* fixed bug where some keys were using the same recal datum objects
* fixed quantization qual calculations when combining multiple reports
* fixed rounding error with empirical quality reported when combining reports
* fixed combine routine in the gatk reports due to the primary keys being out of order
* added auto-recalibration option to BQSR scala script
* reduced the size of the recalibration report by ~15%
* updated md5's
-- Molten now supports variableName and valueName so you don't have to use variable and value if you don't want to.
-- Cleanup code, reorganize a bit more.
-- Fix for broken integrationtests
*** 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
-- Added memory and safety optimizations to StratNode and StratificationManager. Fresh, immutable Hashmaps are allocated for final data structures, so they exactly the correct size and cannot be changed by users.
-- Added ability of a stratification to specify incompatible evaluation. The two strats using this are AC and Sample with VariantSummary, as this computes per-sample averages and so combining these results in an O(n^2) memory requirement. Added integration test to cover incompatible strats and evals
-- Renamed and reorganized infrastructure
-- StratificationManager now a Map from List<Object> -> V. All key functions are implemented. Less commonly used TODO
-- Ready for hookup to VE
-- Created a unit tested tree mapping from a List<String> -> integer (StratificationStates). This class is the key infrastructure necessary to create a complete static mapping from all stratification combinations to an offset in a vector of EvalutionContexts for update in map.
-- Minor code cleanup throughout VE (removing unused headers, for example)
VariantEval is overly abusive of the GATKReport (lack of) spec.
1. It converts numeric values (longs, integers and doubles) to string before sending to the Report, then expects it to decipher that those were actually numbers.
2. Worse, the stratification modules somehow instead of sending the actual values to the report table, sends a string with the value "unknown" and then abuses the GATKReport spec to convert those "unknown" placeholder values with numbers. Then again, it expects the report to know those are numbers, not strings.
Now that the GATKReport HAS specs, VariantEval needs to be overhauled to conform with that. In the meantime, I have added special ad-hoc treatment to these wrong contracts. It works, and the integration tests all passed without changing any MD5's, but right after Mark and Ryan commit their VariantEval refactors, I will step in to change the way it interacts with the GATKReport, so we can clean up the GATKReport.
No wonder, the printing needed to be O(n^2).
* when gathering, be aware that some keys will be missing from some tables.
* when a gatktable has no elements, it should still output the header so we know it had no records
- The Integer column type now accepts byte and shorts
- Updated Unit Tests and added a new testParse() test
Signed-off-by: Mauricio Carneiro <carneiro@broadinstitute.org>
* restructured the hash tables into one class (RecalibrationReport) that has all the functionality for the different tables and key managers
* optmized empirical qual calculation when merging recalibration reports
* centralized the quality score quantization functionalities
* unified the creating/loading of all the key manager/hash table structures.
* added unit tests for the gatherer (disabled because gatk report needs to be sorted for automated testing)
* added integration tests for BQSR and on-the-fly recalibration
-- Minor refactoring of state key iteration in VEW.map to make the dependencies more clear
-- Long discussion about the performance problems with StateKey, and how to fix it, which I have run out of time to address before ESP meeting.
-- The previous approach (requiring > 5 copies among all reads) is breaking down in many samples (>1000) just from sequencing errors.
-- This breakdown is producing spurious clustered indels (lots of these!) around real common indels
-- The new approach requires >X% of reads in a sample to carry an indel of any type (no allele matching) to be including in the counting towards 5. This actually makes sense in that if you have enough data we expect most reads to have the indel, but the allele might be wrong because of alignment, etc. If you have very few reads, then the threshold is crossed with any indel containing read, and it's counted.
-- As far as I can tell this is the right thing to do in general. We'll make another call set in ESP and see how it works at scale.
-- Added integration tests to ensure that the system is behaving as I expect on the site I developed the code on from ESP
-- StateKey no longer extends TreeMap. It's now a final immutable data structure that caches it's toString and hashcode values. TODO optimizations to entirely remove the TreeMap and just store the HashMap for performance and use the tree for the sorted tostring function.
-- NewEvaluationContext has a method makeStateKey() that contains all of the functionality that once was spread around VEUtils
-- AnalysisModuleScanner uses an annotationCache to speed up the reflections getAnnotations() call when invoked over and over on the same objects. Still expensive to convert each field to a string for the cache, but the only way around that is a complete refactoring of the toTransversalDone of VE
-- VariantEvaluator base class has a cached getSimpleName() function
-- VEUtils: general cleanup due to refactoring of StateKey
-- VEWalker: much better iteration of map data structures. If you need access to iterate over all key/value pairs use the Map.Entry construct with entrySet. This is far better than iterating over the keys and calling get() on each key.
-- Now the only use for update0, calculating the number of processed loci, is centrally tracked in the walker itself not the evaluations.
-- This allows us to avoid calling update0 are every genomic base in 100ks of evaluates when there are a lot of stratifications.
-- No need to modify the integration tests, this optimization doesn't change the result of the calculation
* added empirical quality counts to allow quantization during on-the-fly recalibration to any level
* added number of observations and errors to all tables to enable plotting of all covariates
* restructured BQSR to report recalibrated tables.
* implemented empirical quality calculation to the BQSR stage (instead of on-the-fly recalibration)
* linked quality score quantization to the BQSR stage, outputting a quantization histogram
* included the arguments used in BQSR to the GATK Report
* included all three tables (RG, QUAL and COVARIATES) to the GATK Report with empirical qualities
On-the-fly recalibration with GATK Report
* loads all tables from the GATKReport using existing infrastructure (with minor updates)
* implemented initialiazation of the covariates using BQSR's argument list
* reduced memory usage significantly by loading only the empirical quality and estimated quality reported for each bit set key
* applied quality quantization to the base recalibration
* excluded low quality bases from on-the-fly recalibration for mismatches, insertions or deletions
Mauricio Carneiro
Eric Banks
Guillermo del Angel
Mark DePristo
Ryan Poplin
Christopher Hartl
Roger Zurawicki