Refactored several Interval utilties from GenomeLocParser to IntervalUtils, as one might expect they go
Removed GenomeLoc.clone() method, as this was not correctly implemented, and actually unnecessary, as GenomeLocs are immutable. Several iterator classes have changed to remove their use of clone()
Removed misc. unnecessary imports
Disabled, temporarily, the validating pileup integration test, as it uses reads mapped to an different reference sequence for ecoli, and this now does not satisfy the contracts for GenomeLoc
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Reviewed pipelines with dev team.
HSP updates:
- Calling SNPs and Indels at the same time then using SelectVariants to separate them for filtering
- Moved logs next to the files like in WGP
- Flattened outputs into one directory
- The file names for the final outputs are now <projectName>.vcf and <projectName>.eval
- Updated test to pass the chr20 intervals instead of a boolean
- Removed MultiFCP
WGP updates:
- Only cleaning and calling chromosomes 1-22, X, Y, MT
- Splitting SNPs from indels, filtering indels, then merging the selected SNPs and selected Indels back together to make sure there are no collisions in CombineVariants
- Still running VQSR on the recombined SNPs plus hard filtered indels
- Using hard indel filters from delangel
- Reduced number of tranches with rpoplin
- Changed prior for dbsnp from 10 to 8 with rpoplin
- Assuming identical samples on both CombineVariants
- Explicitly using variant merge option UNION even though it's the default
- Not setting the default genotype merge option PRIORITIZE
- Generating a vcf and eval for each tranche
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and its three or four nearest neighbors could be in memory at once. Tweaking
the iterators to ensure that previous AlignmentContexts don't have strong
references which means that the garbage collector can work effectively to
help us trundle through these regions.
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Removed job priority as temp space isn't as tight at the moment and planning on changing the priority interface.
Updated chunk calling with ebanks:
- Using "the bundle" of resources.
- Using dbsnp 132 and 1000G indel RODs for both RTC & IR.
- Using the default maxIntervalSize in RTC.
- Removed use of UG.exactCalculation argument.
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Also added more logging when extension generation fails.
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This step just changes storage of likelihoods so now we have, instead of an internal matrix, a class member which stores, as a hash table, a mapping from pileup element to an (allele, likelihood) pair. There's no functional change aside from internal data storage.
As a bonus, we get for free a 2-3x improvement in speed in calling because redundant likelihood computations are removed.
Next step will hook this up to, and redefine annotation engine interaction with UG for indel case.
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Added doc string for getNBoundRodTracks()
Intermediate commit for CalibrateGenotypeLikelihoods and GenotypeConcordanceTable, so I have a record of my work. Not ready for public consumption. Really looking forward to making local commits so I can track my progress without needing to push incomplete functionality up to the server.
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Using hapmap training and truth based on wiki.
Explicitly setting the ts_filter_level even though 99.0 is the default.
Recal file path now ends with with .recal.
Added ar's vcf input.
Omni rod name now omni instead of 1kg.
The VR RodBind tags had spaces in them.
Was passing both the full intervals and the chunk intervals to chunk jobs.
Switched back to chr20 for default since the VR crashes on small intervals sets with "MESSAGE: Matrix is singular."
Log files names based on the file paths + .out.
Added eval statifications by sample based on the Hybrid Selection / Whole Exome pipeline.
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Hardcoded the reference and dbsnp since the training rods are also hardcoded, for now.
Changed freeze/chr20 to wg/chr20/cent1 to also test the heaviest known shard.
Other cleanup.
TODO: Memory command line options or have the script figure it out using FLS or similar.
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FindLargeShards. Runtime of FindLargeShards on papuan dataset is now 75min.
GATK proper should benefit as well, although the benefits might be so small
as to not be measurable.
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BAM files looking for outliers (outliers right now are defined naively as
shards whose sizes are more than 5 stddevs away from the mean). Runs in
13 minutes per chromosome on 707 low pass whole genome BAMs -- not great, but
much faster than running UG on the same region to discover anomalies.
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