-- VisualizeContextTree now can write out an equivalent BQSR table determined after adaptive context merging of all RG x QUAL x CONTEXT trees
-- Docs, algorithm descriptions, etc so that it makes sense what's going on
-- VisualizeContextTree should really be simplified when into a single tool that just visualize the trees when / if we decide to make adaptive contexts standard part of BQSR
-- Misc. cleaning, organization of the code (recalibation tests were in private but corresponding actual files were public)
-- Uses chi2 test for independences to determine if subcontext is worth representing. Give excellent visual results
-- Writes out analysis output file producing excellent results in R
-- Trivial reformatting of MathUtils
-- Reorganize functions in RecalDatum so that error rate can be computed indepentently. Added unit tests. Removed equals() method, which is a buggy without it's associated implementation for hashcode
-- New class RecalDatumTree based on QualIntervals that inherits from RecalDatum but includes the concept of sub data
-- VisualizeContextTree now uses RecalDatumTree and can trivially compute the penalty function for merging nodes, which it displays in the graph
-- Moved most of BQSR classes (which are used throughout the codebase) to utils.recalibration. It's better in my opinion to keep commonly used code in utils, and only specialized code in walkers. As code becomes embedded throughout GATK its should be refactored to live in utils
-- Removed unncessary imports of BQSR in VQSR v3
-- Now ready to refactor QualQuantizer and unit test into a subclass of RecalDatum, refactor unit tests into RecalDatum unit tests, and generalize into hierarchical recal datum that can be used in QualQuantizer and the analysis of adaptive context covariate
-- Update PluginManager to sort the plugins and interfaces. This allows us to have a deterministic order in which the plugin classes come back, which caused BQSR integration tests to temporarily change because I moved my classes around a bit.
The practical differences between version 1.0 and this one (v1.1) are:
* the underlying data structure now uses arrays instead of hashes, which should drastically reduce the memory overhead required to create large tables.
* no more primary keys; you can still create arbitrary IDs to index into rows, but there is no special cased primary key column in the table.
* no more dangerous/ugly table operations supported except to increment a cell's value (if an int) or to concatenate 2 tables.
Integration tests change because table headers are different.
Old classes are still lying around. Will clean those up in a subsequent commit.
* fixed context covariate famous "off by one" error
* reduced maximum quality score to Q50 (following Eric/Ryan's suggestion)
* remove context downsampling in BQSR R script
This test brings together the old and the new BQSR, building a recalibration table using the two separate frameworks and performing the recalibration calculation using the two different frameworks for 10,000+ bases and asserting that the calculations match in every case.
* removed low quality bases from the recalibration report.
* refactored the Datum (Recal and Accuracy) class structure
* created a new plotting csv table for optimized performance with the R script
* added a datum object that carries the accuracy information (AccuracyDatum) for plotting
* added mean reported quality score to all covariates
* added QualityScore as a covariate for plotting purposes
* added unit test to the key manager to operate with one required covariate and multiple optional covariates
* integrated the plotting into BQSR (automatically generates the pdf with the recalibration tearsheet)
* Added parameter -qq to quantize qualities using a recalibration report
* Added options to quantize using the recalibration report quantization levels, new nLevels and no quantization.
* Updated BQSR scripts to make use of the new parameters
* 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
* 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
Infrastructure:
* Added static interface to all different clipping algorithms of low quality tail clipping
* Added reverse direction pileup element event lookup (indels) to the PileupElement and LocusIteratorByState
* Complete refactor of the KeyManager. Much cleaner implementation that handles keys with no optional covariates (necessary for on-the-fly recalibration)
* EventType is now an independent enum with added capabilities. All functionality is now centralized.
BQSR and RecalibrateBases:
* On-the-fly recalibration is now generic and uses the same bit set structure as BQSR for a reduced memory footprint
* Refactored the object creation to take advantage of the compact key structure
* Replaced nested hash maps with single hash maps indexed by bitsets
* Eliminated low quality tails from the context covariate (using ReadClipper's write N's algorithm).
* Excluded contexts with N's from the output file.
* Fixed cycle covariate for discrete platforms (need to check flow cycle platforms now!)
* Redfined error for indels to look at the previous base in negative strand reads (using new PE functionality)
* Added the covariate ID (for optional covariates) to the output for disambiguation purposes
* Refactored CovariateKeySet -- eventType functionality is now handled by the EventType enum.
* Reduced memory usage of the BQSR script to 4
Tests:
* Refactored BQSRKeyManagerUnitTest to handle the new implementation of the key manager
* Added tests for keys without optional covariates
* Added tests for on-the-fly recalibration (but more tests are necessary)
Infrastructure:
* Generic BitSet implementation with any precision (up to long)
* Two's complement implementation of the bit set handles negative numbers (cycle covariate)
* Memoized implementation of the BitSet utils for better performance.
* All exponents are now calculated with bit shifts, fixing numerical precision issues with the double Math.pow.
* Replace log/sqrt with bitwise logic to get rid of numerical issues
BQSR:
* All covariates output BitSets and have the functionality to decode them back into Object values.
* Covariates are responsible for determining the size of the key they will use (number of bits).
* Generalized KeyManager implementation combines any arbitrary number of covariates into one bitset key with event type
* No more NestedHashMaps. Single key system now fits in one hash to reduce hash table objects overhead
Tests:
* Unit tests added to every method of BitSetUtils
* Unit tests added to the generalized key system infrastructure of BQSRv2 (KeyManager)
* Unit tests added to the cycle and context covariates (will add unit tests to all covariates)
Mark DePristo
Eric Banks
Mauricio Carneiro
Ryan Poplin