* 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
Returns true iff VC is an non-complex indel where every allele represents an expansion or
contraction of a series of identical bases in the reference.
The logic of this function is pretty simple. Take all of the non-null alleles in VC. For
each insertion allele of n bases, check if that allele matches the next n reference bases.
For each deletion allele of n bases, check if this matches the reference bases at n - 2 n,
as it must necessarily match the first n bases. If this test returns true for all
alleles you are a tandem repeat, otherwise you are not. Note that in this context n is the
base differences between the ref and alt alleles
* 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
* 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)
-- TODO for ryan -- there are bugs in ActivityProfile code that I cannot fix right now :-(
-- UnitTesting framework for ActivityProfile -- needs to be expanded
-- Minor helper functions for ActiveRegion to help with unit tests
Several of the unit tests for the new key authorization feature require
read access to the GATK master private key file. Since this file is only
readable by members of the group gsagit, this makes it hard for people
outside the group to run the test suite.
Now, we skip tests that require the master private key if the private
key exists (since not existing would be a true error) but is not readable
by the user running the test suite
Bamboo, of course, will always be able to run these tests.
-Running the GATK with the -et NO_ET or -et STDOUT options now
requires a key issued by us. Our reasons for doing this, and the
procedure for our users to request keys, are documented here:
http://www.broadinstitute.org/gsa/wiki/index.php/Phone_home
-A GATK user key is an email address plus a cryptographic signature
signed using our private key, all wrapped in a GZIP container.
User keys are validated using the public key we now distribute with
the GATK. Our private key is kept in a secure location.
-Keys are cryptographically secure in that valid keys definitely
came from us and keys cannot be fabricated, however keys are not
"copy-protected" in any way.
-Includes private, standalone utilities to create a new GATK user key
(GenerateGATKUserKey) and to create a new master public/private key
pair (GenerateKeyPair). Usage of these tools will be documented on
the internal wiki shortly.
-Comprehensive unit/integration tests, including tests to ensure the
continued integrity of the GATK master public/private key pair.
-Generation of new user keys and the new unit/integration tests both
require access to the GATK private key, which can only be read by
members of the group "gsagit".
* Turns DNA sequences (for context covariates) into bit sets for maximum compression
* Allows variable context size representation guaranteeing uniqueness.
* Works with long precision, so it is limited to a context size of 31 bases (can be extended with BigNumber precision if necessary).
* Unit Tests added
* added support to base before deletion in the pileup
* refactored covariates to operate on mismatches, insertions and deletions at the same time
* all code is in private so original BQSR is still working as usual in public
* outputs a molten CSV with mismatches, insertions and deletions, time to play!
* barely tested, passes my very simple tests... haven't tested edge cases.
* new unit tests for the alignment shift properties of reduce reads
* moved unit tests from ReadUtils that were actually testing GATKSAMRecord, not any of the ReadUtils to it.
* cleaned up ReadUtilsUnitTest
Eric reported this bug due to the reduced reads failing with an index out of bounds on what we thought was a deletion, but turned out to be a read starting with insertion.
* Refactored PileupElement to distinguish clearly between deletions and read starting with insertion
* Modified ExtendedEventPileup to correctly distinguish elements with deletion when creating new pileups
* Refactored most of the lazyLoadNextAlignment() function of the LocusIteratorByState for clarity and to create clear separation between what is a pileup with a deletion and what's not one. Got rid of many useless if statements.
* Changed the way LocusIteratorByState creates extended event pileups to differentiate between insertions in the beginning of the read and deletions.
* Every deletion now has an offset (start of the event)
* Fixed bug when LocusITeratorByState found a read starting with insertion that happened to be a reduced read.
* Separated the definitions of deletion/insertion (in the beginning of the read) in all UG annotations (and the annotator engine).
* Pileup depth of coverage for a deleted base will now return the average coverage around the deletion.
* Indel ReadPositionRankSum test now uses the deletion true offset from the read, changed all appropriate md5's
* The extra pileup elements now properly read by the Indel mode of the UG made any subsequent call have a different random number and therefore all RankSum tests have slightly different values (in the 10^-3 range). Updated all appropriate md5s after extremely careful inspection -- Thanks Ryan!
phew!
* if the adaptor boundary is more than MAXIMUM_ADAPTOR_SIZE bases away from the read, then let's not clip anything and consider the fragment to be undetermined for this read pair.
* updated md5's accordingly
* Knuth-shuffle is a simple, yet effective array permutator (hope this is good english).
* added a simple randomSubset that returns a random subset without repeats of any given array with the same probability for every permutation.
* added unit tests to both functions
* Modified cleanCigarShift to allow insertions in the beginning and end of the read
* Allowed cigars starting/ending in insertions in the systematic ReadClipper tests
* Updated all ReadClipper unit tests
* ReduceReads does not hard clip leading insertions by default anymore
* SlidingWindow adjusts start location if read starts with insertion
* SlidingWindow creates an empty element with insertions to the right
* Fixed all potential divide by zero with totalCount() (from BaseCounts)
* Updated all Integration tests
* Added new integration test for multiple interval reducing
Guillermo del Angel
Eric Banks
Mauricio Carneiro
Mark DePristo
Ryan Poplin
David Roazen
Khalid Shakir
Matt Hanna