANCE 89.58
-- Rewrite / refactored parts of subsetDiploidAlleles in GATKVariantContextUtils to have a BEST_MATCH assignment method that does it's best to simply match the genotype after subsetting to a set of alleles. So if the original GT was A/B and you subset to A/B it remains A/B but if you subset to A/C you get A/A. This means that het-alt B/C genotypes become A/B and A/C when subsetting to bi-allelics which is the convention in the KB. Add lots of unit tests for this functions (from 0 previously)
-- BadSites in Assessment now emits TP sites with discordant genotypes with the type GENOTYPE_DISCORDANCE and tags the expected genotype in the info field as ExpectedGenotype, such as this record:
20 10769255 . A ATGTG 165.73 . ExpectedGenotype=HOM_VAR;SupportingCallsets=ebanks,depristo,CEUTrio_best_practices;WHY=GENOTYPE_DISCORDANCE GT:AD:DP:GQ:PL 0/1:1,9:10:6:360,0,6
Indicating that the call was a HET but the expected result was HOM_VAR
-- Forbid subsetting of diploid genotypes to just a single allele.
-- Added subsetToRef as a separate specific function. Use that in the DiploidExactAFCalc in the case that you need to reduce yourself to ref only. Preserves DP in the genotype field when this is possible, so a few integration tests have changed for the UG
Problem:
-Downsamplers were treating reduced reads the same as normal reads,
with occasionally catastrophic results on variant calling when an
entire reduced read happened to get eliminated.
Solution:
-Since reduced reads lack the information we need to do position-based
downsampling on them, best available option for now is to simply
exempt all reduced reads from elimination during downsampling.
Details:
-Add generic capability of exempting items from elimination to
the Downsampler interface via new doNotDiscardItem() method.
Default inherited version of this method exempts all reduced reads
(or objects encapsulating reduced reads) from elimination.
-Switch from interfaces to abstract classes to facilitate this change,
and do some minor refactoring of the Downsampler interface (push
implementation of some methods into the abstract classes, improve
names of the confusing clear() and reset() methods).
-Rewrite TAROrderedReadCache. This class was incorrectly relying
on the ReservoirDownsampler to preserve the relative ordering of
items in some circumstances, which was behavior not guaranteed by
the API and only happened to work due to implementation details
which no longer apply. Restructured this class around the assumption
that the ReservoirDownsampler will not preserve relative ordering
at all.
-Add disclaimer to description of -dcov argument explaining that
coverage targets are approximate goals that will not always be
precisely met.
-Unit tests for all individual downsamplers to verify that reduced
reads are exempted from elimination
We now run Smith-Waterman on the dangling tail against the corresponding reference tail.
If we can generate a reasonable, low entropy alignment then we trigger the merge to the
reference path; otherwise we abort. Also, we put in a check for low-complexity of graphs
and don't let those pass through.
Added tests for this implementation that checks exact SW results and correct edges added.
-- Reuse infrastructure for RODs for reads to implement general IntervalReferenceOrderedView so that both TraverseReads and TraverseActiveRegions can use the same underlying infrastructure
-- TraverseActiveRegions now provides a meaningful RefMetaDataTracker to ActiveRegionWalker.map
-- Cleanup misc. code as it came up
-- Resolves GSA-808: Write general utility code to do rsID allele matching, hook up to UG and HC
-- Variants will be considered matching if they have the same reference allele and at least 1 common alternative allele. This matching algorithm determines how rsID are added back into the VariantContext we want to annotate, and as well determining the overlap FLAG attribute field.
-- Updated VariantAnnotator and VariantsToVCF to use this class, removing its old stale implementation
-- Added unit tests for this VariantOverlapAnnotator class
-- Removed GATKVCFUtils.rsIDOfFirstRealVariant as this is now better to use VariantOverlapAnnotator
-- Now requires strict allele matching, without any option to just use site annotation.
The previous behavior is to process reads with N CIGAR operators as they are despite that many of the tools do not actually support such operator and results become unpredictible.
Now if the there is some read with the N operator, the engine returns a user exception. The error message indicates what is the problem (including the offending read and mapping position) and give a couple of alternatives that the user can take in order to move forward:
a) ask for those reads to be filtered out (with --filter_reads_with_N_cigar or -filterRNC)
b) keep them in as before (with -U ALLOW_N_CIGAR_READS or -U ALL)
Notice that (b) does not have any effect if (a) is enacted; i.e. filtering overrides ignoring.
Implementation:
* Added filterReadsWithMCigar argument to MalformedReadFilter with the corresponding changes in the code to get it to work.
* Added ALLOW_N_CIGAR_READS unsafe flag so that N cigar containing reads can be processed as they are if that is what the user wants.
* Added ReadFilterTest class commont parent for ReadFilter test cases.
* Refactor ReadGroupBlackListFilterUnitTest to extend ReadFilterTest and push up some functionality to that class.
* Modified MalformedReadFilterUnitTest to extend ReadFilterTest and to test the new filter functionality.
* Added AllowNCigarMalformedReadFilterUnittest to check on the behavior when the unsafe ALLOW_N_CIGAR_READS flag is used.
* Added UnsafeNCigarMalformedReadFilterUnittest to check on the behavior when the unsafe ALL flag is used.
* Updated a broken test case in UnifiedGenotyperIntegrationTest resulting from the new behavior.
* Updated EngineFeaturesIntegrationTest testdata to be compliant with new behavior
- Memoized MathUtil's cumulative binomial probability function.
- Reduced the default size of the read name map in reduced reads and handle its resets more efficiently.
-- In the case where we have multiple potential alternative alleles *and* we weren't calling all of them (so that n potential values < n called) we could end up trimming the alleles down which would result in the mismatch between the PerReadAlleleLikelihoodMap alleles and the VariantContext trimmed alleles.
-- Fixed by doing two things (1) moving the trimming code after the annotation call and (2) updating AD annotation to check that the alleles in the VariantContext and the PerReadAlleleLikelihoodMap are concordant, which will stop us from degenerating in the future.
-- delivers [#50897077]
-- This occurred because we were reverting reads with soft clips that would produce reads with negative (or 0) alignment starts. From such reads we could end up with adaptor starts that were negative and that would ultimately produce the "Only one of refStart or refStop must be < 0, not both" error in the FragmentUtils merging code (which would revert and adaptor clip reads).
-- We now hard clip away bases soft clipped reverted bases that fall before the 1-based contig start in revertSoftClippedBases.
-- Replace buggy cigarFromString with proper SAM-JDK call TextCigarCodec.getSingleton().decode(cigarString)
-- Added unit tests for reverting soft clipped bases that create a read before the contig
-- [delivers #50892431]
-- Ultimately this was caused by an underlying bug in the reverting of soft clipped bases in the read clipper. The read clipper would fail to properly set the alignment start for reads that were 100% clipped before reverting, such as 10H2S5H => 10H2M5H. This has been fixed and unit tested.
-- Update 1 ReduceReads MD5, which was due to cases where we were clipping away all of the MATCH part of the read, leaving a cigar like 50H11S and the revert soft clips was failing to properly revert the bases.
-- delivers #50655421
-- Although the original bug report was about SplitSamFile it actually was an engine wide error. The two places in the that provide compression to the BAM write now check the validity of the compress argument via a static method in ReadUtils
-- delivers #49531009
-- We now inject the given alleles into the reference haplotype and add them to the graph.
-- Those paths are read off of the graph and then evaluated with the appropriate marginalization for GGA mode.
-- This unifies how Smith-Waterman is performed between discovery and GGA modes.
-- Misc minor cleanup in several places.
1) Add in checks for input parameters in MathUtils method. I was careful to use the bottom-level methods whenever possible, so that parameters don't needlessly go through multiple checks (so for instance, the parameters n and k for a binomial aren't checked on log10binomial, but rather in the log10binomialcoefficient subroutine).
This addresses JIRA GSA-767
Unit tests pass (we'll let bamboo deal with the integrations)
2) Address reviewer comments (change UserExceptions to IllegalArgumentExceptions).
3) .isWellFormedDouble() tests for infinity and not strictly positive infinity. Allow negative-infinity values for log10sumlog10 (as these just correspond to p=0).
After these commits, unit and integration tests now pass, and GSA-767 is done.
rebase and fix conflict:
public/java/src/org/broadinstitute/sting/utils/MathUtils.java
-- This allows us to use -rf ReassignMappingQuality to reassign mapping qualities to 60 *before* the BQSR filters them out with MappingQualityUnassignedFilter.
-- delivers #50222251
The problem ultimately was that ReadUtils.readStartsWithInsertion() ignores leading hard/softclips, but
ReduceReads does not. So I refactored that method to include a boolean argument as to whether or not
clips should be ignored. Also rebased so that return type is no longer a Pair.
Added unit test to cover this situation.
-ReadShardBalancer was printing out an extra "Loading BAM index data for next contig"
message at traversal end, which was confusing users and making the GATK look stupid.
Suppress the extraneous message, and reword the log messages to be less confusing.
-Improve log message output when initializing the shard iterator in GenomeAnalysisEngine.
Don't mention BAMs when the are none, and say "Preparing for traversal" rather than
mentioning the meaningless-for-users concept of "shard strategy"
-These log messages are needed because the operations they surround might take a
while under some circumstances, and the user should know that the GATK is actively
doing something rather than being hung.
-Throw a UserException if a Locus or ActiveRegion walker is run with -dcov < 200,
since low dcov values can result in problematic downsampling artifacts for locus-based
traversals.
-Read-based traversals continue to have no minimum for -dcov, since dcov for read traversals
controls the number of reads per alignment start position, and even a dcov value of 1 might
be safe/desirable in some circumstances.
-Also reorganize the global downsampling defaults so that they are specified as annotations
to the Walker, LocusWalker, and ActiveRegionWalker classes rather than as constants in the
DownsamplingMethod class.
-The default downsampling settings have not been changed: they are still -dcov 1000
for Locus and ActiveRegion walkers, and -dt NONE for all other walkers.
BandedHMM
---------
-- An implementation of a linear runtime, linear memory usage banded logless PairHMM. Thought about 50% faster than current PairHMM, this implementation will be superceded by the GraphHMM when it becomes available. The implementation is being archived for future reference
Useful infrastructure changes
-----------------------------
-- Split PairHMM into a N2MemoryPairHMM that allows smarter implementation to not allocate the double[][] matrices if they don't want, which was previously occurring in the base class PairHMM
-- Added functionality (controlled by private static boolean) to write out likelihood call information to a file from inside of LikelihoodCalculationEngine for using in unit or performance testing. Added example of 100kb of data to private/testdata. Can be easily read in with the PairHMMTestData class.
-- PairHMM now tracks the number of possible cell evaluations, and the LoglessCachingPairHMM updates the nCellsEvaluated so we can see how many cells are saved by the caching calculation.
Don't map class to counts in the ReadMetrics (necessitating 2 HashMap lookups for every increment).
Instead, wrap the ReadFilters with a counting version and then set those counts only when updating global metrics.
-- Add() call had a misplaced map.put call, so that we were always putting the result of get() back into the map, when what we really intended was to only put the value back in if the original get() resulted in a null and so initialized the result
-- Previous version took a Collection<GATKSAMRecord> to remove, and called ArrayList.removeAll() on this collection to remove reads from the ActiveRegion. This can be very slow when there are lots of reads, as ArrayList.removeAll ultimately calls indexOf() that searches through the list calling equals() on each element. New version takes a set, and uses an iterator on the list to remove() from the iterator any read that is in the set. Given that we were already iterating over the list of reads to update the read span, this algorithm is actually simpler and faster than the previous one.
-- Update HaplotypeCaller filterReadsInRegion to use a Set not a List.
-- Expanded the unit tests a bit for ActiveRegion.removeAll
-- The previous version of PerReadAlleleLikelihoodMap only stored the alleles in an ArrayList, and used ArrayList.contains() to determine if an allele was already present in the map. This is very slow with many alleles. Now keeps both the ArrayList (for get() performance) and a Set of alleles for contains().
1. Don't clone the dataSource's metrics object (because then the engine won't continue to get updated counts)
2. Use the dataSource's metrics object in the CountingFilteringIterator and not the first shard's object!
3. Synchronize ReadMetrics.incrementMetrics to prevent race conditions.
Also:
* Make sure users realize that the read counts are approximate in the print outs.
* Removed a lot of unused cruft from the metrics object while I was in there.
* Added test to make sure that the ReadMetrics read count does not overflow ints.
* Added unit tests for traversal metrics (reads, loci, and active region traversals); these test counts of reads and records.
-- [Delivers #49876703]
-- Add integration test and test file
-- Update SymbolicAlleles combine variant tests, which was turning unfiltered records into PASS!
- Converted my old GATKBAMIndexText (within PileupWalkerIntegrationTest) to use a dataProvider
- Added two integration tests to test -outputInsertLength option
-- The previous implementation of the maxRuntime would require us to wait until all of the work was completed within a shard, which can be a substantial amount of work in the case of a locus walker with 16kb shards.
-- This implementation ensures that we exit from the traversal very soon after the max runtime is exceeded, without completely all of our work within the shard. This is done by updating all of the traversal engines to return false for hasNext() in the nano scheduled input provider. So as soon as the timeout is exceeeded, we stop generating additional data to process, and we only have to wait until the currently executing data processing unit (locus, read, active region) completes.
-- In order to implement this timeout efficiently at this fine scale, the progress meter now lives in the genome analysis engine, and the exceedsTimeout() call in the engine looks at a periodically updated runtime variable in the meter. This variable contains the elapsed runtime of the engine, but is updated by the progress meter daemon thread so that the engine doesn't call System.nanotime() in each cycle of the engine, which would be very expense. Instead we basically wait for the daemon to update this variable, and so our precision of timing out is limited by the update frequency of the daemon, which is on the order of every few hundred milliseconds, totally fine for a timeout.
-- Added integration tests to ensure that subshard timeouts are working properly
-- Previously we used the LocusShardBalancer for the haplotype caller, which meant that TraverseActiveRegions saw its shards grouped in chunks of 16kb bits on the genome. These locus shards are useful when you want to use the HierarchicalMicroScheduler, as they provide fine-grained accessed to the underlying BAM, but they have two major drawbacks (1) we have to fairly frequently reset our state in TAR to handle moving between shard boundaries and (2) with the nano scheduled TAR we end up blocking at the end of each shard while our threads all finish processing.
-- This commit changes the system over to using an ActiveRegionShardBalancers, that combines all of the shard data for a single contig into a single combined shard. This ensures that TAR, and by extensions the HaplotypeCaller, gets all of the data on a single contig together so the the NanoSchedule runs efficiently instead of blocking over and over at shard boundaries. This simple change allows us to scale efficiently to around 8 threads in the nano scheduler:
-- See https://www.dropbox.com/s/k7f280pd2zt0lyh/hc_nano_linear_scale.pdf
-- See https://www.dropbox.com/s/fflpnan802m2906/hc_nano_log_scale.pdf
-- Misc. changes throughout the codebase so we Use the ActiveRegionShardBalancer where appropriate.
-- Added unit tests for ActiveRegionShardBalancer to confirm it does the merging as expected.
-- Fix bad toString in FilePointer
-- Made CountReadsInActiveRegions Nano schedulable, confirming identical results for linear and nano results
-- Made Haplotype NanoScheduled, requiring misc. changes in the map/reduce type so that the map() function returns a List<VariantContext> and reduce actually prints out the results to disk
-- Tests for NanoScheduling
-- CountReadsInActiveRegionsIntegrationTest now does NCT 1, 2, 4 with CountReadsInActiveRegions
-- HaplotypeCallerParallelIntegrationTest does NCT 1,2,4 calling on 100kb of PCR free data
-- Some misc. code cleanup of HaplotypeCaller
-- Analysis scripts to assess performance of nano scheduled HC
-- In order to make the haplotype caller thread safe we needed to use an AtomicInteger for the class-specific static ID counter in SeqVertex and MultiDebrujinVertex, avoiding a race condition where multiple new Vertex() could end up with the same id.
* This version inherits from the original SW implementation so it can use the same matrix creation method.
* A bunch of refactoring was done to the original version to clean it up a bit and to have it do the
right thing for indels at the edges of the alignments.
* Enum added for the overhang strategy to use; added implementation for the INDEL version of this strategy.
* Lots of systematic testing added for this implementation.
* NOT HOOKED UP TO HAPLOTYPE CALLER YET. Committing so that people can play around with this for now.
-Diff engine output is now included in the actual exception message thrown as a
result of an MD5 mismatch, which allows it to be conveniently viewed on the
main page of a build in Bamboo.
Minor Additional Improvements:
-WalkerTestSpec now auto-detects test class name via new JVMUtils.getCallingClass()
method, and the test class name is now included as a regular part of integration
test output for each test.
-Fix race condition in MD5DB.ensureMd5DbDirectory()
-integrationtests dir is now cleaned by "ant clean"
GSA-915 #resolve
Problem
-------
The DeBruijn assembler was too slow. The cause of the slowness was the need to construct many kmer graphs (from max read length in the interval to 11 kmer, in increments of 6 bp). This need to build many kmer graphs was because the assembler (1) needed long kmers to assemble through regions where a shorter kmer was non-unique in the reference, as we couldn't split cycles in the reference (2) shorter kmers were needed to be sensitive to differences from the reference near the edge of reads, which would be lost often when there was chain of kmers of longer length that started before and after the variant.
Solution
--------
The read threading assembler uses a fixed kmer, in this implementation by default two graphs with 10 and 25 kmers. The algorithm operates as follows:
identify all non-unique kmers of size K among all reads and the reference
for each sequence (ref and read):
find a unique starting position of the sequence in the graph by matching to a unique kmer, or starting a new source node if non exist
for each base in the sequence from the starting vertex kmer:
look at the existing outgoing nodes of current vertex V. If the base in sequence matches the suffix of outgoing vertex N, read the sequence to N, and continue
If no matching next vertex exists, find a unique vertex with kmer K. If one exists, merge the sequence into this vertex, and continue
If a merge vertex cannot be found, create a new vertex (note this vertex may have a kmer identical to another in the graph, if it is not unique) and thread the sequence to this vertex, and continue
This algorithm has a key property: it can robustly use a very short kmer without introducing cycles, as we will create paths through the graph through regions that aren't unique w.r.t. the sequence at the given kmer size. This allows us to assemble well with even very short kmers.
This commit includes many critical changes to the haplotype caller to make it fast, sensitive, and accurate on deep and shallow WGS and exomes, the key changes are highlighted below:
-- The ReadThreading assembler keeps track of the maximum edge multiplicity per sample in the graph, so that we prune per sample, not across all samples. This change is essential to operate effectively when there are many deep samples (i.e., 100 exomes)
-- A new pruning algorithm that will only prune linear paths where the maximum edge weight among all edges in the path have < pruningFactor. This makes pruning more robust when you have a long chain of bases that have high multiplicity at the start but only barely make it back into the main path in the graph.
-- We now do a global SmithWaterman to compute the cigar of a Path, instead of the previous bubble-based SmithWaterman optimization. This change is essential for us to get good variants from our paths when the kmer size is small. It also ensures that we produce a cigar from a path that only depends only the sequence of bases in the path, unlike the previous approach which would depend on both the bases and the way the path was decomposed into vertices, which depended on the kmer size we used.
-- Removed MergeHeadlessIncomingSources, which was introducing problems in the graphs in some cases, and just isn't the safest operation. Since we build a kmer graph of size 10, this operation is no longer necessary as it required a perfect match of 10 bp to merge anyway.
-- The old DebruijnAssembler is still available with a command line option
-- The number of paths we take forward from the each assembly graph is now capped at a factor per sample, so that we allow 128 paths for a single sample up to 10 x nSamples as necessary. This is an essential change to make the system work well for large numbers of samples.
-- Add a global mismapping parameter to the HC likelihood calculation: The phredScaledGlobalReadMismappingRate reflects the average global mismapping rate of all reads, regardless of their mapping quality. This term effects the probability that a read originated from the reference haploytype, regardless of its edit distance from the reference, in that the read could have originated from the reference haplotype but from another location in the genome. Suppose a read has many mismatches from the reference, say like 5, but has a very high mapping quality of 60. Without this parameter, the read would contribute 5 * Q30 evidence in favor of its 5 mismatch haplotype compared to reference, potentially enough to make a call off that single read for all of these events. With this parameter set to Q30, though, the maximum evidence against the reference that this (and any) read could contribute against reference is Q30. -- Controllable via a command line argument, defaulting to Q60 rate. Results from 20:10-11 mb for branch are consistent with the previous behavior, but this does help in cases where you have rare very divergent haplotypes
-- Reduced ActiveRegionExtension from 200 bp to 100 bp, which is a performance win and the large extension is largely unnecessary with the short kmers used with the read threading assembler
Infrastructure changes / improvements
-------------------------------------
-- Refactored BaseGraph to take a subclass of BaseEdge, so that we can use a MultiSampleEdge in the ReadThreadingAssembler
-- Refactored DeBruijnAssembler, moving common functionality into LocalAssemblyEngine, which now more directly manages the subclasses, requiring them to only implement a assemble() method that takes ref and reads and provides a List<SeqGraph>, which the LocalAssemblyEngine takes forward to compute haplotypes and other downstream operations. This allows us to have only a limited amount of code that differentiates the Debruijn and ReadThreading assemblers
-- Refactored active region trimming code into ActiveRegionTrimmer class
-- Cleaned up the arguments in HaplotypeCaller, reorganizing them and making arguments @Hidden and @Advanced as appropriate. Renamed several arguments now that the read threading assembler is the default
-- LocalAssemblyEngineUnitTest reads in the reference sequence from b37, and assembles with synthetic reads intervals from 10-11 mbs with only the reference sequence as well as artificial snps, deletions, and insertions.
-- Misc. updates to Smith Waterman code. Added generic interface to called not surpisingly SmithWaterman, making it easier to have alternative implementations.
-- Many many more unit tests throughout the entire assembler, and in random utilities
-- the Queue jobreport PDF script now provides a high-level summary of the de-scattered runtimes of each analysis, so that its easy to see where your script is spending its time across scatters.
Only try to clip adaptors when both reads of the pair are on opposite strands
-- Read pairs that have unusual alignments, such as two reads both oriented like:
<-----
<-----
where previously having their adaptors clipped as though the standard calculation of the insert size was meaningful, which it is not for such oddly oriented pairs. This caused us to clip extra good bases from reads.
-- Update MD5s due change in adaptor clipping, which add some coverage in some places
-- Previous version would trim down 2M2D2M into 2M if you asked for the first 2 bases, but this can result in incorrect alignment of the bases to the reference as the bases no longer span the full reference interval expected. Fixed and added unit tests
Output didn't "mix-up" the genotypes, it outputed the same HET vs HET (e.g.) 3 times rather than the combinations of HET vs {HET, HOM, HOM_REF}, etc.
This was only a problem in the text, _not_ the actual numbers, which were outputted correctly.
- Updated MD5's after looking at diffs to verify that the change is what I expected.
-Changes in Java 7 related to comparators / sorting produce a large number
of innocuous differences in our test output. Updating expectations now
that we've moved to using Java 7 internally.
-Also incorporate Eric's fix to the GATKSAMRecordUnitTest to prevent
intermittent failures.
This class, being unused, was no longer getting packaged into the
GATK release jar by bcel, and so attempting to run its unit test
on the release jar was producing an error.
RR counts are represented as offsets from the first count, but that wasn't being done
correctly when counts are adjusted on the fly. Also, we were triggering the expensive
conversion and writing to binary tags even when we weren't going to write the read
to disk.
The code has been updated so that unconverted counts are passed to the GATKSAMRecord
and it knows how to encode the tag correctly. Also, there are now methods to write
to the reduced counts array without forcing the conversion (and methods that do force
the conversion).
Also:
1. counts are now maintained as ints whenever possible. Only the GATKSAMRecord knows
about the internal encoding.
2. as discussed in meetings today, we updated the encoding so that it can now handle
a range of values that extends to 255 instead of 127 (and is backwards compatible).
3. tests have been moved from SyntheticReadUnitTest to GATKSAMRecordUnitTest accordingly.
-- The previous version of the read clipping operations wouldn't modify the reduced reads counts, so hardClipToRegion would result in a read with, say, 50 bp of sequence and base qualities but 250 bp of reduced read counts. Updated the hardClip operation to handle reduce reads, and added a unit test to make sure this works properly. Also had to update GATKSAMRecord.emptyRead() to set the reduced count to new byte[0] if the template read is a reduced read
-- Update md5s, where the new code recovers a TP variant with count 2 that was missed previously
-Do not throw an exception when parsing snpEff output files
generated by not-officially-supported versions of snpEff,
PROVIDED that snpEff was run with -o gatk
-Requested by the snpEff author
-Relevant integration tests updated/expanded
Note that this works only in the case of pileups (i.e. coming from UG);
allele-biased down-sampling for RR just cannot work for haplotypes.
Added lots of unit tests for new functionality.
-- The previous version was unclipping soft clipped bases, and these were sometimes adaptor sequences. If the two reads successfully merged, we'd lose all of the information necessary to remove the adaptor, producing a very high quality read that matched reference. Updated the code to first clip the adapter sequences from the incoming fragments
-- Update MD5s
-Acquire file locks in a background thread with a timeout of 30 seconds,
and throw a UserException if a lock acquisition call times out
* should solve the locking issue for most people provided they
RETRY failed farm jobs
* since we use NON-BLOCKING lock acquisition calls, any call that
takes longer than a second or two indicates a problem with the
underlying OS file lock support
* use daemon threads so that stuck lock acquisition tasks don't
prevent the JVM from exiting
-Disable both auto-index creation and file locking for integration tests
via a hidden GATK argument --disable_auto_index_creation_and_locking_when_reading_rods
* argument not safe for general use, since it allows reading from
an index file without first acquiring a lock
* this is fine for the test suite, since all index files already
exist for test files (or if they don't, they should!)
-Added missing indices for files in private/testdata
-Had to delete most of RMDTrackBuilderUnitTest, since it mostly tested auto-index
creation, which we can't test with locking disabled, but I replaced the deleted
tests with some tests of my own.
-Unit test for FSLockWithShared to test the timeout feature
1. Using cumulative binomial probability was not working at high coverage sites (because p-values quickly
got out of hand) so instead we use a hybrid system for determining significance: at low coverage sites
use binomial prob and at high coverage sites revert to using the old base proportions. Then we get the
best of both worlds. As a note, coverage refers to just the individual base counts and not the entire pileup.
2. Reads were getting lost because of the comparator being used in the SlidingWindow. When read pairs had
the same alignment end position the 2nd one encountered would get dropped (but added to the header!). We
now use a PriorityQueue instead of a TreeSet to allow for such cases.
3. Each consensus keeps track of its own number of softclipped bases. There was no reason that that number
should be shared between them.
4. We output consensus filtered (i.e. low MQ) reads whenever they are present for now. Don't lose that
information. Maybe we'll decide to change this in the future, but for now we are conservative.
5. Also implemented various small performance optimizations based on profiling.
Added unit tests to cover these changes; systematic assessment now tests against low MQ reads too.
-- Now that this function is used in the core of LIBS it needed some basic optimizations, which are now complete, pass all unit tests.
-- Added caliper benchmark for AlignmentUtils to assess performance (showing new version is 3x-10x faster)
-- Remove unused import in ReadStateManager
* Moved redundant code out of UGEngine
* Added overloaded methods that assume p=0.5 for speed efficiency
* Added unit test for the binomialCumulativeProbability method
The Problem:
Exomes seem to be more prone to base errors and one error in 20x coverage (or below, like most
regions in an exome) causes RR (with default settings) to consider it a variant region. This
seriously hurts compression performance.
The Solution:
1. We now use a probabilistic model for determining whether we can create a consensus (in other
words, whether we can error correct a site) instead of the old ratio threshold. We calculate
the cumulative binomial probability of seeing the given ratio and trigger consensus creation if
that pvalue is lower than the provided threshold (0.01 by default, so rather conservative).
2. We also allow het compression globally, not just at known sites. So if we cannot create a
consensus at a given site then we try to perform het compression; and if we cannot perform het
compression that we just don't reduce the variant region. This way very wonky regions stay
uncompressed, regions with one errorful read get fully compressed, and regions with one errorful
locus get het compressed.
Details:
1. -minvar is now deprecated in favor of -min_pvalue.
2. Added integration test for bad pvalue input.
3. -known argument still works to force het compression only at known sites; if it's not included
then we allow het compression anywhere. Added unit tests for this.
4. This commit includes fixes to het compression problems that were revealed by systematic qual testing.
Before finalizing het compression, we now check for insertions or other variant regions (usually due
to multi-allelics) which can render a region incompressible (and we back out if we find one). We
were checking for excessive softclips before, but now we add these tests too.
5. We now allow het compression on some but not all of the 4 consensus reads: if creating one of the
consensuses is not possible (e.g. because of excessive softclips) then we just back that one consensus
out instead of backing out all of them.
6. We no longer create a mini read at the stop of the variant window for het compression. Instead, we
allow it to be part of the next global consensus.
7. The coverage test is no longer run systematically on all integration tests because the quals test
supercedes it. The systematic quals test is now much stricter in order to catch bugs and edge cases
(very useful!).
8. Each consensus (both the normal and filtered) keep track of their own mapping qualities (before the MQ
for a consensus was affected by good and bad bases/reads).
9. We now completely ignore low quality bases, unless they are the only bases present in a pileup.
This way we preserve the span of reads across a region (needed for assembly). Min base qual moved to Q15.
10.Fixed long-standing bug where sliding window didn't do the right thing when removing reads that start
with insertions from a header.
Note that this commit must come serially before the next commit in which I am refactoring the binomial prob
code in MathUtils (which is failing and slow).
-- The previous algorithm would compute the likelihood of each haplotype pooled across samples. This has a tendency to select "consensus" haplotypes that are reasonably good across all samples, while missing the true haplotypes that each sample likes. The new algorithm computes instead the most likely pair of haplotypes among all haplotypes for each sample independently, contributing 1 vote to each haplotype it selects. After all N samples have been run, we sort the haplotypes by their counts, and take 2 * nSample + 1 haplotypes or maxHaplotypesInPopulation, whichever is smaller.
-- After discussing with Mauricio our view is that the algorithmic complexity of this approach is no worse than the previous approach, so it should be equivalently fast.
-- One potential improvement is to use not hard counts for the haplotypes, but this would radically complicate the current algorithm so it wasn't selected.
-- For an example of a specific problem caused by this, see https://jira.broadinstitute.org/browse/GSA-871.
-- Remove old pooled likelihood model. It's worse than the current version in both single and multiple samples:
1000G EUR samples:
10Kb
per sample: 7.17 minutes
pooled: 7.36 minutes
Name VariantType TRUE_POSITIVE FALSE_POSITIVE FALSE_NEGATIVE TRUE_NEGATIVE CALLED_NOT_IN_DB_AT_ALL
per_sample SNPS 50 0 5 8 1
per_sample INDELS 6 0 7 2 1
pooled SNPS 49 0 6 8 1
pooled INDELS 5 0 8 2 1
100 kb
per sample: 140.00 minutes
pooled: 145.27 minutes
Name VariantType TRUE_POSITIVE FALSE_POSITIVE FALSE_NEGATIVE TRUE_NEGATIVE CALLED_NOT_IN_DB_AT_ALL
per_sample SNPS 144 0 22 28 1
per_sample INDELS 28 1 16 9 11
pooled SNPS 143 0 23 28 1
pooled INDELS 27 1 17 9 11
java -Xmx2g -jar dist/GenomeAnalysisTK.jar -T HaplotypeCaller -I private/testdata/AFR.structural.indels.bam -L 20:8187565-8187800 -L 20:18670537-18670730 -R ~/Desktop/broadLocal/localData/human_g1k_v37.fasta -o /dev/null -debug
haplotypes from samples: 8 seconds
haplotypes from pools: 8 seconds
java -Xmx2g -jar dist/GenomeAnalysisTK.jar -T HaplotypeCaller -I /Users/depristo/Desktop/broadLocal/localData/phaseIII.4x.100kb.bam -L 20:10,000,000-10,001,000 -R ~/Desktop/broadLocal/localData/human_g1k_v37.fasta -o /dev/null -debug
haplotypes from samples: 173.32 seconds
haplotypes from pools: 167.12 seconds
-- Add pair cleaning feature. Reads in query-name sorted order are required and pairs need to appear consecutively, but if -cleanPairs option is set, a malformed pair where second read is missing is just skipped instead of erroring out.
-- Add integration tests
-- Move walker to public
The Problem
----------
Some read x haplotype pairs were getting very low likelihood when caching is on. Turning it off seemed to give the right result.
Solution
--------
The HaplotypeCaller only initializes the PairHMM once and then feed it with a set of reads and haplotypes. The PairHMM always caches the matrix when the previous haplotype length is the same as the current one. This is not true when the read has changed. This commit adds another condition to zero the haplotype start index when the read changes.
Summarized Changes
------------------
* Added the recacheReadValue check to flush the matrix (hapStartIndex = 0)
* Updated related MD5's
Bamboo link: http://gsabamboo.broadinstitute.org/browse/GSAUNSTABLE-PARALLEL9
Key improvement
---------------
-- The haplotype caller was producing unstable calls when comparing the following two haplotypes:
ref: ACAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA
alt: TGTGTGTGTGTGTGACAGAGAGAGAGAGAGAGAGAGAGAGAGAGA
in which the alt and ref haplotypes differ in having indel at both the start and end of the bubble. The previous parameter values used in the Path algorithm were set so that such haplotype comparisons would result in the either the above alignment or the following alignment depending on exactly how many GA units were present in the bubble.
ref: ACAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA
alt: TGTGTGTGTGTGTGACAGAGAGAGAGAGAGAGAGAGAGAGAGAGA
The number of elements could vary depending on how the graph was built, and resulted in real differences in the calls between BWA mem and BWA-SW calls. I added a few unit tests for this case, and found a set of SW parameter values with lower gap-extension penalties that significantly favor the first alignment, which is the right thing to do, as we really don't mind large indels in the haplotypes relative to having lots of mismatches.
-- Expanded the unit tests in both SW and KBestPaths to look at complex events like this, and to check as well somewhat sysmatically that we are finding many types of expected mutational events.
-- Verified that this change doesn't alter our calls on 20:10,000,000-11,000,000 at all
General code cleanup
--------------------
-- Move Smith-Waterman to its own package in utils
-- Refactored out SWParameters class in SWPairwiseAlignment, and made constructors take either a named parameter set or a Parameter object directly. Depreciated old call to inline constants. This makes it easier to group all of the SW parameters into a single object for callers
-- Update users of SW code to use new Parameter class
-- Also moved haplotype bam writers to protected so they can use the Path SW parameter, which is protected
-- Removed the storage of the SW scoring matrix in SWPairwiseAligner by default. Only the SWPairwiseAlignmentMain test program needs this, so added a gross protected static variable that enables its storage
-- Ensure that BQSR works properly for an Ion Torrent BAM. (Added integration test and bam)
-- Improve the error message when a unknown platform is found (integration test added)
-- When the alignments are sufficiently apart from each other all the scores in the sw matrix could be negative which screwed up the max score calculation since it started at zero.
Problem:
--------
Print Reads was running out of disk space when using the -BQSR option even for small bam files
Solution:
---------
Configure setupWriter to expect pre sorted reads
-- Add a maximum per sample and overall maximum number of reads held in memory by the ART at any one time. Does this in a new TAROrderedReadCache data structure that uses a reservior downsampler to limit the total number of reads to a constant amount. This constant is set to be by default 3000 reads * nSamples to a global maximum of 1M reads, all controlled via the ActiveRegionTraversalParameters annotation.
-- Added an integration test and associated excessively covered BAM excessiveCoverage.1.121484835.bam (private/testdata) that checks that the system is operating correctly.
-- #resolves GSA-921
-- This method provides client with the current number of elements, without having to retreive the underlying list<T>. Added unit tests for LevelingDownsampler and ReservoirDownsampler as these are the only two complex ones. All of the others are trivially obviously correct.
-- The function getReducedCounts() was returning the undecoded reduced read tag, which looks like [10, 5, -1, -5] when the depths were [10, 15, 9, 5]. The only function that actually gave the real counts was getReducedCount(int i) which did the proper decoding. Now GATKSAMRecord decodes the tag into the proper depths vector so that getReduceCounts() returns what one reasonably expects it to, and getReduceCount(i) merely looks up the value at i. Added unit test to ensure this behavior going forward.
-- Changed the name of setReducedCounts() to setReducedCountsTag as this function assumes that counts have already been encoded in the tag way.
-- The previous likelihood calculation proceeds as normal, but after each read has been evaluated against each haplotype we go through the read / allele / likelihoods map and eliminate all reads that have poor fit to any of the haplotypes. This functionality stops us from making a particular type of error in the HC, where we have a haplotype that's very far from the reference allele but not the right true haplotype. All of the reads that are slightly closer to this FP haplotype than the reference previously generated enormous likelihoods in favor of this FP haplotype because they were closer to it than the reference, even if each read had many mismatches w.r.t. the FP haplotype (and so the FP haplotype was a bad model for the true underlying haplotype).
-- Trims down active regions and associated reads and haplotypes to a smaller interval based on the events actually in the haplotypes within the original active region (without extension). Radically speeds up calculations when using large active region extensions. The ActiveRegion.trim algorithm does the best job it can of trimming an active region down to a requested interval while ensuring the resulting active region has a region (and extension) no bigger than the original while spanning as much of the requested extend as possible. The trimming results in an active region that is a subset of the previous active region based on the position and types of variants found among the haplotypes
-- Retire error corrector, archive old code and repurpose subsystem into a general kmer counter. The previous error corrector was just broken (conceptually) and was disabled by default in the engine. Now turning on error correction throws a UserException. Old part of the error corrector that counts kmers was extracted and put into KMerCounter.java
-- Add final simplify graph call after we prune away the non-reference paths in DeBruijnAssembler
-- These events always occur on the very edge of the haplotypes, and are intrinsically dodgy. So instead of emitting them and then potentially having to deal with merging real basepair events into them we just no longer emit those events.
-- Moved R^2 LD haplotype merging system to the utils.haplotype package
-- New LD merging only enabled with HC argument.
-- EventExtractor and EventExtractorUnitTest refactors so we can test the block substitution code without having to enabled it via a static variable
-- A few misc. bug fixes in LDMerger itself
-- Refactoring of Haplotype event splitting and merging code
-- Renamed EventExtractor to EventMap
-- EventMap has a static method that computes the event maps among n haplotypes
-- Refactor Haplotype score and base comparators into their own classes and unit tested them
-- Refactored R^2 based LD merging code into its own class HaplotypeR2Calculator and unit tested much of it.
-- LDMerger now uses the HaplotypeR2Calculator, which cleans up the code a bunch and allowed me to easily test that code with a MockHaplotypeR2Calculator. For those who haven't seen this testing idiom, have a look, and very useful
-- New algorithm uses a likelihood-ratio test to compute the probability that only the phased haplotypes exist in the population.
-- Fixed fundamental bug in the way the previous R^2 implementation worked
-- Optimizations for HaplotypeLDCalculator: only compute the per sample per haplotype summed likelihoods once, regardless of how many calls there are
-- Previous version would enter infinite loop if it merged two events but the second event had other low likelihood events in other haplotypes that didn't get removed. Now when events are removed they are removed from all event maps, regardless of whether the haplotypes carry both events
-- Bugfixes for EventMap in the HaplotypeCaller as well. Previous version was overly restrictive, requiring that the first event to make into a block substitution was a snp. In some cases we need to merge an insertion with a deletion, such as when the cigar is 10M2I3D4M. The new code supports this. UnitTested and documented as well. LDMerger handles case where merging two alleles results in a no-op event. Merging CA/C + A/AA -> CAA/CAA -> no op. Handles this case by removing the two events. UnitTested
-- Turn off debugging output for the LDMerger in the HaplotypeCaller unless -debug was enabled
-- This new version does a much more specific test (that's actually right). Here's the new algorithm:
* Compute probability that two variants are in phase with each other and that no
* compound hets exist in the population.
*
* Implemented as a likelihood ratio test of the hypothesis:
*
* x11 and x22 are the only haplotypes in the populations
*
* vs.
*
* all four haplotype combinations (x11, x12, x21, and x22) all exist in the population.
*
* Now, since we have to have both variants in the population, we exclude the x11 & x11 state. So the
* p of having just x11 and x22 is P(x11 & x22) + p(x22 & x22).
*
* Alternatively, we might have any configuration that gives us both 1 and 2 alts, which are:
*
* - P(x11 & x12 & x21) -- we have hom-ref and both hets
* - P(x22 & x12 & x21) -- we have hom-alt and both hets
* - P(x22 & x12) -- one haplotype is 22 and the other is het 12
* - P(x22 & x21) -- one haplotype is 22 and the other is het 21
-- This fixes edge base bugs where non-consolidated cigars are causing problems in users of the Haplotype object. Input arguments are now checks (let's see if we blow up)
-- Picard extension so Queue scripts can use FastqToSam
-- Single-sample BAM processing: merge/trim reads + BWA + IR + MD + BQSR. Mostly identical to standard pipeline,
except for the adaptor trimming/merging which is critical for short-insert libraries.
-- Single-sample calling (experimental, work in progress): standard UG run but outputting at all sites, meant for
deep whole genomes.
New scripts
Problem:
--------
PairHMM was generating positive likelihoods (even after the re-work of the model)
Solution:
---------
The caching idices were never re-initializing the initial conditions in the first position of the deletion matrix. Also the match matrix was being wrongly initialized (there is not necessarily a match in the first position). This commit fixes both issues on both the Logless and the Log10 versions of the PairHMM.
Summarized Changes:
------------------
* Redesign the matrices to have only 1 col/row of padding instead of 2.
* PairHMM class now owns the caching of the haplotype (keeps track of last haplotypes, and decides where the caching should start)
* Initial condition (in the deletionMatrix) is now updated every time the haplotypes differ in length (this was wrong in the previous version)
* Adjust the prior and probability matrices to be one based (logless)
* Update Log10PairHMM to work with prior and probability matrices as well
* Move prior and probability matrices to parent class
* Move and rename padded lengths to parent class to simplify interface and prevent off by one errors in new implementations
* Simple cleanup of PairHMMUnitTest class for a little speedup
* Updated HC and UG integration test MD5's because of the new initialization (without enforcing match on first base).
* Create static indices for the transition probabilities (for better readability)
[fixes#47399227]
* As reported here: http://gatkforums.broadinstitute.org/discussion/comment/4270#Comment_4270
* This was a commit into the variant.jar; the changes here are a rev of that jar and handling of errors in VF
* Added integration test to confirm failure with User Error
* Removed illegal header line in KB test VCF that was causing related tests to fail.
* Very trivial, but I happened to see this code and it drove me nuts so I felt compelled to refactor it.
* Instead of iterating over keys in map to get the values, just iterate over the values...
-- When consecutive intervals were within the bandpass filter size the ActiveRegion traversal engine would create
duplicate active regions.
-- Now when flushing the activity profile after we jump to a new interval we remove the extra states which are outside
of the current interval.
-- Added integration test which ensures that the output VCF contains no duplicate records. Was failing test before this commit.
-A UserException is now thrown if either the fai or dict file for the
reference does not exist, with pointers to instructions for creating
these files.
-Gets rid of problematic file locking that was causing intermittent
errors on our farm.
-Integration tests to verify that correct exceptions are thrown in
the case of a missing fai / dict file.
GSA-866 #resolve
-The algorithm for finding the intersection of two sets of intervals
relies on the sortedness of the intervals within each set, but the engine
was not sorting the intervals before attempting to find the intersection.
-The result was that if one or both interval lists was unsorted / lexicographically
sorted, we would often fail to find the intersection correctly.
-Now the IntervalBinding sorts all sets of intervals before returning them,
solving the problem.
-Added an integration test for this case.
GSA-909 #resolve
-- Sometimes it's desireable to specify a set of "good" regions and filter out other stuff (like say an alignability mask or a "good regions" mask). But by default, the -mask argument in VF will only filter sites inside a particular mask. New argument -filterNotInMask will reverse default logic and filter outside of a given mask.
-- Added integration test, and made sure we also test with a BED rod.
The Problem:
------------
the SAM spec does not allow multiple @PG tags with the same id. Our @PG tag writing routines were allowing that to happen with the boolean parameter "keep_all_pg_records".
How this fixes it:
------------------
This commit removes that option from all the utility functions and cleans up the code around the classes that used these methods off-spec.
Summarized changes:
-------------------
* Remove keep_all_pg_records option from setupWriter utility methos in Util
* Update all walkers to now replace the last @PG tag of the same walker (if it already exists)
* Cleanup NWaySamFileWriter now that it doesn't need to keep track of the keep_all_pg_records variable
* Simplify the multiple implementations to setupWriter
Bamboo:
-------
http://gsabamboo.broadinstitute.org/browse/GSAUNSTABLE-PARALLEL31
Issue Tracker:
--------------
[fixes 47100885]
-- Corrected logic to pick biallelic vc to left align.
-- Added integration test to make sure this feature is tested and feature to trim bases is also tested.
The current implementation of the PairHMM had issues with the probabilities and the state machines. Probabilities were not adding up to one because:
# Initial conditions were not being set properly
# Emission probabilities in the last row were not adding up to 1
The following commit fixes both by
# averaging all potential start locations (giving an equal prior to the state machine in it's first iteration -- allowing the read to start it's alignment anywhere in the haplotype with equal probability)
# discounting all paths that end in deletions by not adding the last row of the deletion matrix and summing over all paths ending in matches and insertions (this saves us from a fourth matrix to represent the end state)
Summarized changes:
* Fix LoglessCachingPairHMM and Log10PairHMM according to the new algorithm
* Refactor probabilities check to throw exception if we ever encounter probabilities greater than 1.
* Rename LoglessCachingPairHMM to LoglessPairHMM (this is the default implementation in the HC now)
* Rename matrices to matchMatrix, insertionMatrix and deletionMatrix for clarity
* Rename metric lengths to read and haplotype lengths for clarity
* Rename private methods to initializePriors (distance) and initializeProbabilities (constants) for clarity
* Eliminate first row constants (because they're not used anyway!) and directly assign initial conditions in the deletionMatrix
* Remove unnecessary parameters from updateCell()
* Fix the expected probabilities coming from the exact model in PairHMMUnitTest
* Neatify PairHMM class (removed unused methods) and PairHMMUnitTest (removed unused variables)
* Update MD5s: Probabilities have changed according to the new PairHMM model and as expected HC and UG integration tests have new MD5s.
[fix 47164949]
-- Added ability to trim common bases in front of indels before left-aligning. Otherwise, records may not be left-aligned if they have common bases, as they will be mistaken by complext records.
-- Added ability to split multiallelic records and then left align them, otherwise we miss a lot of good left-aligneable indels.
-- Motivated by this, renamed walker to LeftAlignAndTrimVariants.
-- Code refactoring, cleanup and bring up to latest coding standards.
-- Added unit testing to make sure left alignment is performed correctly for all offsets.
-- Changed phase 3 HC script to new syntax. Add command line options, more memory and reduce alt alleles because jobs keep crashing.
Currently, the multi-allelic test is covering the following case:
Eval A T,C
Comp A C
reciprocate this so that the reverse can be covered.
Eval A C
Comp A T,C
And furthermore, modify ConcordanceMetrics to more properly handle the situation where multiple alternate alleles are available in the comp. It was possible for an eval C/C sample to match a comp T/T sample, so long as the C allele were also present in at least one other comp sample.
This comes from the fact that "truth" reference alleles can be paired with *any* allele also present in the truth VCF, while truth het/hom var sites are restricted to having to match only the alleles present in the genotype. The reason that truth ref alleles are special case is as follows, imagine:
Eval: A G,T 0/0 2/0 2/2 1/1
Comp: A C,T 0/0 1/0 0/0 0/0
Even though the alt allele of the comp is a C, the assessment of genotypes should be as follows:
Sample1: ref called ref
Sample2: alleles don't match (the alt allele of the comp was not assessed in eval)
Sample3: ref called hom-var
Sample4: alleles don't match (the alt allele of the eval was not assessed in comp)
Before this change, Sample2 was evaluated as "het called het" (as the T allele in eval happens to also be in the comp record, just not in the comp sample). Thus: apply current
logic to comp hom-refs, and the more restrictive logic ("you have to match an allele in the comp genotype") when the comp is not reference.
Also in this commit,major refactoring and testing for MathUtils. A large number of methods were not used at all in the codebase, these methods were removed:
- dotProduct(several types). logDotProduct is used extensively, but not the real-space version.
- vectorSum
- array shuffle, random subset
- countOccurances (general forms, the char form is used in the codebase)
- getNMaxElements
- array permutation
- sorted array permutation
- compare floats
- sum() (for integer arrays and lists).
Final keyword was extensively added to MathUtils.
The ratio() and percentage() methods were revised to error out with non-positive denominators, except in the case of 0/0 (which returns 0.0 (ratio), or 0.0% (percentage)). Random sampling code was updated to make use of the cleaner implementations of generating permutations in MathUtils (allowing the array permutation code to be retired).
The PaperGenotyper still made use of one of these array methods, since it was the only walker it was migrated into the genotyper itself.
In addition, more extensive tests were added for
- logBinomialCoefficient (Newton's identity should always hold)
- logFactorial
- log10sumlog10 and its approximation
All unit tests pass
-- This new functionality allows the client to make decisions about how to handle non-informative reads, rather than having a single enforced constant that isn't really appropriate for all users. The previous functionality is maintained now and used by all of the updated pieces of code, except the BAM writers, which now emit reads to display to their best allele, regardless of whether this is particularly informative or not. That way you can see all of your data realigned to the new HC structure, rather than just those that are specifically informative.
-- This all makes me concerned that the informative thresholding isn't appropriately used in the annotations themselves. There are many cases where nearby variation makes specific reads non-informative about one event, due to not being informative about the second. For example, suppose you have two SNPs A/B and C/D that are in the same active region but separated by more than the read length of the reads. All reads would be non-informative as no read provides information about the full combination of 4 haplotypes, as they reads only span a single event. In this case our annotations will all fall apart, returning their default values. Added a JIRA to address this (should be discussed in group meeting)
* It is now cleaner and easier to test; added tests for newly implemented methods.
* Many fixes to the logic to make it work
* The most important change was that after triggering het compression we actually need to back it out if it
creates reads that incorporated too many softclips at any one position (because they get unclipped).
* There was also an off-by-one error in the general code that only manifested itself with het compression.
* Removed support for creating a het consensus around deletions (which was broken anyways).
* Mauricio gave his blessing for this.
* Het compression now works only against known sites (with -known argument).
* The user can pass in one or more VCFs with known SNPs (other variants are ignored).
* If no known SNPs are provided het compression will automatically be disabled.
* Added SAM tag to stranded (i.e. het compressed) reduced reads to distinguish their
strandedness from normal reduced reads.
* GATKSAMRecord now checks for this tag when determining whether or not the read is stranded.
* This allows us to update the FisherStrand annotation to count het compressed reduced reads
towards the FS calculation.
* [It would have been nice to mark the normal reads as unstranded but then we wouldn't be
backwards compatible.]
* Updated integration tests accordingly with new het compressed bams (both for RR and UG).
* In the process of fixing the FS annotation I noticed that SpanningDeletions wasn't handling
RR properly, so I fixed it too.
* Also, the test in the UG engine for determining whether there are too many overlapping
deletions is updated to handle RR.
* I added a special hook in the RR integration tests to additionally run the systematic
coverage checking tool I wrote earlier.
* AssessReducedCoverage is now run against all RR integration tests to ensure coverage is
not lost from original to reduced bam.
* This helped uncover a huge bug in the MultiSampleCompressor where it would drop reads
from all but 1 sample (now fixed).
* AssessReducedCoverage moved from private to protected for packaging reasons.
* #resolve GSA-639
At this point, this commit encompasses most of what is needed for het compression to go live.
There are still a few TODO items that I want to get in before the 2.5 release, but I will save
those for a separate branch because as it is I feel bad for the person who needs to review all
these changes (sorry, Mauricio).
-- added calls to representativeCount() of the pileup instead of using ++
-- renamed CallableLoci integration test
-- added integration test for reduce read support on callable loci
-- Previously we tried to include lots of these low mapping quality reads in the assembly and calling, but we effectively were just filtering them out anyway while generating an enormous amount of computational expense to handle them, as well as much larger memory requirements. The new version simply uses a read filter to remove them upfront. This causes no major problems -- at least, none that don't have other underlying causes -- compared to 10-11mb of the KB
-- Update MD5s to reflect changes due to no longer including mmq < 20 by default
-- DeBruijnAssemblerUnitTest and AlignmentUtilsUnitTest were both in DEBUG = true mode (bad!)
-- Remove the maxHaplotypesToConsider feature of HC as it's not useful
-- DeBruijnAssembler functions are no longer static. This isn't the right way to unit test your code
-- An a HaplotypeCaller command line option to use low-quality bases in the assembly
-- Refactored DeBruijnGraph and associated libraries into base class
-- Refactored out BaseEdge, BaseGraph, and BaseVertex from DeBruijn equivalents. These DeBruijn versions now inherit from these base classes. Added some reasonable unit tests for the base and Debruijn edges and vertex classes.
-- SeqVertex: allows multiple vertices in the sequence graph to have the same sequence and yet be distinct
-- Further refactoring of DeBruijnAssembler in preparation for the full SeqGraph <-> DeBruijnGraph split
-- Moved generic methods in DeBruijnAssembler into BaseGraph
-- Created a simple SeqGraph that contains SeqVertex objects
-- Simple chain zipper for SeqGraph that reproduces the results for the mergeNode function on DeBruijnGraphs
-- A working version of the diamond remodeling algorithm in SeqGraph that converts graphs that look like A -> Xa, A -> Ya, Xa -> Z, Ya -> Z into A -> X -> a, A -Y -> a, a -> Z
-- Allow SeqGraph zip merging of vertices where the in vertex has multiple incoming edges or the out vertex has multiple outgoing edges
-- Fix all unit tests so they work with the new SeqGraph system. All tests passed without modification.
-- Debugging makes it easier to tell which kmer graph contributes to a haplotype
-- Better docs and unit tests for BaseVertex, SeqVertex, BaseEdge, and KMerErrorCorrector
-- Remove unnecessary printing of cleaning info in BaseGraph
-- Turn off kmer graph creation in DeBruijnAssembler.java
-- Only print SeqGraphs when debugGraphTransformations is set to true
-- Rename DeBruijnGraphUnitTest to SeqGraphUnitTest. Now builds DeBruijnGraph, converts to SeqGraph, uses SeqGraph.mergenodes and tests for equality.
-- Update KBestPathsUnitTest to use SeqGraphs not DebruijnGraphs
-- DebruijnVertex now longer takes kmer argument -- it's implicit that the kmer length is the sequence.length now
-- Added a -dontGenotype mode for testing assembly efficiency
-- However, it looks like this has a very negative impact on the quality of the results, so the code should be deleted
--Based on existing code in GenomeAnalysisEngine
--Hashmaps hold mapping of deprecated tool name to version number and recommended replacement (if any)
--Using FastUtils for maps; specifically Object2ObjectMap but there could be a better type for Strings...
--Added user exception for deprecated annotations
--Added deprecation check to AnnotationInterfaceManager.validateAnnotations
--Run when annotations are initialized
--Made annotation sets instead of lists
--Refactored listAnnotations basic method out of VA into HelpUtils
--HelpUtils.listAnnotations() is now called by both VA and the new ListAnnotations utility (lives in sting.tools)
--This way we keep the VA --list option but we also offer a way to list annotations without a full valid VA command-line, which was a pain users continually complained about
--We could get rid of the VA --list option altogether ...?
--Mostly doc block tweaks
--Added @DocumentedGATKFeature to some walkers that were undocumented because they were ending up in "uncategorized". Very important for GSA: if a walker is in public or protected, it HAS to be properly tagged-in. If it's not ready for the public, it should be in private.
-- @Output isn't required for AssessNA12878
-- Previous version would could non-variant sites in NA12878 that resulted from subsetting a multi-sample VC to NA12878 as CALLED_BUT_NOT_IN_DB sites. Now they are properly skipped
-- Bugfix for subsetting samples to NA12878. Previous version wouldn't trim the alleles when subsetting down a multi-sample VCF, so we'd have false FN/FP sites at indels when the multi-sample VCF has alleles that result in the subset for NA12878 having non-trimmed alleles. Fixed and unit tested now.
-- Simply caps PairHMM likelihoods from rising above 0 by taking the min of the likelihood and 0. Will be properly fixed in GATK 2.5 with better PairHMM implementation.
Increase one timeout, restore others that were only timing out due to the
Java crypto lib bug to their original values.
-DOUBLE timeout for NanoSchedulerUnitTest.testNanoSchedulerInLoop()
-REDUCE timeout for EngineFeaturesIntegrationTest to its original value
-REDUCE timeout for MaxRuntimeIntegrationTest to its original value
-REDUCE timeout for GATKRunReportUnitTest to its original value
- Moved AverageAltAlleleLength, MappingQualityZeroFraction and TechnologyComposition to Private
- VariantType, TransmissionDisequilibriumTest, MVLikelihoodRatio and GCContent are no longer Experimental
- AlleleBalanceBySample, HardyWeinberg and HomopolymerRun are Experimental and available to users with a big bold caveat message
- Refactored getMeanAltAlleleLength() out of AverageAltAlleleLength into GATKVariantContextUtils in order to make QualByDepth independent of where AverageAltAlleleLength lives
- Unrelated change, bundled in for convenience: made HC argument includeUnmappedreads @Hidden
- Removed unnecessary check in AverageAltAlleleLength
ALL GATK DEVELOPERS PLEASE READ NOTES BELOW:
I have updated the @Output annotation to behave differently and to include a 'defaultToStdout' tag.
* The 'defaultToStdout' tags lets walkers specify whether to default to stdout if -o is not provided.
* The logic for @Output is now:
* if required==true then -o MUST be provided or a User Error is generated.
* if required==false and defaultToStdout==true then the output is assigned to stdout if no -o is provided.
* this is the default behavior (i.e. @Output with no modifiers).
* if required==false and defaultToStdout==false then the output object is null.
* use this combination for truly optional outputs (e.g. the -badSites option in AssessNA12878).
* I have updated walkers so that previous behavior has been maintained (as best I could).
* In general, all @Outputs with default long/short names have required=false.
* Walkers with nWayOut options must have required==false and defaultToStdout==false (I added checks for this)
* I added unit tests for @Output changes with David's help (thanks!).
* #resolve GSA-837
* ClippingOp updated to incorporate Ns in the hard clips.
* ReadUtils.getReadCoordinateForReferenceCoordinate() updated to account for Ns.
* Added test that covers the BQSR case we saw.
* Created GSA-856 (for Mauricio) to add lots of tests to ReadUtils.
* It will require refactoring code and not in the scope of what I was willing to do to fix this.
-- Strandless GATK reads are ones where they don't really have a meaningful strand value, such as Reduced Reads or fragment merged reads. Added GATKSAMRecord support for such reads, along with unit tests
-- The merge overlapping fragments code in FragmentUtils now produces strandless merged fragments
-- FisherStrand annotation generalized to treat strandless as providing 1/2 the representative count for both strands. This means that that merged fragments are properly handled from the HC, so we don't hallucinate fake strand-bias just because we managed to merge a lot of reads together.
-- The previous getReducedCount() wouldn't work if a read was made into a reduced read after getReducedCount() had been called. Added new GATKSAMRecord method setReducedCounts() that does the right thing. Updated SlidingWindow and SyntheticRead to explicitly call this function, and so the readTag parameter is now gone.
-- Update MD5s for change to FS calculation. Differences are just minor updates to the FS
-- Code was undocumented, big, and not well tested. All three things fixed.
-- Currently not passing, but the framework works well for testing
-- Added concat(byte[] ... arrays) to utils
-Allow the default S3 put timeout of 30 seconds for GATKRunReports
to be overridden via a constructor argument, and use a timeout
of 300 seconds for tests. The timeout remains 30 seconds in all
other cases.
-Change integration tests that themselves dispatch farm jobs
into pipeline tests. Necessary because some farm nodes are
not set up as submit hosts. Pipeline tests are still run
directly on gsa4.
-Bump up the timeout for the MaxRuntimeIntegrationTest even more
(was still occasionally failing on the farm!)
GATK-73 updated docs for bqsr args
GATK-9 differentiate CountRODs from CountRODsByRef
GATK-76 generate GATKDoc for CatVariants
GATK-4 made resource arg required
GATK-10 added -o, some docs to CountMales; some docs to CountLoci
GATK-11 fixed by MC's -o change; straightened out the docs.
GATK-77 fixed references to wiki
GATK-76 Added Ami's doc block
GATK-14 Added note that these annotations can only be used with VariantAnnotator
GATK-15 specified required=false for two arguments
GATK-23 Added documentation block
GATK-33 Added documentation
GATK-34 Added documentation
GATK-32 Corrected arg name and docstring in DiffObjects
GATK-32 Added note to DO doc about reference (required but unused)
GATK-29 Added doc block to CountIntervals
GATK-31 Added @Output PrintStream to enable -o
GATK-35 Touched up docs
GATK-36 Touched up docs, specified verbosity is optional
GATK-60 Corrected GContent annot module location in gatkdocs
GATK-68 touched up docs and arg docstrings
GATK-16 Added note of caution about calling RODRequiringAnnotations as a group
GATK-61 Added run requirements (num samples, min genotype quality)
Tweaked template and generic doc block formatting (h2 to h3 titles)
GATK-62 Added a caveat to HR annot
Made experimental annotation hidden
GATK-75 Added setup info regarding BWA
GATK-22 Clarified some argument requirements
GATK-48 Clarified -G doc comments
GATK-67 Added arg requirement
GATK-58 Added annotation and usage docs
GSATDG-96 Corrected doc
Updated MD5 for DiffObjectsIntegrationTests (only change is link in table title)
Ancient DNA sequencing data is in many ways different from modern data, and methods to analyze it need to be adapted accordingly.
Feature 1: Read adaptor trimming. Ancient DNA libraries typically have very short inserts (in the order of 50 bp), so typical Illumina libraries sequenced in, say, 100bp HiSeq will have a large adaptor component being read after the insert.
If this adaptor is not removed, data will not be aligneable. There are third party tools that remove adaptor and potentially merge read pairs, but are cumbersome to use and require precise knowledge of the library construction and adaptor sequence.
-- New walker ReadAdaptorTrimmer walks through paired end data, computes pair overlap and trims auto-detected adaptor sequence.
-- Unit tests added for trimming operation.
-- Utility walker (may be retired later) DetailedReadLengthDistribution computes insert size or read length distribution stratified by read group and mapping status and outputs a GATKReport with data.
-- Renamed MaxReadLengthFilter to ReadLengthFilter and added ability to specify minimum read length as a filter (may be useful if, as a consequence of adaptor trimming, we're left with a lot of very short reads which will map poorly and will just clutter output BAMs).
Feature 2: Unbiased site QUAL estimation: many times ancestral allele status is not known and VCF fields like QUAL, QD, GQ, etc. are affected by the pop. gen. prior at a site. This might introduce subtle biases in studies where a species is aligned against the reference of another species, so an option for UG and HC not to apply such prior is introduced.
-- Added -noPrior argument to StandardCallerArgumentCollection.
-- Added option not to fill priors is such argument is set.
-- Added an integration test.
- This was needed since samples with spaces in their names are regularly found in the picard pipeline.
- Modified the tests to account for this (removed spaces from the good tests, and changed the failing tests accordingly)
- Cleaned up the unit tests using a @DataProvider (I'm in love...).
- Moved AlleleBiasedDownsamplingUtilsUnitTest to public to match location of class it is testing (due to the way bamboo operates)
-Make MaxRuntimeIntegrationTest more lenient by assuming that startup overhead
might be as long as 120 seconds on a very slow node, rather than the original
assumption of 20 seconds
-In TraverseActiveRegionsUnitTest, write temp bam file to the temp directory, not
to the current working directory
-SimpleTimerUnitTest: This test was internally inconsistent. It asserted that
a particular operation should take no more than 10 milliseconds, and then asserted
again that this same operation should take no more than 100 microseconds (= 0.1 millisecond).
On a slow node it could take slightly longer than 100 microseconds, however.
Changed the test to assert that the operation should require no more than 10000 microseconds
(= 10 milliseconds)
-change global default test timeout from 20 to 40 minutes (things just take longer
on the farm!)
-build.xml: allow runtestonly target to work with scala test classes
* ReadTransformers can say they must be first, must be last, or don't care.
* By default, none of the existing ones care about ordering except BQSR (must be first).
* This addresses a bug reported on the forum where BAQ is incorrectly applied before BQSR.
* The engine now orders the read transformers up front before applying iterators.
* The engine checks for enabled RTs that are not compatible (e.g. both must be first) and blows up (gracefully).
* Added unit tests.
By default all output is assigned to stdout if a -o is not provided. Technically this makes @Output a not required parameter, and the documentation is misleading because it's reading from the annotation.
GSA-820 #resolve
Too many users (with RNASeq reads) are hitting these exceptions that were never supposed to happen. Let's give them (and us) a better and clearer error message.
-- The new code includes a new mode to write out a BAM containing reads realigned to the called haplotypes from the HC, which can be easily visualized in IGV.
-- Previous functionality maintained, with bug fixes
-- Haplotype BAM writing code now lives in utils
-- Created a base class that includes most of the functionality of writing reads realigned to haplotypes onto haplotypes.
-- Created two subclasses, one that writes all haplotypes (previous functionality) and a CalledHaplotypeBAMWriter that will only write reads aligned to the actually called haplotypes
-- Extended PerReadAlleleLikelihoodMap.getMostLikelyAllele to optionally restrict set of alleles to consider best
-- Massive increase in unit tests in AlignmentUtils, along with several new powerful functions for manipulating cigars
-- Fix bug in SWPairwiseAlignment that produces cigar elements with 0 size, and are now fixed with consolidateCigar in AlignmentUtils
-- HaplotypeCaller now tracks the called haplotypes in the GenotypingEngine, and returns this information to the HC for use in visualization.
-- Added extensive docs to HaplotypeCaller on how to use this capability
-- BUGFIX -- don't modify the read bases in GATKSAMRecord in LikelihoodCalculationEngine in the HC
-- Cleaned up SWPairwiseAlignment. Refactored out the big main and supplementary static methods. Added a unit test with a bug TODO to fix what seems to be an edge case bug in SW
-- Integration test to make sure we can actually write a BAM for each mode. This test only ensures that the code runs and doesn't exception out. It doesn't actually enforce any MD5s
-- HaplotypeBAMWriter also left aligns indels in the reads, as SW can return a random placement of a read against the haplotype. Calls leftAlign to make the alignments more clear, with unit test of real read to cover this case
-- Writes out haplotypes for both all haplotype and called haplotype mode
-- Haplotype writers now get the active region call, regardless of whether an actual call was made. Only emitting called haplotypes is moved down to CalledHaplotypeBAMWriter
* Fixed GenomeLocSortedSet.add() to ensure that overlapping intervals are detected and an exception is thrown.
* Fixed GenomeLocSortedSet.addRegion() by merging it with the add() method; it now produces sorted inputs in all cases.
* Cleaned up duplicated code throughout the engine to create a list of intervals over all contigs.
* Added more unit tests for add functionality of GLSS.
* Resolves GSA-775.
-- Refactor initialization routine into BadSitesWriter. This now adds the GQ and DP genotype header lines which are necessarily if the input VCF doesn't have proper headers
-- GATKVariantContextUtils subset to biallelics now tolerates samples with bad GL values for multi-allelics, where it just removes the PLs and issues a warning.
* Split the cases into reads that don't have a RG at all vs. those with a RG that's not defined in the header.
* Added integration tests to make sure that the correct error is thrown.
* Resolved GSA-407.
* Removed from codebase NestedHashMap since it is unused and untested.
* Integration tests change because the BQSR CSV is now sorted automatically.
* Resolves GSA-732
-Some QScripts used by public pipeline tests unnecessarily used the (now protected) UnifiedGenotyper.
Changed them to use PrintReads instead.
-Moved ExampleUnifiedGenotyperPipelineTest to protected
-Attempt to fix the flawed and sporadically failing MisencodedBaseQualityUnitTest:
After looking at this class a bit, I think the problem was the use of global arrays for the quals
shared across all reads in all tests (BAMRecord class definitely does not make a separate copy for
each read!). One test (testFixBadQuals) modifies the bad quals array, and if this happens to run
before the testBadQualsThrowsError test the bad quals array will have been "fixed" and no exception
will be thrown.
-replace unnecessary uses of the UnifiedGenotyper by public integration tests
with PrintReads
-move NanoSchedulerIntegrationTest to protected, since it's completely dependent
on the UnifiedGenotyper
This walker was not updated since 2009, and users were getting wrong answers when running it with ReduceReads. I don't want to deal with this because DiagnoseTargets does everything this walker does.
-- This is done to take advantage of longer reads which can produce less ambiguous haplotypes
-- Integration tests change for HC and BiasedDownsampling
The GATK engine does not behave correctly when contigs are indexed
differently in the reads sequence dictionaries vs. the reference
sequence dictionary, and the inconsistently-indexed contigs are included
in the user's intervals. For example, given the dictionaries:
Reference dictionary = { chrM, chr1, chr2, ... }
BAM dictionary = { chr1, chr2, ... }
and the interval "-L chr1", the engine would fail to correctly retrieve
the reads from chr1, since chr1 has a different index in the two dictionaries.
With this patch, we throw an exception if there are contig index differences
between the dictionaries for reads and reference, AND the user's intervals
include at least one of the mismatching contigs.
The user can disable this exception via -U ALLOW_SEQ_DICT_INCOMPATIBILITY
In all other cases, dictionary validation behaves as before.
I also added comprehensive unit tests for the (previously-untested)
SequenceDictionaryUtils class.
GSA-768 #resolve
-- Instead of doing a full SW alignment against the reference we read off bubbles from the assembly graph.
-- Smith-Waterman is run only on the base composition of the bubbles which drastically reduces runtime.
-- Refactoring graph functions into a new DeBruijnAssemblyGraph class.
-- Bug fix in path.getBases().
-- Adding validation code to the assembly engine.
-- Renaming SimpleDeBruijnAssembler to match the naming of the new Assembly graph class.
-- Adding bug fixes, docs and unit tests for DeBruijnAssemblyGraph and KBestPaths classes.
-- Added ability to ignore bubbles that are too divergent from the reference
-- Max kmer can't be bigger than the extension size.
-- Reverse the order that we create the assembly graphs so that the bigger kmers are used first.
-- New algorithm for determining unassembled insertions based on the bubble traversal instead of the full SW alignment.
-- Don't need the full read span reference loc for anything any more now that we clip down to the extended loc for both assembly and likelihood evaluation.
-- Updating HaplotypeCaller and BiasedDownsampling integration tests.
-- Rebased everything into one commit as requested by Eric
-- improvements to the bubble traversal are coming as a separate push
-- changed SkipException constructors that are now private in TestNG
-- Updated build.xml to use the latest testng
-- Added guice dependency to ivy
-- Fixed broken SampleDBUnitTest
The SampleDBUnitTest was only passing before because the map comparison in the old TestNG was broken. It was comparing two DIFFERENT samples and testing for "equals"
GSA-695 #resolve
-- AssessNA12878 now breaks out multi-allelics into bi-allelic components. This means that we can properly assess multi-allelic calls against the bi-allelic KB
-- Refactor AssessNA12878, moving into assess package in KB. Split out previously private classes in the walker itself into separate classes. Added real docs for all of the classes.
-- Vastly expand (from 0) unit tests for NA12878 assessments
-- Allow sites only VCs to be evaluated by Assessor
-- Move utility for creating simple VCs from a list of string alleles from GATKVariantContextUtilsUnitTest to GATKVariantContextUtils
-- Assessor bugfix for discordant records at a site. Previous version didn't handle properly the case where one had a non-matching call in the callset w.r.t. the KB, so that the KB element was eaten during the analysis. Fixed. UnitTested
-- See GSA-781 -- Handle multi-allelic variants in KB for more information
-- Bugfix for missing site counting in AssessNA12878. Previous version would count N misses for every missed value at a site. Not that this has much impact but it's worth fixing
-- UnitTests for BadSitesWriter
-- UnitTests for filtered and filtering sites in the Assessor
-- Cleanup end report generation code (simply the code). Note that instead of "indel" the new code will print out "INDELS"
-- Assessor DoC calculations now us LIBS and RBPs for the depth calculation. The previous version was broken for reduced reads. Added unit test that reads a complex reduced read example and matches the DoC of this BAM with the output of the GATK DoC tool here.
-- Added convenience constructor for LIBS using just SAMFileReader and an iterator. It's now easy to create a LIBS from a BAM at a locus. Added advanceToLocus function that moves the LIBS to a specific position. UnitTested via the assessor (which isn't ideal, but is a proper test)
-- Now supports trimming the alleles from both the reverse and forward direction.
-- Added lots of unit tests for forwrad allele trimming, as well as creating VC from forward and reverse trimming.
-- Added docs and tests for the code, to bring it up to GATK spec
These 2 changes improve runtime performance almost as much as Ryan's previous attempt (with ID-based comparisons):
* Don't unnecessarily overload Allele.getBases() in the Haplotype class.
* Haplotype.getBases() was calling clone() on the byte array.
* Added a constructor to Allele (and Haplotype) that takes in an Allele as input.
* It makes a copy of he given allele without having to go through the validation of the bases (since the Allele has already been validated).
* Rev'ed the variant jar accordingly.
For the reviewer: all tests passed before rebasing, so this should be good to go as far as correctness.
-- top-level walker type (locus, read etc)
-- parallelism options (nt or nct)
-- annotation type (for Variant Annotations)
-- downsampling settings that override engine defaults
-- reference window size
-- active region settings
-- partitionBy info
-- Added getClazzAnnotations() as hub to retrieve various annotations values and class properties through reflection
-- Added getReadFilters() method to retrieve Read Filter annotations
-- getReadFilters() uses recursion to walk up the inheritance to also capture superclass annotations
-- getClazzAnnotations() stores collected info in doc handler root, which is unit.forTemplate in Doclet
-- Modified FreeMarker template to use the Readfilters info (displayed after arg table, before additional capabilities)
-- Tadaaa :-) #GSATDG-63 resolve
-- modified ReadBin GenomeLoc to keep track of softStart() and softEnd() of the reads coming in, to make sure the reference will always be sufficient even if we want to use the soft-clipped bases
-- changed the verification from readLength to aligned bases to allow reads with soft-clipped bases
-- switched TreeSet -> PriorityQueue in the ConstrainedMateFixer as some different reads can be considered equal by picard's SAMRecordCoordinateComparator (the Set was replacing them)
-- pulled out ReadBin class so it can be testable
-- added unit tests for ReadBin with soft-clips
-- added tests for getMismatchCount (AlignmentUtils) to make sure it works with soft-clipped reads
GSA-774 #resolve
-- added a logger.error with a more descriptive message of what the most likely cause of the error is
Typical error happens when a walker's global variable is not initialized properly (usually in test conditions). The old error message was very hard to understand "Could not create module because of an exception of type NullPointerException ocurred caused by exception null"
-- Active regions are created as normal, but they are split and trimmed to the engine intervals when added to the traversal, if there are intervals present.
-- UnitTests for ActiveRegion.splitAndTrimToIntervals
-- GenomeLocSortedSet.getOverlapping uses binary search to efficiently in ~ log N time find overlapping intervals
-- UnitTesting overlap function in GenomeLocSortedSet
-- Discovered fundamental implementation bug in that adding genome locs out of order (elements on 20 then on 19) produces an invalid GenomeLocSortedSet. Created a JIRA to address this: https://jira.broadinstitute.org/browse/GSA-775
-- Constructor that takes a collection of genome locs now sorts its input and merges overlapping intervals
-- Added docs for the constructors in GLSS
-- Update HaplotypeCaller MD5s, which change because ActiveRegions are now restricted to the engine intervals, which changes slightly the regions in the tests and so the reads in the regions, and thus the md5s
-- GenomeAnalysisEngineUnitTest needs to provide non-null genome loc parser
-- log10 functions in QualityUtils allow -Infinity to allow log10(0.0) values
-- Fix edge condition of log10OneMinusX failing with Double.MIN_VALUE
-- Fix another edge condition of log10OneMinusX failing with a small but not min_value double
-- The UG was using MathUtils binomial probability backward, so that the estimated confidence was always NaN, and was as a side effect other utils converted this to a meaningless 0.0. This is all because there wasn't a unit test.
-- I've fixed the calculation, so it's now log10 based, uses robust MathUtils and QualityUtils functions to compute probabilities, and added a unit test.
-- Fixed a few conversion bugs with edge case quals (ones that were very high)
-- Fixed a critical bug in the conversion of quals that was causing near capped quals to fall below their actual value. Will undoubtedly need to fix md5s
-- More precise prob -> qual calculations for very high confidence events in phredScaleCorrectRate, trueProbToQual, and errorProbToQual. Very likely to improve accuracy of many calculations in the GATK
-- Added errorProbToQual and trueProbToQual calculations that accept an integer cap, and perform the (tricky) conversion from int to byte correctly.
-- Full docs and unit tests for phredScaleCorrectRate and phredScaleErrorRate.
-- Renamed probToQual to trueProbToQual
-- Added goodProbability and log10OneMinusX to MathUtils
-- Went through the GATK and cleaned up many uses of QualityUtils
-- Cleanup constants in QualityUtils
-- Added full docs for all of the constants
-- Rename MAX_QUAL_SCORE to MAX_SAM_QUAL_SCORE for clarity
-- Moved MAX_GATK_USABLE_Q_SCORE to RecalDatum, as it's s BQSR specific feature
-- Convert uses of QualityUtils.errorProbToQual(1-x) to QualityUtils.trueProbToQual(x)
-- Cleanup duplicate quality score routines in MathUtils. Moved and renamed MathUtils.log10ProbabilityToPhredScale => QualityUtils.phredScaleLog10ErrorRate. Removed 3 routines from MathUtils, and remapped their usages into the better routines in QualityUtils
@argument (-)-version
(should this be @hidden?)
Prints out the version to System.out and quit(0)
No tests. (any ideas on how to test this would be happily accepted)
This helps a lot since FileChannel is very low-level and traversing the BAMIndex involves lots of short reads.
- Fixed a deterioration in BAMIndex due to rev'ed picard (see below)
- Added unit tests for SeekableBufferedStream
- Added integrationTests for GATKBAMIndex (in PileupWalkerIntegrationTest)
- Added a runtime-test to verify that the amount read equals the amount requested.
- Added failing tests with expectedExceptions
- Used a DataProvider to make code nicer
-- The default of 10 minutes is right on the edge for some tests, and we really want a default not to enforce a max time (test should be short) but to stop testng from failing to terminate ever in the case where some test is truly hung
-- Renamed ValidatePileup to CheckPileup since validation is reserved word
-- Renamed AlignmentValidation to CheckAlignment (same as above)
-- Refactored category definitions to use constants defined in HelpConstants
-- Fixed a couple of minor typos and an example error
-- Reorganized the GATKDocs index template to use supercategories
-- Refactored integration tests for renamed walkers (my earlier refactoring had screwed them up or not carried over)
-- HaplotypeCaller and PerReadAlleleLikelihoodMap should use LinkedHashMaps instead of plain HashMaps. That way the ordering when traversing alleles is maintained. If the JVM traverses HashMaps with random ordering, different reads (with same likelihood) may be removed by contamination checker, and different alleles may be picked if they have same likelihoods for all reads.
-- Put in some GATKDocs and contracts in HaplotypeCaller files (far from done, code is a beast)
-- Update md5's due to different order of iteration in LinkedHashMaps instead of HashMaps inside HaplotypeCaller (due to change in PerReadAlleleLikelihoodMap that also slightly modifies reads chosen by per-read downsampling).
-- Reenabled testHaplotypeCallerMultiSampleGGAMultiAllelic test
-- Added some defensive argument checks into HaplotypeCaller public functions (not intended to be done yet).
-- Sorted out contents of BAM Processing vs. Diagnostics & QC Tools
-- Moved two validation-related walkers from Diagnostics & QC to Validation Utilities
-- Reworded some category names and descriptions to be more explicit and user-friendly
-- Uses 1/N for N potential start sites as the probability of starting at any one of the potential start sites
-- Add flag that says to use the original edge condition, respected by all subclasses. This brings the new code back to the original state, but with all of the cleanup I've done
-- Only test configurations where the read length <= haplotype length. I think this is actually the contract, but we'll talk about this tomorrow
-- Fix egregious bug with the myLog10SumLog10 function doing the exact opposite of the requested arguments, so that doExact really meant don't do exact
-- PairHMM now exposes computeReadLikelihoodGivenHaplotypeLog10 but subclasses must overload subComputeReadLikelihoodGivenHaplotypeLog10. This protected function does the work, and the public function will do argument and result QC
-- Have to be more tolerant of reference (approximate) HMM. All unit tests from the original HMM implementations pass now
-- Added locs of docs
-- Generalize unit tests with multiple equivalent matches of read to haplotype
-- Added runtime argument checking for initial and computeReadLikelihoodGivenHaplotypeLog10
-- Functions to dumpMatrices for debugging
-- Fix nasty bug (without original unit tests) in LoglessPairHMM
-- Max read and haplotype lengths only worked in previous code if they were exactly equal to the provided read and haplotype sizes. Fixed bug. Added unit test to ensure this doesn't break again.
-- Added dupString(string, n) method to Utils
-- Added TODOs for next commit. Need to compute number of potential start sites not in initialize but in the calc routine since this number depends not on the max sizes but the actual read sizes
-- Unit tests for the hapStartIndex functionality of PairHMM
-- Moved computeFirstDifferingPosition to PairHMM, and added unit tests
-- Added extensive unit tests for the hapStartIndex functionality of computeReadLikelihoodGivenHaplotypeLog10
-- Still TODOs left in the code that I'll fix up
-- Logless now compute constants, if they haven't been yet initialized, even if you forgot to say so
-- General: the likelihood penalty for potential start sites is now properly computed against the actual read and reference bases, not the maximum. This involved moving some initialize() code into the computeLikelihoods function. That's ok because all of the potential log10 functions are actually going to cached versions, so the slowdown is minimal
-- Added some unit tests to ensure that common errors (providing haplotypes too long, reads too long, not initializing the HMM) are captured as errors
-- Would have been squashed but could not because of subsequent deletion of Caching and Exact/Original PairHMMs
-- Actual working unit tests for PairHMMUnitTest
-- Fixed incorrect logic in how I compared hmm results to the theoretical and exact results
-- PairHMM has protected variables used throughout the subclasses
-- Added CAPILLARY and HELICOS platforms as required by spec 1.4
-- Added extensive unit tests to ensure NGSPlatform functions work as expected.
-- Fixed some NPE bugs for reads that don't have RGs or PLs in their RG fields
- Throws user exception if it is.
- Can be turned off with --allow_bqsr_on_reduced_bams_despite_repeated_warnings argument.
- Added test to check this is working.
- Added docs to BQSRReadTransformer explaining why this check is not performed on PrintReads end.
- Added small bug fix to GenomeAnalysisEngine that I uncovered in this process.
- Added comment about not changing the program record name, as per reviewer comments.
- Removed unused variable.
- I had added the framework in the VA engine but should not have hooked it up to the HC yet since the RefMetaDataTracker is always null.
- Added contracts and docs to the relevant methods in the VA engine so that this doesn't happen in the future.
The migration of org.broadinstitute.variant into the Picard repo is
complete. This commit deletes the org.broadinstitute.variant sources
from our repo and replaces it with a jar built from a checkout of the
latest Picard-public svn revision.
contain two columns, Sample (String) and Fraction (Double) that form the Sample-Fraction map for the per-sample AlleleBiasedDownsampling.
-Integration tests to UnifiedGenotyper (Using artificially contaminated BAMs created from a mixure of two broadly concented samples) were added
-includes throwing an exception in HC if called using per-sample contamination file (not implemented); tested in a new integration test.
-(Note: HaplotypeCaller already has "Flat" contamination--using the same fraction for all samples--what it doesn't have is
_per-sample_ AlleleBiasedDownsampling, which is what has been added here to the UnifiedGenotyper.
-New class: DefaultHashMap (a Defaulting HashMap...) and new function: loadContaminationFile (which reads a Sample-Fraction file and returns a map).
-Unit tests to the new class and function are provided.
-Added tests to see that malformed contamination files are found and that spaces and tabs are now read properly.
-Merged the integration tests that pertain to biased downsampling, whether HaplotypeCaller or unifiedGenotyper, into a new IntegrationTest class.
-- The progress meter isn't started until the GATK actually calls execute on the microscheduler. Now we get a message saying "Creating shard strategy" while this (expensive) operation runs
- Added contract enforcement for public methods
- Refactored the conversion from read -> (allele -> likelihood) to allele -> list[read] into its own method
- added method documentation for non getters/setters
- finals, finals everywhere
- Add in a unit test for the PerReadAlleleLikelihoodMap. Complete coverage except for .clear() and a method that is a straight call into a separately-tested utility class.
-- Bringing code up to document, style, and code coverage specs
-- Move GATKRunReportUnitTest to private
-- Fully expand GATKRunReportUnitTests to coverage writing and reading GATKRunReport to local disk, to standard out, to AWS.
-- Move documentation URL from GATKRunReport to UserException
-- Delete a few unused files from s3GATKReport
-- Added capabilities to GATKRunReport to make testing easier
-- Added capabilities to deserialize GATKRunReports from an InputStream
- Added RR qual correctness tests (note that this is a case where we don't add code coverage but still need to test critical infrastructure).
- Also added minor cleanup of BaseUtils
I've confirmed via a script that all of these differences only
involve the version number bump in the BAM headers and nothing
else:
< @HD VN:1.0 GO:none SO:coordinate
---
> @HD VN:1.4 GO:none SO:coordinate
These patches to GATKBAMIndex are causing massive BAM index reading errors in
combination with the latest version of Picard. The bug is either in the patches
themselves or in the underlying SeekableBufferedStream class they rely on. Until
the cause can be identified, we are temporarily backing out these changes so that
we can continue to run with the latest Picard/Tribble.
This reverts commits:
81483ec21e528790dfa719d18cdee27d577ca98e
68cf0309db490b79eecdabb4034987ff825ffea8
54bb68f28ad5fe1b3df01702e9c5e108106a0176
This is a necessary prerequisite for the org.broadinstitute.variant migration.
-Picard and sam-jdk go from version 1.67.1197 to 1.84.1337
-Picard-private goes from version 2375 to 2662
-Tribble goes from version 119 to 1.84.1337
-RADICALLY trimmed down the list of classes we extract from Picard-private
(jar goes from 326993 bytes to 6445 bytes!)
Resources must be in a subdirectory called "resources" in the package
hierarchy to be picked up by the packaging system. Adding each resource
manually to the jars in build.xml does not cause the resource to be
added to the standalone GATK jar when we package the GATK, so it's best
to always use this convention.
If a read had an existing BAQ tag, was clipped by our engine, and couldn't have the BAQ recalculated (for whatever reason), then we would
fail in the BQSR because we would default to using the old tag (which no longer matched the length of the read bases).
The right thing to do here is to remove the old BAQ tag when RECALCULATE and ADD_TAG are the BAQ modes used but BAQ cannot be recalculated.
Added a unit test to ensure that the tags are removed in such a case.
-- 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
-- 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.
-- New algorithm will only try to create an active region if there's at least maxREgionSize + propagation distance states in the list. When that's true, we are guaranteed to actually find a region. So this algorithm is not only truly correct but as super fast, as we only ever do the search for the end of the region when we will certainly find one, and actually generate a region.
-- 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
-- Now records the position of the current locus, as well as that of the last read. Necessary when passing through regions with no reads. The previous version would keep accumulating empty active regions, and never discharge them until end of traversal (if there was no reads in the future) or until a read was finally found
-- Protected a call to logger.debug with if ( logger.isDebugEnabled()) to avoid a lot of overhead in writing unseen debugger logging information
-- 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
I've resigned myself instead to create a mapping from Allele to Haplotype. It's cheap so not a big deal, but really shouldn't be necessary.
Ryan and I are talking about refactoring for GATK2.5.
-- 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...
This way, we don't need to create a new Allele for every read/Haplotype pair to be placed in the PerReadAlleleLikelihoodMap (very inefficient). Also, now we can easily get the Haplotype associated with the best allele for a given read.
2. Framework is set up in the VariantAnnotator for the HaplotypeCaller to be able to call in to annotate dbSNP plus comp RODs. Until the HC uses meta data though, this won't work.
-- 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?
-- Add an option to not allocate always ArrayLists of targetSampleSize, but rather the previous size + MARGIN. This helps for LIBS as most of the time we don't need nearly so much space as we allow
-- consumeFinalizedItems returns an empty list if the reservior is empty, which it often true for our BAM files with low coverage
-- Allow empty sample lists for SamplePartitioner as these are used by the RefTraversals and other non-read based traversals
Make the reservoir downsampler use a linked list, rather than a fixed sized array list, in the expectFewOverflows case
-- Instead of storing a list of list of alignment starts, which is expensive to manipulate, we instead store a linear list of alignment starts. Not grouped as previously. This enables us to simplify iteration and update operations, making them much faster
-- Critically, the downsampler still requires this list of list. We convert back and forth between these two representations as required, which is very rarely for normal data sets (WGS NA12878 on chr20 is 0.2%, 4x WGS is even less).
-- 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
-- Use a linked hash map instead of a hash map since we want to iterate through the map fairly often
-- Ensure that we call doneSubmittingReads before getting reads for samples. This function call fell out before and since it wasn't enforced I only noticed the problem while writing comments
-- Don't make unnecessary calls to contains for map. Just use get() and check that the result is null
-- Use a LinkedList in PassThroughDownsampler, since this is faster for add() than the existing ArrayList, and we were's using random access to any resulting
-- 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
-- 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.
These pipelines were supposed to serve as an example for the community, they were written a long-long-long time ago and are being used today by users as the 'best practice pipeline'. Unless we decide we want to support and maintain an example best-practices pipeline, I'm moving these to private.
-- 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.
1. Add the symbolic 'current' link for the new bundle dir
2. Don't gzip and copy .out files
3. Don't call chr20 SNPs on the example BAM because it's now just a few reads on chr1
-- 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.
Chris Hartl
Geraldine Van der Auwera
Ryan Poplin
Louis Bergelson
Mauricio Carneiro
Eric Banks
David Roazen
sathibault
Menachem Fromer
Valentin Ruano Rubio
Mark DePristo
Khalid Shakir
Francesco
Scott Thibault
Guillermo del Angel
James Warren
Michael McCowan
Yossi Farjoun
Chris Hartl
Ami Levy-Moonshine
Joel Thibault
Tad Jordan
xingwei2012