-- Idea is simply to create a persistent database of all TP/FP sites on chr20 in NA12878. Individual callsets can be imported, and a consensus algorithm is run over all callsets in the database to create a consensus collection, which can be used to assess NA12878 callsets for GATK and methods development
-- Framework for representing simple VariantContexts and Genotypes in MongoDB, querying for records, and iterating over them in the GATK
-- Not hooked up to Tribble, but could be done reasonably easily now (future TODO)
-- Tools to import callsets, create consensus callsets, import and export reviews
-- Scripts to reset the knowledge base and repopulate it with the standard data files (Eric will expand)
-- Actually scales to all of chr20, includes AssessNA12878 that reads a VCF and itemizes it against the truth data set
-- ImportCallset can load OMNI, HM3, CEU best practices, mills/devine sites and genotypes, properly marking sites as poly/mono/unk as well as TP/FP/UNK based on command line parameters
-- Added shell scripts that start up a local mongo db, that connect to a local or BI hosted mongo for NA12878.db for debugging, and a setupNA12878db script that can load OMNI, HM3, CEU best practices, Mills/Devine into the db and then update the consensus.
-- Reviewed sites can be exported to a VCF, and imported again, as a mechanism to safely store the only non-recoverable data from the Mongo DB.
-- Created a NA12878DBWalker that manages the outer DB interaction, and that all MongoDB interacting walkers inherit from. Added a NA12878DBArgumentCollection.java consolating all of the common command line arguments (though strictly not necessary as all of this occurs in the root walker)
UnitTests
-- Can connect to a test knowledge base for development and unit testing
-- PolymorphicStatus, TruthStatus, SiteIterator
-- NA12878KBUnitTestBase provides simple utilities for connecting to the test mongo db, getting calls, etc
-- MongoVariantContext tests creation, matching, and encoding -> writing -> read -> decoding from the mongodb
AssessNA12878
-- Generic tool for comparing a NA12878 callset against the knowledge base. See http://gatkforums.broadinstitute.org/discussion/1848/using-the-na12878-knowledge-base for detailed documentation
-- Performs trivial filtering on FS, MQ, QD for SNPs and non-SNPs to separate out variants likely to be filtered from those that are honest-to-goodness FPs
Misc
-- Ability to provide Description for Simplified GATK report
-- UnifiedGenotyperEngine no longer keeps a thread local double[2] array for the normalized posteriors array. This is way heavy-weight compared to just making the array each time.
-- Added getNormalizedPosteriorOfAFGTZero and getNormalizedPosteriorOfAFzero to AFResult object. That's the place it should really live
-- Add tests for priors, uncovering bugs in the contracts of the tri-allelic priors w.r.t. the AC of the MAP. Added TODOs
-- Groups inputs for each thread so that we don't have one thread execution per map() call
-- Added shutdown function
-- Documentation everywhere
-- Code cleanup
-- Extensive unittests
-- At this point I'm ready to integrate it into the engine for CPU parallel read walkers
-- BCF2 now determines whether it can safely write out raw genotype blocks, which is true in the case where the VCF header of the input is a complete, ordered subset of the output header. Added utilities to determine this and extensive unit tests (headerLinesAreOrderedConsistently)
-- Cleanup collapseStringList and exploreStringList for new unit tests of BCF2Utils. Fixed bug in edge case that never occurred in practice
-- VCFContigHeaderLine now provides its own key (VCFHeader.CONTIG_KEY) directly instead of requiring the user to provide it (and hoping its right)
-- More ways to access the data in VCFHeader
-- BCF2Writer uses a cache to avoid recomputing unnecessarily whether raw genotype blocks can be emitted directly into the output
-- Optimization of fullyDecodeAttributes -- attributes.size() is expensive and unnecessary. We just guess that on average we need ~10 elements for the attribute map
-- CombineVariants optimization -- filters are online HashSet but are sorted at the end by creating a TreeSet
-- makeCombinations is now makePermutations, and you can request to create the permutations with or without replacement
-- Now only includes leaf nodes in the summary, i.e., summaries of the form "*.*....*.X", which are really the most valuable to see. This calculation can be accomplished in linear time for N differences, rather than the previous O(n^2) algorithm
-- Now computes the max number of elements to read correctly. Counts now the size of the entire element tree, not just the count of the roots, which was painful because the trees vary by orders of magnitude in size.
-- Because of this we can enforce a meaningful, useful value for the max elements in MD5 or 100K, and this works well.
-- Added integration test for new leaf and old pairwise calculations
-- Bugfix for Utils.join(sep, int[]) that was eating the first element of the AD, PL fields
-- Created a new Genotype interface with a more limited set of operations
-- Old genotype object is now SlowGenotype. New genotype object is FastGenotype. They can be used interchangable
-- There's no way to create Genotypes directly any longer. You have to use GenotypeBuilder just like VariantContextBuilder
-- Modified lots and lots of code to use GenotypeBuilder
-- Added a temporary hidden argument to engine to use FastGenotype by default. Current default is SlowGenotype
-- Lots of bug fixes to BCF2 codec and encoder.
-- Feature additions
-- Now properly handles BCF2 -> BCF2 without decoding or encoding from scratch the BCF2 genotype bytes
-- Cleaned up semantics of subContextFromSamples. There's one function that either rederives or not the alleles from the subsetted genotypes
-- MASSIVE BUGFIX in SelectVariants. The code has been decoding genotypes always, even if you were not subsetting down samples. Fixed!
-- Not hooked up yet, so the output of VariantEval should be the same as before
-- Implemented a VariantEvalUnitTest that tests the low level strat / eval combinatorics and counting routines
-- Better docs throughout
*** WAY FASTER ***
-- 3x performance for multiple sample analysis with 1000 samples
-- Analyzing 1MB of the ESP call set (3100 samples) takes 40 secs, compared to several minutes in the previous version
-- According to JProfiler all of the runtime is now spent decoding genotypes, which will only get better when we move to BCF2
-- Remove the TableType system, as this was way too complex. No longer possible to embed what were effectively multiple tables in a single Evaluator. You now have to have 1 table per eval
-- Replaced it with @Molten, which allows an evaluator to provide a single Map from variable -> value for analysis. IndelLengthHistogram is now a @Molten data type. GenotypeConcordance is also.
-- No longer allow Evaluators to use private and protected variables at @DataPoints. You get an error if you do.
-- Simplified entire IO system of VE. Refactored into VariantEvalReportWriter.
-- Commented out GenotypePhasingEvaluator, as it uses the retired TableType
-- Stratifications are all fully typed, so it's easy for GATKReports to format them.
-- Removed old VE work around from GATKReportColumn
-- General code cleanup throughout
-- Updated integration tests
Pulled out the functionality from Indel Realigner and Table Recalibrator into Utils.setupWriter to make everyone else's life's easier if they want to include the PG tag in their walkers.
* expanded the systematic cigar string space test framework Roger wrote to all tests
* moved utility functions into Utils and ReadUtils
* cleaned up unused classes
-- scatterLocusIntervals master utility
-- Moved around some general functionality from GenomeLocSortedSet to GenomeLoc
-- Util function for reversing a list (List<T> -> List<T>, unlike Collections version)
-- DoC is PartitionType.INTERVAL
-- Significant unit tests on new functionality (all passing)
-- Ready for real-world testing, as soon as I can get LocusScatterFunction.scala to actually work
initial size for a Java hash table (HashMap, HashSet, etc.) given an expected
maximum number of elements. The optimum size is the smallest size that's
guaranteed not to result in any rehash / table-resize operations.
Example Usage:
Map<String, Object> hash = new HashMap<String, Object>(Utils.optimumHashSize(expectedMaxElements));
I think we're paying way too heavy a price in unnecessary rehash operations across
the GATK. If you don't specify an initial size, you get a table of size 16 that gets
completely rehashed and doubles in size every time it becomes 75% full. This means you
do at least twice as much work as you need to in order to populate your table:
(n + n/2 + n/4 + ... 16 ~= (1 + 1/2 + 1/4...) * n ~= 2 * n
Mauricio Carneiro
Mark DePristo
Eric Banks
Khalid Shakir
David Roazen