This code eventually needs to end up in the VariantFiltration system - when we are ready to parameterize on the fly.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1528 348d0f76-0448-11de-a6fe-93d51630548a
| 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.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1526 348d0f76-0448-11de-a6fe-93d51630548a
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.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1509 348d0f76-0448-11de-a6fe-93d51630548a
Also, it's safer to let colt do the log factorial calculations for us.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1503 348d0f76-0448-11de-a6fe-93d51630548a
The user is warned if a locus exceeds this threshold, and no more reads are added.
Also CombineDup walker had an incorrect package name.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1496 348d0f76-0448-11de-a6fe-93d51630548a
This now allows us to incorporate both the clustered SNP filter and a SNP-near-indels filter, which otherwise wasn't possible.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1484 348d0f76-0448-11de-a6fe-93d51630548a
@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.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1462 348d0f76-0448-11de-a6fe-93d51630548a
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...
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1414 348d0f76-0448-11de-a6fe-93d51630548a
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)
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1411 348d0f76-0448-11de-a6fe-93d51630548a
- 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)
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1388 348d0f76-0448-11de-a6fe-93d51630548a
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
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1369 348d0f76-0448-11de-a6fe-93d51630548a
- 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
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1363 348d0f76-0448-11de-a6fe-93d51630548a
Note that nothing was changed for dnSNP/hapmap priors (not sure what we want to do with these yet - any thoughts?)
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1342 348d0f76-0448-11de-a6fe-93d51630548a
- 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...
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1341 348d0f76-0448-11de-a6fe-93d51630548a
sources and post-construction validation back into the GATKEngine, leaving the MicroScheduler
to just microschedule.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1336 348d0f76-0448-11de-a6fe-93d51630548a
ebanks
chartl
sjia
aaron
asivache
kiran
jmaguire
depristo
kcibul
hanna
mmelgar
andrewk