a) Sites were numerically well behaved now, but another hard fact of life is that the AF iteration is defined in linear Pr space, not in log likelihood space, and the math doesn't work out in log space. So, we need to convert back and forth from lin to log space.
b) As a consequence of a), the code got a major slowdown, and calling the 629 samples was about 15 times slower than before (sic).
c) To solve b), log10 of integers are now cached at init, and numerical approximations are now made. Most importantly, I'm using the approximation that log(exp(a) + exp(b)) ~= max(a,b) which seems almost inconsequential in practical performance but reduces computation time to what it was before. More detailes analyses are forthcoming. This approximation can be refined further on to avoid expensive log-exp conversions if further profiling and analysis deems it necessary.
Also, two other issues were solved:
a) Strand bias computation was actually wrong in the case where the optimal AC was bigger than max(forward reads,reverse reads). Now the code is exactly as buggy as the grid search model (all bugs are equal, but some are more equal than others)
b) Genotype likelihoods are now computed in a better way and if a likelihood < 0 we don't just cap to 0 but do something a bit smarter.
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a) Fix it up because it broke with a recent checkin to annotate vcf with unfiltered depth.
b) Printout of ref/alt alleles in output vcf was incorrect because the start/stop positions of associated GenomeLoc were incorrectly computed in case of a deletion.
c) Redid Beagle input/output walkers as not assume that ref was a single base, not to assume that variant was a vcf and generalized it to be indel-capable, so now the Beagle walkers can be used for indels as well.
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a) In Indel genotyper: we can't deal yet with extended events correctly and we are still triggering at each extended event which results in repeated records on a vcf. So, to avoid this, keep track of start position of candidate variantes we've visited and if we've visited a variant before we don't do it again.
b) Avoid infinite terms in QUAL and in genotype likelihoods which can happen if posterior AF happens to be exactly zero. For now, hard-code a minimum value of each term of the posterior AF likelihood to be -300 (ie 1e-300 in lin space). This can be solved with better and smarter log-to-lin conversions and some precision fixes in AF calculation.
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a) Fixed bugs in new dynamic programming-based genotyper
b) Fixed up temp hack that handles extended pileups for now.
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- Brought over exact AF estimation from branch (which is now dead). Exact model is default in UnifiedGenotyperV2.
- Implemented completely new genotyping algorithm given best AF estimate using dynamic programming, which in theory should be better than both greedy search and any HWE-based genotyper.
- Integrated and added new Dindel likelihood estimation model.
- Corrected annotators that would call readBasePileup: since we can be annotating extended events, best way is to interrogate context for kind of pileup and either readBasePileup or readExtendedEventPileup.
All changes above except last one are still in playground since they require more testing.
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This will allow other programs like Queue to reuse the functionality.
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pieces of core that depend on playground. Most of these have been eliminated by
(temporarily) promoting Aaron's report system to core in this checkin. I'll
follow up with other changes in separately.
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Also added a new error message that I thought would be helpful...
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*** Three integration tests had to change: ***
RecalibarationWalkersIntegrationTest:
One of the tests was using the interval as the snp track, and wasn't supplying a DbSNP track (for CountCovariates)
SequenomValidationConverterIntegrationTest:
relies on Plink ROD which we've removed.
PileupWalkerIntegrationTest:
we no longer have implicit interval tracks, so there isn't a rod name over the specified region. Otherwise the same result.
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This commit adds two important classes: Sample, which contains data about one sample; and SampleDataSource, which manages sample data a la ReferenceDataSource and ReadsDataSource.
This code should be stable, but it has not been integrated with existing walkers yet. That's the next commit.
In the meantime, feel free to experiment with the code - there are two basic example walkers in the playground.sample package. And PLEASE let me know if you see any errors/inconsistencies.
Note that this also adds a new dependency on SnakeYaml, a YAML parser.
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This object designed to be both the structure that holds data during the execution of the walker, as well as the object that properly formats and emits the data so that it can be easily loaded into R. In the end, you get a table that looks like this:
##:GATKReport.v0.1 ErrorRatePerCycle : The error rate per sequenced position in the reads
cycle errorrate.61PA8.7 qualavg.61PA8.7
0 0.007451835696110506 25.474613284804366
1 0.002362777171937477 29.844949954504095
2 9.087604507451836E-4 32.87590975254731
3 5.452562704471102E-4 34.498999090081895
4 9.087604507451836E-4 35.14831665150137
5 5.452562704471102E-4 36.07223435225619
6 5.452562704471102E-4 36.1217248908297
7 5.452562704471102E-4 36.1910480349345
8 5.452562704471102E-4 36.00345705967977
...
A GATKReport object can hold multiple tables, and the write() method will emit all tables in succession. Each table starts with its own ##:GATKReport.v0.1 table header, so each table can stand alone. This allows for tables to be mixed and matched in a single file, or for the output from different walkers to be combined into a single file with no ill effect.
The display property of individual columns can be turned off. This is useful when a column is used to store intermediate results, necesary for the computation of some later value, but the contents of the intermediate column itself are not required in the final output file.
Finally, the GATKReportTable allows for some simple, mathematical, element-wise and column-wise operations. For instance, two whole columns can be divided, the results of the operation being stored in a third column. This mimics the most basic of R operations, where whole vectors can be added, subtracted, multiplied or divided without requiring the developer to explicitly write a loop.
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1. Rip out all of Ben's code intended to circumvent the stable VCF Writer output system in multi-threaded mode (I threw up a little when
I saw this code). This will improve memory consumption when running with -nt.
2. Don't annotate indels or > bi-allelic sites.
3. Fix bug where not all records were making it into the output VCF.
4. General code clean up.
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- GATKVCFWriter deleted, to be replaced if absolutely necessary when VCF writing goes into Tribble.
- VCFWriter is now an interface, for easier redirection.
- VCFWriterImpl fleshes out the VCFWriter interface.
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Added ability to skip up-to-date jobs where the outputs are older than the inputs.
Changed -T CountDuplicates --quiet to --quietLocus so that Queue GATK extensions can use both short and full argument names.
Short names can be used to set values on Queue GATK extensions, for example: vf.XL :+= myFile
Moved Hidden from the GATK to StingUtils.
Updated ivy from 2.0.0 to 2.2.0-rc1 to fix sha1 issue: http://bit.ly/aX72w7
Added Queue to javadoc and testing build targets.
Added first Queue unit test.
Another pass at avoiding cycles in the DAG thanks to all function I/O being files.
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- Tribble is included directly in the GATK repo; those who have access to commit to Tribble can now directly commit from the GATK directory from Intellij; command line users can commit from
inside the tribble directory.
- Hapmap ROD now in Tribble; all mentions have been switched over.
- VariantContext does not know about GenomeLoc; use VariantContextUtils.getLocation(VariantContext vc) to get a genome loc.
- VariantContext.getSNPSubstitutionType is now in VariantContextUtils.
- This does not include the checked-in project files for Intellij; still running into issues with changes to the iml files being marked as changes by SVN
I'll send out an email to GSAMembers with some more details.
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