but mostly altering the code so it's much more readable and understandable, and much less hacky-looking.
ADDED:
@Quad: This is just like Pair, except with four fields. In the original CoverageAndPowerWalker I often used
a pair of pairs to hold things, which made the code nigh unreadable.
@SQuad: An extension of Quad for when you want to store objects of the same type. Let's you simply declare
new SQuad<X> rather than new Quad<X,X,X,X>
@ReadOffsetQuad: An extension of Quad specifically for holding two lists of reads and two lists of offsets
Supports construction from AlignmentContexts and conversion to AlignmentContexts (given
a GenomeLoc). There are methods that make it very clear what the code is doing (getSecondRead()
rather than the cryptic getThird() )
@PowerAndCoverageWalker: The new version of CoverageAndPowerWalker. If the tests all go well, then I'll remove
the old version. New to this version is the ability to give an output file directly
to the walker, so that locus information prints to the file, while the final reduce
prints to standard out. Bootstrap iterations are now a command line argument rather
than a final int; and users can instruct the walker to print out the coverage/power
statistics for both the original reads, and those reads whose quality score exceeds
a user-defined threshold.
CHANGES:
@PoolUtils: Altered methods to accept as argumetns, and return, Quad objects. Added a random partition method
for bootstrapping.
@CoverageAndPowerWalker: Altered methods to work with the new PoolUtils methods.
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Rollback of Variant-related changes of r1585, additional PGC code
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@VariantEvalWalker - added a command line option to input a file path to a pooled call file for pooled genotype concordance checking. This string is to be passed to the PooledGenotypeConcordance object.
@AllelicVariant - added a method isPooled() to distinguish pooled AllelicVariants from unpooled ones.
@ all the rest - implemented isPooled(); for everything other than PooledEMSNProd it simply returns false, for PooledEMSNProd it returns true.
Added:
@PooledGenotypeConcordance - takes in a filepath to a pool file with the names of hapmap individuals for concordance checking with pooled calls
and does said concordance checking over all pools. Commented out as all the methods are as yet unwritten.
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After consulting our resident expert (Aaron), we're going to (temporarily) remove the date from the vcf output until we can come up with a better solution. However, this shouldn't cause any short-term problems because the data truly is optional.
VF test's MD5s are updated.
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1. VariantsToVCF can now be called statically to output VCF for a single ROD instance; this is temporary until we have a VCF ROD.
2. VariantFiltration now outputs only 2 files, both mandatory: all variants that pass filters in geli text, and all variants in VCF.
If there are any problems, go find Aaron.
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@CoverageAndPowerWalker - removed a hanging colon that was being printed after the reference position
@VariantEvalWalker - added a command line argument for pool size for eventual use in doing pooled caller evaluations. As now, the variable is unused.
@AlignmentContext - altered the scope of class variables from private to protected in order that child objects might have access to them
New Additions:
Filtered Contexts
Sometimes we want to filter or partition reads by some aspect (quality score, read direction, current base, whatever) and use only those reads as
part of the alignment context. Prior to this I've been doing the split externally and creating a new AlignmentContext object. This new approach makes
it a bit easier, as each of these objects are children of AlignmentContext, and can be instantiated from a "raw" AlignmentContext.
@FilteredAlignmentContext is an abstract class that defines the behavior. The abstract method 'filter' is called on the input AlignmentContext, filtering
those reads and offsets by whatever you can think of. The filtered reads/offsets are then maintained in the reads and offsets fields. These classes can
be passed around as AlignmentContexts themselves. Writing a new kind of read-filtered alignment context boils down to implementing the filter method.
@ReverseReadsContext - a FilteredAlignmentContext that takes only reads in the reverse direction
@ForwardReadsContext - a FilteredAlignmentContext that takes only reads in the forward direction
@QualityScoreThresholdContext - a FilteredAlignmentContext that takes only reads above a given quality score threshold (defaults to 22 if none provided).
A unit test bamfile and associated unit tests for these are in the works.
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Also fixed the case where MD5 sums had leading zero's clipped off
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1) Moved various disparate concordance / set splitting functionalities to a new parent tool which works like VariantFiltration (i.e. people can write various modules that fit inside and can be run though it).
2) Fixed up argument parsing in VariantFiltration to use key=value format so we don't accidentally mox up values (like I had been doing).
3) Have indel rod print samples
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-Remove KGenomesSNPROD
-Remove rodFLT
-Renamed rodGFF to RodGenotypeChipAsGFF
-Fixed a problem in SSGenotypeCall
-Added basic SSGenotype Test class
-Make VCFHeader constructors public
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1536 348d0f76-0448-11de-a6fe-93d51630548a
I'll move this all to core in a bit...
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This code eventually needs to end up in the VariantFiltration system - when we are ready to parameterize on the fly.
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| Added items |
---------------
@/varianteval/PoolAnalysis
Interface to identify variant analyses that are pool-specific.
@/varianteval/BasicPoolVariantAnalysis
Nearly the same as BasicVariantAnalysis with the addition of a protected integer (numIndividualsInPool)
which holds the pool size. One soulcrushing change is that "protected String filename" needed to
become "protected String[] filename" since now multiple truth files may be looked at. It was tempting
to make the change in BasicVariantAnalysis with some default methods that would maintain usability of
the remainder of the VariantAnalysis objects, but I decided to hold off. We can always merge these
together later.
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Doesn't work yet; same command I used to use now causes GATK to throw an exception.
Will check with Matt & Aaron tomorrow, then do a regression test.
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Also, it's safer to let colt do the log factorial calculations for us.
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The user is warned if a locus exceeds this threshold, and no more reads are added.
Also CombineDup walker had an incorrect package name.
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This now allows us to incorporate both the clustered SNP filter and a SNP-near-indels filter, which otherwise wasn't possible.
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@MathUtils - added a new method: cumBinomialProbLog which calculates a cumulant from any start point to any end point using the BinomProbabilityLog calculation.
@PoolUtils - added a new utility class specifically for items related to pooled sequencing. A major part of the power calculation is now to calculate powers
independently by read direction. The only method in this class (currently) takes your reads and offsets, and splits them into two groups
by read direction.
@CoverageAndPowerWalker - completely rewritten to split coverage, median qualities, and power by read direction. Makes use of cumBinomialProbLog rather than
doing that calculation within the object itself.
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Minor changes to CoverageAndPowerWalker bootstrapping (faster selection of indeces).
Entirely new Aritifical Pool Walker (ArtificialPoolWalkerMk2), will likely replace ArtificialPoolWalker on the next commit. Adapted the method of sampling, and added a helper context class: ArtificialPoolContext which carries much of the burden of calculation and data handling for the walker. The walker itself maps and reduces ArtificialPoolContexts.
Cheers!
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1461 348d0f76-0448-11de-a6fe-93d51630548a
binomialProbabilityLog uses a log-space calculation of the
binomial pmf to avoid the coefficient blowing up and thus
returning Infinity or NaN (or in some very strange cases
-Infinity). The log calculation compares very well, it seems
with our current method. It's in MathUtils but could stand
testing against rigorous truth data before becoming standard.
Added median calculator functions to ListUtils
getQScoreMedian is a new utility I wrote that given reads and
offsets will find the median Q score. While I was at it, I wrote
a similar method, getMedian, which will return the median of any
list of Comparables, independent of initial order. These are in
ListUtils.
Added a new poolseq directory and three walkers
CoverageAndPowerWalker is built on top of the PrintCoverage walker
and prints out the power to detect a mutant allele in a pool of
2*(number of individuals in the pool) alleles. It can be flagged
either to do this by boostrapping, or by pure math with a
probability of error based on the median Q-score. This walker
compiles, runs, and gives quite reasonable outputs that compare
visually well to the power calculation computed by Syzygy.
ArtificialPoolWalker is designed to take multiple single-sample
.bam files and create a (random) artificial pool. The coverage of
that pool is a user-defined proportion of the total coverage over
all of the input files. The output is not only a new .bam file,
but also an auxiliary file that has for each locus, the genotype
of the individuals, the confidence of that call, and that person's
representation in the artificial pool .bam at that locus. This
walker compiles and, uhh, looks pretty. Needs some testing.
AnalyzePowerWalker extends CoverageAndPowerWalker so that it can read previous power
calcuations (e.g. from Syzygy) and print them to the output file as well for direct
downstream comparisons.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1460 348d0f76-0448-11de-a6fe-93d51630548a
This is useful when producing Sequenom input files for validating indels...
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Picard has such a tool but it is geared towards their pipeline and requires intimate knowledge of the lanes/flowcells,etc. This is just easy.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1413 348d0f76-0448-11de-a6fe-93d51630548a
Also: do not die if alignment record does not have NM tags (but mapping quality will not be recomputed after remapping/reducing for the lack of required data)
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- added first version of a more complicated reference maker which takes in RODs and creates an alternative reference based on the variants (indels and/or SNPs)
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1409 348d0f76-0448-11de-a6fe-93d51630548a
* splitting off only start or end of the read, or both; the output will contain
chopped sequences AND corresponding base qualities
* splitting arbitrary number of bases off each end (different numbers
for left and right segments can be specified; segments can overlap)
* splitting only unmapped reads, ignoring mapped ones
* writing splitted ends into separate sam/bam files, or into a single output file
* decorating original read names with user-specified suffixes for each end
(e.g. _1 and _2 for left and right parts of the read); default: no decoration,
original read names are used
* when mapped reads are split, the alignment cigars are chopped appropriately
and the alignment start positions are adjusted (for the right end) to correctly
specify the alignment of the selected part of the read
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1402 348d0f76-0448-11de-a6fe-93d51630548a
-Set some reasonable defaults (based on pilot2)
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from an array of bases to an object (ReferenceContext), and LocusContext has been renamed to reflect
the fact that it contains contextual information only about the alignments, not the locus in general.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1376 348d0f76-0448-11de-a6fe-93d51630548a
-move out isHet test to GenotypeUtils so all can use it
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- make the filters brainless in that they strictly have thresholds and filter based on them; require user to calculate and input these thresholds.
- update filters in preparation for migration to new output format
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Note that nothing was changed for dnSNP/hapmap priors (not sure what we want to do with these yet - any thoughts?)
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- fixed math bug (pValue needs to be initialized to pCutoff, not 0)
- perform factorial calculations in log space so that huge numbers don't explode
- cache factorial calculations so that each value needs to be computed just once for any given instance of the filter
I've tested it against R and it has held up so far...
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sources and post-construction validation back into the GATKEngine, leaving the MicroScheduler
to just microschedule.
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- SSG is much simpler now
- GeliText has been added as a GenotypeWriter
- AlleleFrequencyWalker will be deleted when I untangle the AlleleMetric's dependance on it
- GenotypeLikelihoods now implements GenotypeGenerator, but could still use cleanup
There is still a lot more work to do, but this is a good initial check-in.
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rough initial implementation, but should provide enough support so that people can stop
creating SAMFileWriters in reduceInit.
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-variants need a length method (can't assume it's a SNP)!
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2. free DB coverage from isSNP restriction
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the string (unfortunately) needs to be sorted for lookup in the table (otherwise we throw a NullPointerException)
TO DO: have the table be smarter instead of sorting each genotype string
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inspecting the source tree and loading walkers, rather than trying to roll
our own by hand.
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the RODs it sees. This is the easiest solution to get around the (temporary)
bug of reads being seen multiple times by reads walkers when close intervals
are passed to them (i.e. process full contigs and then use a ref walker to
filter the ones within your intervals of choice)
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This should work, although I need to test it with some real GLFs
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Yes, the name is retarded, but I'm under pressure here...
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-Refactored strand filter to handle calls from the walker
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(since there's less area to actually clean against)
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chartl
andrewk
ebanks
asivache
depristo
aaron
sjia
kiran
jmaguire
kcibul
hanna
mmelgar