now thrown if the combination of -L and -XL intervals specified on the command
line results in an empty interval set after set subtraction.
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message. Lines containing only whitespace in .list files are now ignored.
Also added support for comments in .list files: lines whose first
non-whitespace character is '#' are now also ignored.
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QuickCCTest is my test script for the gatherer.
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-number N -- generates a VCF with exactly N randomly chosen variants with equal probability.
-fraction F -- generates a VCF with approximately F (between 0-1) randomly chosen variants with equal probability. (Similar behavior to RandomlySplitVariants walker).
The reason for two modes is that the first one may need a lot of memory if your sample size is too large. The wiki is being updated with this information now.
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- create size() method that returns an approximation of the uncompressed size in bytes of BAM span.
I'll use this method as a protoshard weighting function until we determine how to normalize the
weights across the different data access mechanisms (reads, reference, RODs).
- Implementations of basic union/intersection/subtraction mechanisms for BAM spans; should be enough
to get an accurate weight for two proto-shards put together.
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Works in the same coordinate space as BAM chunks, so this will be used to
calibrate chunk weighting.
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concurrently-written fix for the same.
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900 samples in a single BAM:
Release: 64.29
With sample-specific size: 24s - 35s
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1-10mb of chromosome one, from NA12878 HiSeq 64x data set on hg18:
Full BAM
Write time: 8.6 m
Size: 866M
CountReads time: 2.9 m
UG time: 11.3 m
Simplified BAM:
Write time: 6.2
Size: 458M
CountReads time: 85.7 s
UG time: 10.1 m
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VCF gathering passes on the no_header and sites_only flags to CombineVariants.
Fixed deletion of gathered log files. Although they are intermediate and do not need to be re-run if not present, they should not be deleted.
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schedule file infrastructure has been restored, and is now a single file.
Only the exact bins required for the traversal are stored in the schedule.
Very close to being able to merge schedule entries.
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- now it differentiates between confident REF calls and not confident calls.
- you can now use a BAM file as the truth set.
- output is much clearer now
dataProcessingPipeline version 2, ready to be used.
- All the processing is now done at the sample level
- Reads the input bam file headers to combine all lanes of the same sample.
- Cleaning is now scattered/gathered. Inteligently breaks down in as many intervals as possible, given the dataset.
- Outputs one processed bam file per sample (and a .list file with all processed files listed)
- Much faster, low pass (read Papuans) can run in the hour queue.
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ProduceBeagleInputWalker can optionally emit a beagle markers file, necessary to use the beagled reference panel for imputation. Also supports the VQSR calibration curve idea that a site can be flagged as a certain FP, based on the VQSLOD field. This allows us to have both continuous quality in the refinement of sites as well as hard filtering at some threshold so we don't end up with lots of sites with all 1/3 1/3 1/3 likelihoods for all samples (i.e., a definite FP site where we don't know anything about the samples).
Added a new VariantsToBeagleUnphased walker that writes out a marker drive hard-call unphased genotypes file suitable for imputating missing genotypes with a reference panel with beagle. Can optionally keep back a fraction of sites, marked as missing in the genotypes file, for assessment of imputation accuracy and power. The bootstrap sites can be written to a separate VCF for assessment as well.
Finally, my general Queue script for creating and evaluating reference panels from VCF files. Supports explicitly genotyping a BAM file at each panel SNP site, for assessment of imputation accuracy of a reference panel. Lots of options for exploring the impact of the VQS likelihooods, multiple VCFs for constructing the reference panel, as well as fraction of sites left out in assessing the panel's power.
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a) Scrapped the way in which we constructed candidate haplotypes because it wasnt fully correct and yielded corner conditions with incorrect genotyping and likelihood computation. Ideally, a haplotype should "cover" the read and the most likely alignments should be such that the ends of the read are inside the ends of the haplotype. This wasn't happening, and if you have a "dangling read off a haplotype" the probabilistic alignment model may prefer to shift a read instead of scoring it correctly - this is especially bad with tandem repeat insertions.
So now, we build haplotypes based on the reference context and adaptively change them based on read alignment positions, plus some padding and uncertainty in the alignment.
b) Changed the way soft clipped based are dealt with. Instead of either ignoring them or using them, we only use them if the read start or end position (after soft clipping) are within eventDistance of the current location. This is done because it's very common that BWA's strictly local SW implementation will soft clip every single read at an insertion position because it couldn't place that end of the read without too many mismatches, but the read is legit and the bases are good quality. If we don't take these bases into consideration, reads which are informative of an insertion event are essentially discarded because the informative part is clipped away.
c) Several cleanups and fixes to the context-dependent gap penalty model based on length of HRun.
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indexes'. Not yet elegant, but proves that it circumvents the performance
issues associated with the meta-index.
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VariantEval: now sports the new MendelianViolation utility class.
MendelianViolationClassifier: I noticed I had broken chartl's walker by changing VariantEval, so I took the liberty to modify it to use the new library too, though I kept modifications to a minimum, could have gone into full integration if this is a useful tool, but since it's in oneoffs, I decided not to go all out.
MendelianViolation: Some getter methods were added for chartl and VariantEval.
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b) small bug fix when setting debug arguments
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ps: I moved it out of tribble. If you think it should reside in a different place, just yell at me.
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Reinstated. Proper unit test to follow.
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GenotypeAndValidate: now annotates the validations with callStatus.
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b) Add an experimental annotation to log indel type string inside the INFO field, just for debugging/temp analysis purposes (will consider making it standard if it proves useful).
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GAV: First version of the walker that validates reads from a BAM file based on an annotated VCF with TP/FP annotations.
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same shards as the previous sharder, so that I can directly compare filespans
to see where some performance bugs lie.
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-disc (--discordance) parameter together with a ROD track will output a VCF with the variants in the ROD track that are not present in the 'variants' VCF. Useful tool to list the variants from hapmap (for example) that weren't called in a dataset.
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Model is disabled by default and we're still using the old Dindel model until I'm more confident that new model is a definitive improvement, so right now this is enabled by hidden command line arguments, and it's not to be used yet.
In detail:
a) Several refactorings to share softMax() available to other modules, so its now part of MathUtils.
b) Refactored a couple of read utilities and moved from BAQ to ReadUtils.
c) New PairHMMIndelErrorModel class implementing new likelihood model
d) Several new hidden debug arguments in UAC.
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Also, created a standard Queue script that calculates a suite of standard indel and SNP assessment results. Will be the basis for a general evaluation Queue script with standardized data files for SNPs and Indels.
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whatsoever were misusing the Picard MergingIterator, triggering a re-traversal
through the entire contig.
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- RefMetaDataTracker: grabbing variant contexts given a prefix (not sure where else this was implemented, if someone can show me I'll remove it)
- VCFUtils: grabbing VCF headers given a prefix
- MathUtils: Useful functions for calculating statistics on collections of Numbers
- VariantAnnotator: Made isUniqueHeaderLine a public static method -- maybe this should go into a different class. Not sure.
- Associations: PluginManager now used to propagate classes, implementations for Z,T,U tests, slight alteration to format to make the objects stored
in the window optionally different from those returned by whatever statistic is run across the window
Added:
- MannWhitneyU. Started to fix up WilcoxonRankSum but there are comments in there questioning the validity of some of the code, and I'm sure that
it's actually doing a U test. This implementation includes the direct calculation of p-values for small sample sizes, and a uniform approximation
for when one of the sample sets is small, and the other large. Unit tests to follow.
- BootstrapCallsMerger: takes n VCFs which have been called on the same samples; merges them together while averaging the annotations
- BootstrapCalls.q: qscript for testing the effectiveness of boostrap low-pass calling on the exome
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we find a read flagged as 'mapped' in the unmapped region at the end of the
file.
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case UserException.MalformedBAM to match case of UserExceptio.MissortedBAM for consistency and
ease-of-use.
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file. Temporary fix until Picard changes the behavior of indexing.
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bin tree are dictated by the lowest bin level, whether it exists or not.
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an unexpectedly small amount of data is present.
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optimizer was mutating the data stored in the indices. Protosharding still
disabled by default.
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are works in progress, and this checkin will provide a baseline against which to gauge
improvements to both projects.
Low-memory BAM protoshards (disabled by default):
- Currently passing ValidatingPileupIntegrationTest.
- Gets progressively slower throughout the traversal, but should run at least as fast as original implementation.
- Uses 10+ file handles per BAM, but should use 3.
BAM performance microbenchmark test system:
- Currently tests performance of BAM reading using SAM-JDK vs. GATK
- Tests do not hit all GATK performance hotspots.
- New tests that require input data in a slightly different form are hard to implement.
- Output of test results is not easily parseable (investigating Google Caliper for possible improvements).
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BatchMerge - additional support for indels (can't just test the alternate allele when it's an extended event, must also specify that you want to use the dindel model when you actually test the allele)
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a) In case of a deletion value was completely broken, we'd report 0 or -1.
b) For indels, we report maximum of forward and backward values - I've seen empirically many sites which are not strand biased but which seem to be artifacts and the homopolymer run is always to the right only (because we left align by convention).
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hanna
aaron
rpoplin
ebanks
asivache
fromer
chartl
droazen
carneiro
depristo
kshakir
delangel
kiran