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.
Principle is simple: when coverage is deep enough, any single-base read error will look like a rare k-mer but correct sequence will be supported by many reads to correct sequences will look like common k-mers. So, algorithm has 3 main steps:
1. K-mer graph buildup.
For each read in an active region, a map from k-mers to the number of times they have been seen is built.
2. Building correction map.
All "rare" k-mers that are sparse (by default, seen only once), get mapped to k-mers that are good (by default, seen at least 20 times but this is a CL argument), and that lie within a given Hamming distance (by default, =1). This map can be empty (i.e. k-mers can be uncorrectable).
3. Correction proposal
For each constituent k-mer of each read, if this k-mer is rare and maps to a good k-mer, get differing base positions in k-mer and add these to a list of corrections for each base in each read. Then, correct read at positions where correction proposal is unanimous and non-empty.
The algorithm defaults are chosen to be very stringent and conservative in the correction: we only try to correct singleton k-mers, we only look for good k-mers lying at Hamming distance = 1 from them, and we only correct a base in read if all correction proposals are congruent.
By default, algorithm is disabled but can be enabled in HaplotypeCaller via the -readErrorCorrect CL option. However, at this point it's about 3x-10x more expensive so it needs to be optimized if it's to be used.
Ns are treated as wildcards in the PairHMM so creating haplotypes with Ns gives them artificial advantages over other ones.
This was the cause of at least one FN where there were Ns at a SNP position.
Problem:
The sequence graphs can get very complex and it's not enough just to test that any given read has non-unique kmers.
Reads with variants can have kmers that match unique regions of the reference, and this causes cycles in the final
sequence graph. Ultimately the problem is that kmers of 10/25 may not be large enough for these complex regions.
Solution:
We continue to try kmers of 10/25 but detect whether cycles exist; if so, we do not use them. If (and only if) we
can't get usable graphs from the 10/25 kmers, then we start iterating over larger kmers until we either can generate
a graph without cycles or attempt too many iterations.
-- 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.
-- Created a new annotation DepthPerSampleHC that is by default on in the HaplotypeCaller
-- The depth for the HC is the sum of the informative alleles at this site. It's not perfect (as we cannot differentiate between reads that align over the event but aren't informative vs. those that aren't even close) but it's a pretty good proxy and it matches with the AD field (i.e., sum(AD) = DP).
-- Update MD5s
-- delivers [#48240601]
-- 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]
-- Ultimately this was caused by overly aggressive merging of CommonSuffixMerger. In the case where you have this graph:
ACT [ref source] -> C
G -> ACT -> C
we would merge into
G -> ACT -> C
which would linearlize into
GACTC
Causing us to add bases to the reference source node that couldn't be recovered. The solution was to ensure that CommonSuffixMerger only operates when all nodes to be merged aren't source nodes themselves.
-- Added a convenient argument to the haplotype caller (captureAssemblyFailureBAM) that will write out the exact reads to a BAM file that went into a failed assembly run (going to a file called AssemblyFailure.BAM). This can be used to rerun the haplotype caller to produce the exact error, which can be hard in regions of deep coverage where the downsampler state determines the exact reads going into assembly and therefore makes running with a sub-interval not reproduce the error
-- Did some misc. cleanup of code while debugging
-- [delivers #50917729]
-- 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
-- The previous implementation attempted to be robust to this, but not all cases were handled properly. Added a helper function updateInde() that bounds up the update to be in the range of the indel array, and cleaned up logic of how the method works. The previous behavior was inconsistent across read fwd/rev stand, so that the indel cigars at the end of read were put at the start of reads if the reads were in the forward strand but not if they were in the reverse strand. Everything is now consistent, as can be seen in the symmetry of the unit tests:
tests.add(new Object[]{"1D3M", false, EventType.BASE_DELETION, new int[]{0,0,0}});
tests.add(new Object[]{"1M1D2M", false, EventType.BASE_DELETION, new int[]{1,0,0}});
tests.add(new Object[]{"2M1D1M", false, EventType.BASE_DELETION, new int[]{0,1,0}});
tests.add(new Object[]{"3M1D", false, EventType.BASE_DELETION, new int[]{0,0,1}});
tests.add(new Object[]{"1D3M", true, EventType.BASE_DELETION, new int[]{1,0,0}});
tests.add(new Object[]{"1M1D2M", true, EventType.BASE_DELETION, new int[]{0,1,0}});
tests.add(new Object[]{"2M1D1M", true, EventType.BASE_DELETION, new int[]{0,0,1}});
tests.add(new Object[]{"3M1D", true, EventType.BASE_DELETION, new int[]{0,0,0}});
tests.add(new Object[]{"4M1I", false, EventType.BASE_INSERTION, new int[]{0,0,0,1,0}});
tests.add(new Object[]{"3M1I1M", false, EventType.BASE_INSERTION, new int[]{0,0,1,0,0}});
tests.add(new Object[]{"2M1I2M", false, EventType.BASE_INSERTION, new int[]{0,1,0,0,0}});
tests.add(new Object[]{"1M1I3M", false, EventType.BASE_INSERTION, new int[]{1,0,0,0,0}});
tests.add(new Object[]{"1I4M", false, EventType.BASE_INSERTION, new int[]{0,0,0,0,0}});
tests.add(new Object[]{"4M1I", true, EventType.BASE_INSERTION, new int[]{0,0,0,0,0}});
tests.add(new Object[]{"3M1I1M", true, EventType.BASE_INSERTION, new int[]{0,0,0,0,1}});
tests.add(new Object[]{"2M1I2M", true, EventType.BASE_INSERTION, new int[]{0,0,0,1,0}});
tests.add(new Object[]{"1M1I3M", true, EventType.BASE_INSERTION, new int[]{0,0,1,0,0}});
tests.add(new Object[]{"1I4M", true, EventType.BASE_INSERTION, new int[]{0,1,0,0,0}});
-- delivers #50445353
-- 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.
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.
-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.
-- Started by Mark. Finished up by Ryan.
-- GGA mode still respected glm argument for SNP and INDEL models, so that you would silently fail to genotype indels at all if the -glm INDEL wasn't provided, but you'd still emit the sites, so you'd see records in the VCF but all alleles would be no calls.
-- https://www.pivotaltracker.com/story/show/48924339 for more information
-- [resolves#48924339]
Problem
--------
Diagnose Targets is outputting missing intervals to stdout if the argument -missing is not provided
Solution
--------
Make it NOT default to stdout
[Delivers #50386741]
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.
-- 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
-- [Delivers #49876703]
-- Add integration test and test file
-- Update SymbolicAlleles combine variant tests, which was turning unfiltered records into PASS!
Bug fixes and missing interval functionality for Diagnose Targets
While the code seems fine, the complex parts of it are untested. This is probably fine for now, but private code can have a tendency to creep into the codebase once accepted. I would have preferred that unit test OR a big comment stating that the code is untested (and thus broken by Mark's rule).
It is with these cavets that I accept the pull request.
Problem
------
Diagnose Targets identifies holes in the coverage of a targetted experiment, but it only reports them doesn't list the actual missing loci
Solution
------
This commit implements an optional intervals file output listing the exact loci that did not pass filters
Itemized changes
--------------
* Cache callable statuses (to avoid recalculation)
* Add functionality to output missing intervals
* Implement new tool to qualify the missing intervals (QualifyMissingIntervals) by gc content, size, type of missing coverage and origin (coding sequence, intron, ...)
Problem
-------
When the interval had no reads, it was being sent to the VCF before the intervals that just got processed, therefore violating the sort order of the VCF.
Solution
--------
Use a linked hash map, and make the insertion and removal all happen in one place regardless of having reads or not. Since the input is ordered, the output has to be ordered as well.
Itemized changes
--------------
* Clean up code duplication in LocusStratification and SampleStratification
* Add number of uncovered sites and number of low covered sites to the VCF output.
* Add new VCF format fields
* Fix outputting multiple status when threshold is 0 (ratio must be GREATER THAN not equal to the threshold to get reported)
[fixes#48780333]
[fixes#48787311]
-- 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.
Mark DePristo
Ryan Poplin
Eric Banks
Guillermo del Angel
Mauricio Carneiro
Valentin Ruano-Rubio
Michael McCowan
David Roazen
Yossi Farjoun
Chris Hartl