/** Collect relevant information from each variant in the supplied VCFs */
@Override
public Integer map(RefMetaDataTracker tracker, ReferenceContext ref, AlignmentContext context) {
// we track the processed bp and expose this for modules instead of wasting CPU power on
// calculating
// the same thing over and over in evals that want the processed bp
synchronized (this) {
nProcessedLoci += context.getSkippedBases() + (ref == null ? 0 : 1);
}
if (tracker != null) {
String aastr =
(ancestralAlignments == null)
? null
: new String(
ancestralAlignments
.getSubsequenceAt(
ref.getLocus().getContig(),
ref.getLocus().getStart(),
ref.getLocus().getStop())
.getBases());
// // update the dynamic stratifications
// for (final VariantContext vc : tracker.getValues(evals, ref.getLocus())) {
// // don't worry -- DynamicStratification only work with one eval object
// for ( final DynamicStratification ds : dynamicStratifications ) {
// ds.update(vc);
// }
// }
// --------- track --------- sample - VariantContexts -
HashMap<RodBinding<VariantContext>, HashMap<String, Collection<VariantContext>>> evalVCs =
variantEvalUtils.bindVariantContexts(
tracker, ref, evals, byFilterIsEnabled, true, perSampleIsEnabled, mergeEvals);
HashMap<RodBinding<VariantContext>, HashMap<String, Collection<VariantContext>>> compVCs =
variantEvalUtils.bindVariantContexts(
tracker, ref, comps, byFilterIsEnabled, false, false, false);
// for each eval track
for (final RodBinding<VariantContext> evalRod : evals) {
final Map<String, Collection<VariantContext>> emptyEvalMap = Collections.emptyMap();
final Map<String, Collection<VariantContext>> evalSet =
evalVCs.containsKey(evalRod) ? evalVCs.get(evalRod) : emptyEvalMap;
// for each sample stratifier
for (final String sampleName : sampleNamesForStratification) {
Collection<VariantContext> evalSetBySample = evalSet.get(sampleName);
if (evalSetBySample == null) {
evalSetBySample = new HashSet<VariantContext>(1);
evalSetBySample.add(null);
}
// for each eval in the track
for (VariantContext eval : evalSetBySample) {
// deal with ancestral alleles if requested
if (eval != null && aastr != null) {
eval = new VariantContextBuilder(eval).attribute("ANCESTRALALLELE", aastr).make();
}
// for each comp track
for (final RodBinding<VariantContext> compRod : comps) {
// no sample stratification for comps
final HashMap<String, Collection<VariantContext>> compSetHash = compVCs.get(compRod);
final Collection<VariantContext> compSet =
(compSetHash == null || compSetHash.size() == 0)
? Collections.<VariantContext>emptyList()
: compVCs.get(compRod).values().iterator().next();
// find the comp
final VariantContext comp = findMatchingComp(eval, compSet);
for (EvaluationContext nec :
getEvaluationContexts(
tracker, ref, eval, evalRod.getName(), comp, compRod.getName(), sampleName)) {
// eval against the comp
synchronized (nec) {
nec.apply(tracker, ref, context, comp, eval);
}
// eval=null against all comps of different type that aren't bound to another eval
for (VariantContext otherComp : compSet) {
if (otherComp != comp && !compHasMatchingEval(otherComp, evalSetBySample)) {
synchronized (nec) {
nec.apply(tracker, ref, context, otherComp, null);
}
}
}
}
}
}
}
if (mergeEvals) break; // stop processing the eval tracks
}
}
return null;
}
/**
* General-purpose tool for variant evaluation (% in dbSNP, genotype concordance, Ti/Tv ratios, and
* a lot more)
*
* <p>Given a variant callset, it is common to calculate various quality control metrics. These
* metrics include the number of raw or filtered SNP counts; ratio of transition mutations to
* transversions; concordance of a particular sample's calls to a genotyping chip; number of
* singletons per sample; etc. Furthermore, it is often useful to stratify these metrics by various
* criteria like functional class (missense, nonsense, silent), whether the site is CpG site, the
* amino acid degeneracy of the site, etc. VariantEval facilitates these calculations in two ways:
* by providing several built-in evaluation and stratification modules, and by providing a framework
* that permits the easy development of new evaluation and stratification modules.
*
* <h2>Input</h2>
*
* <p>One or more variant sets to evaluate plus any number of comparison sets.
*
* <h2>Output</h2>
*
* <p>Evaluation tables detailing the results of the eval modules which were applied. For example:
*
* <pre>
* output.eval.gatkreport:
* ##:GATKReport.v0.1 CountVariants : Counts different classes of variants in the sample
* CountVariants CompRod CpG EvalRod JexlExpression Novelty nProcessedLoci nCalledLoci nRefLoci nVariantLoci variantRate ...
* CountVariants dbsnp CpG eval none all 65900028 135770 0 135770 0.00206024 ...
* CountVariants dbsnp CpG eval none known 65900028 47068 0 47068 0.00071423 ...
* CountVariants dbsnp CpG eval none novel 65900028 88702 0 88702 0.00134601 ...
* CountVariants dbsnp all eval none all 65900028 330818 0 330818 0.00502000 ...
* CountVariants dbsnp all eval none known 65900028 120685 0 120685 0.00183133 ...
* CountVariants dbsnp all eval none novel 65900028 210133 0 210133 0.00318866 ...
* CountVariants dbsnp non_CpG eval none all 65900028 195048 0 195048 0.00295976 ...
* CountVariants dbsnp non_CpG eval none known 65900028 73617 0 73617 0.00111710 ...
* CountVariants dbsnp non_CpG eval none novel 65900028 121431 0 121431 0.00184265 ...
* ...
* </pre>
*
* <h2>Examples</h2>
*
* <pre>
* java -Xmx2g -jar GenomeAnalysisTK.jar \
* -R ref.fasta \
* -T VariantEval \
* -o output.eval.gatkreport \
* --eval:set1 set1.vcf \
* --eval:set2 set2.vcf \
* [--comp comp.vcf]
* </pre>
*/
@DocumentedGATKFeature(
groupName = HelpConstants.DOCS_CAT_VARMANIP,
extraDocs = {CommandLineGATK.class})
@Reference(window = @Window(start = -50, stop = 50))
@PartitionBy(PartitionType.NONE)
public class VariantEval extends RodWalker<Integer, Integer> implements TreeReducible<Integer> {
public static final String IS_SINGLETON_KEY = "ISSINGLETON";
@Output protected PrintStream out;
/** The variant file(s) to evaluate. */
@Input(fullName = "eval", shortName = "eval", doc = "Input evaluation file(s)", required = true)
public List<RodBinding<VariantContext>> evals;
/** The variant file(s) to compare against. */
@Input(fullName = "comp", shortName = "comp", doc = "Input comparison file(s)", required = false)
public List<RodBinding<VariantContext>> compsProvided = Collections.emptyList();
private List<RodBinding<VariantContext>> comps = new ArrayList<RodBinding<VariantContext>>();
/**
* dbSNP comparison VCF. By default, the dbSNP file is used to specify the set of "known"
* variants. Other sets can be specified with the -knownName (--known_names) argument.
*/
@ArgumentCollection protected DbsnpArgumentCollection dbsnp = new DbsnpArgumentCollection();
/**
* Some analyses want to count overlap not with dbSNP (which is in general very open) but actually
* want to itemize their overlap specifically with a set of gold standard sites such as HapMap,
* OMNI, or the gold standard indels. This argument provides a mechanism for communicating which
* file to use
*/
@Input(
fullName = "goldStandard",
shortName = "gold",
doc =
"Evaluations that count calls at sites of true variation (e.g., indel calls) will use this argument as their gold standard for comparison",
required = false)
public RodBinding<VariantContext> goldStandard = null;
/** Note that the --list argument requires a fully resolved and correct command-line to work. */
@Argument(
fullName = "list",
shortName = "ls",
doc = "List the available eval modules and exit",
required = false)
protected Boolean LIST = false;
// Partitioning the data arguments
@Argument(
shortName = "select",
doc = "One or more stratifications to use when evaluating the data",
required = false)
protected ArrayList<String> SELECT_EXPS = new ArrayList<String>();
@Argument(
shortName = "selectName",
doc = "Names to use for the list of stratifications (must be a 1-to-1 mapping)",
required = false)
protected ArrayList<String> SELECT_NAMES = new ArrayList<String>();
@Argument(
fullName = "sample",
shortName = "sn",
doc =
"Derive eval and comp contexts using only these sample genotypes, when genotypes are available in the original context",
required = false)
protected Set<String> SAMPLE_EXPRESSIONS;
/** List of rod tracks to be used for specifying "known" variants other than dbSNP. */
@Argument(
shortName = "knownName",
doc =
"Name of ROD bindings containing variant sites that should be treated as known when splitting eval rods into known and novel subsets",
required = false)
protected HashSet<String> KNOWN_NAMES = new HashSet<String>();
List<RodBinding<VariantContext>> knowns = new ArrayList<RodBinding<VariantContext>>();
// Stratification arguments
@Argument(
fullName = "stratificationModule",
shortName = "ST",
doc =
"One or more specific stratification modules to apply to the eval track(s) (in addition to the standard stratifications, unless -noS is specified)",
required = false)
protected String[] STRATIFICATIONS_TO_USE = {};
@Argument(
fullName = "doNotUseAllStandardStratifications",
shortName = "noST",
doc =
"Do not use the standard stratification modules by default (instead, only those that are specified with the -S option)",
required = false)
protected Boolean NO_STANDARD_STRATIFICATIONS = false;
/** See the -list argument to view available modules. */
@Argument(
fullName = "evalModule",
shortName = "EV",
doc =
"One or more specific eval modules to apply to the eval track(s) (in addition to the standard modules, unless -noEV is specified)",
required = false)
protected String[] MODULES_TO_USE = {};
@Argument(
fullName = "doNotUseAllStandardModules",
shortName = "noEV",
doc =
"Do not use the standard modules by default (instead, only those that are specified with the -EV option)",
required = false)
protected Boolean NO_STANDARD_MODULES = false;
@Argument(
fullName = "minPhaseQuality",
shortName = "mpq",
doc = "Minimum phasing quality",
required = false)
protected double MIN_PHASE_QUALITY = 10.0;
@Argument(
shortName = "mvq",
fullName = "mendelianViolationQualThreshold",
doc =
"Minimum genotype QUAL score for each trio member required to accept a site as a violation. Default is 50.",
required = false)
protected double MENDELIAN_VIOLATION_QUAL_THRESHOLD = 50;
@Argument(
shortName = "ploidy",
fullName = "samplePloidy",
doc = "Per-sample ploidy (number of chromosomes per sample)",
required = false)
protected int ploidy = GATKVariantContextUtils.DEFAULT_PLOIDY;
@Argument(
fullName = "ancestralAlignments",
shortName = "aa",
doc = "Fasta file with ancestral alleles",
required = false)
private File ancestralAlignmentsFile = null;
@Argument(
fullName = "requireStrictAlleleMatch",
shortName = "strict",
doc =
"If provided only comp and eval tracks with exactly matching reference and alternate alleles will be counted as overlapping",
required = false)
private boolean requireStrictAlleleMatch = false;
@Argument(
fullName = "keepAC0",
shortName = "keepAC0",
doc =
"If provided, modules that track polymorphic sites will not require that a site have AC > 0 when the input eval has genotypes",
required = false)
private boolean keepSitesWithAC0 = false;
@Hidden
@Argument(
fullName = "numSamples",
shortName = "numSamples",
doc =
"If provided, modules that track polymorphic sites will not require that a site have AC > 0 when the input eval has genotypes",
required = false)
private int numSamplesFromArgument = 0;
/**
* If true, VariantEval will treat -eval 1 -eval 2 as separate tracks from the same underlying
* variant set, and evaluate the union of the results. Useful when you want to do -eval chr1.vcf
* -eval chr2.vcf etc.
*/
@Argument(
fullName = "mergeEvals",
shortName = "mergeEvals",
doc = "If provided, all -eval tracks will be merged into a single eval track",
required = false)
public boolean mergeEvals = false;
/** File containing tribble-readable features for the IntervalStratificiation */
@Input(
fullName = "stratIntervals",
shortName = "stratIntervals",
doc = "File containing tribble-readable features for the IntervalStratificiation",
required = false)
public IntervalBinding<Feature> intervalsFile = null;
/**
* File containing tribble-readable features containing known CNVs. For use with VariantSummary
* table.
*/
@Input(
fullName = "knownCNVs",
shortName = "knownCNVs",
doc =
"File containing tribble-readable features describing a known list of copy number variants",
required = false)
public IntervalBinding<Feature> knownCNVsFile = null;
Map<String, IntervalTree<GenomeLoc>> knownCNVsByContig = Collections.emptyMap();
// Variables
private Set<SortableJexlVCMatchExp> jexlExpressions = new TreeSet<SortableJexlVCMatchExp>();
private boolean isSubsettingSamples;
private Set<String> sampleNamesForEvaluation = new LinkedHashSet<String>();
private Set<String> sampleNamesForStratification = new LinkedHashSet<String>();
// important stratifications
private boolean byFilterIsEnabled = false;
private boolean perSampleIsEnabled = false;
// Public constants
private static String ALL_SAMPLE_NAME = "all";
// the number of processed bp for this walker
long nProcessedLoci = 0;
// Utility class
private final VariantEvalUtils variantEvalUtils = new VariantEvalUtils(this);
// Ancestral alignments
private IndexedFastaSequenceFile ancestralAlignments = null;
// The set of all possible evaluation contexts
StratificationManager<VariantStratifier, EvaluationContext> stratManager;
// Set<DynamicStratification> dynamicStratifications = Collections.emptySet();
/** Initialize the stratifications, evaluations, evaluation contexts, and reporting object */
public void initialize() {
// Just list the modules, and exit quickly.
if (LIST) {
variantEvalUtils.listModulesAndExit();
}
// maintain the full list of comps
comps.addAll(compsProvided);
if (dbsnp.dbsnp.isBound()) {
comps.add(dbsnp.dbsnp);
knowns.add(dbsnp.dbsnp);
}
// Add a dummy comp track if none exists
if (comps.size() == 0)
comps.add(
new RodBinding<VariantContext>(VariantContext.class, "none", "UNBOUND", "", new Tags()));
// Set up set of additional knowns
for (RodBinding<VariantContext> compRod : comps) {
if (KNOWN_NAMES.contains(compRod.getName())) knowns.add(compRod);
}
// Now that we have all the rods categorized, determine the sample list from the eval rods.
Map<String, VCFHeader> vcfRods = GATKVCFUtils.getVCFHeadersFromRods(getToolkit(), evals);
Set<String> vcfSamples =
SampleUtils.getSampleList(
vcfRods, GATKVariantContextUtils.GenotypeMergeType.REQUIRE_UNIQUE);
// Load the sample list, using an intermediate tree set to sort the samples
final Set<String> allSampleNames = SampleUtils.getSamplesFromCommandLineInput(vcfSamples);
sampleNamesForEvaluation.addAll(
new TreeSet<String>(
SampleUtils.getSamplesFromCommandLineInput(vcfSamples, SAMPLE_EXPRESSIONS)));
isSubsettingSamples = !sampleNamesForEvaluation.containsAll(allSampleNames);
if (Arrays.asList(STRATIFICATIONS_TO_USE).contains("Sample")) {
sampleNamesForStratification.addAll(sampleNamesForEvaluation);
}
sampleNamesForStratification.add(ALL_SAMPLE_NAME);
// Initialize select expressions
for (VariantContextUtils.JexlVCMatchExp jexl :
VariantContextUtils.initializeMatchExps(SELECT_NAMES, SELECT_EXPS)) {
SortableJexlVCMatchExp sjexl = new SortableJexlVCMatchExp(jexl.name, jexl.exp);
jexlExpressions.add(sjexl);
}
// Initialize the set of stratifications and evaluations to use
// The list of stratifiers and evaluators to use
final List<VariantStratifier> stratificationObjects =
variantEvalUtils.initializeStratificationObjects(
NO_STANDARD_STRATIFICATIONS, STRATIFICATIONS_TO_USE);
final Set<Class<? extends VariantEvaluator>> evaluationClasses =
variantEvalUtils.initializeEvaluationObjects(NO_STANDARD_MODULES, MODULES_TO_USE);
checkForIncompatibleEvaluatorsAndStratifiers(stratificationObjects, evaluationClasses);
for (VariantStratifier vs : stratificationObjects) {
if (vs.getName().equals("Filter")) byFilterIsEnabled = true;
else if (vs.getName().equals("Sample")) perSampleIsEnabled = true;
}
if (intervalsFile != null) {
boolean fail = true;
for (final VariantStratifier vs : stratificationObjects) {
if (vs.getClass().equals(IntervalStratification.class)) fail = false;
}
if (fail)
throw new UserException.BadArgumentValue(
"ST", "stratIntervals argument provided but -ST IntervalStratification not provided");
}
// Initialize the evaluation contexts
createStratificationStates(stratificationObjects, evaluationClasses);
// Load ancestral alignments
if (ancestralAlignmentsFile != null) {
try {
ancestralAlignments = new IndexedFastaSequenceFile(ancestralAlignmentsFile);
} catch (FileNotFoundException e) {
throw new ReviewedStingException(
String.format(
"The ancestral alignments file, '%s', could not be found",
ancestralAlignmentsFile.getAbsolutePath()));
}
}
// initialize CNVs
if (knownCNVsFile != null) {
knownCNVsByContig = createIntervalTreeByContig(knownCNVsFile);
}
}
final void checkForIncompatibleEvaluatorsAndStratifiers(
final List<VariantStratifier> stratificationObjects,
Set<Class<? extends VariantEvaluator>> evaluationClasses) {
for (final VariantStratifier vs : stratificationObjects) {
for (Class<? extends VariantEvaluator> ec : evaluationClasses)
if (vs.getIncompatibleEvaluators().contains(ec))
throw new UserException.BadArgumentValue(
"ST and ET",
"The selected stratification "
+ vs.getName()
+ " and evaluator "
+ ec.getSimpleName()
+ " are incompatible due to combinatorial memory requirements."
+ " Please disable one");
}
}
final void createStratificationStates(
final List<VariantStratifier> stratificationObjects,
final Set<Class<? extends VariantEvaluator>> evaluationObjects) {
final List<VariantStratifier> strats = new ArrayList<VariantStratifier>(stratificationObjects);
stratManager = new StratificationManager<VariantStratifier, EvaluationContext>(strats);
logger.info("Creating " + stratManager.size() + " combinatorial stratification states");
for (int i = 0; i < stratManager.size(); i++) {
EvaluationContext ec = new EvaluationContext(this, evaluationObjects);
stratManager.set(i, ec);
}
}
public final Map<String, IntervalTree<GenomeLoc>> createIntervalTreeByContig(
final IntervalBinding<Feature> intervals) {
final Map<String, IntervalTree<GenomeLoc>> byContig =
new HashMap<String, IntervalTree<GenomeLoc>>();
final List<GenomeLoc> locs = intervals.getIntervals(getToolkit());
// set up the map from contig -> interval tree
for (final String contig : getContigNames())
byContig.put(contig, new IntervalTree<GenomeLoc>());
for (final GenomeLoc loc : locs) {
byContig.get(loc.getContig()).put(loc.getStart(), loc.getStop(), loc);
}
return byContig;
}
/** Collect relevant information from each variant in the supplied VCFs */
@Override
public Integer map(RefMetaDataTracker tracker, ReferenceContext ref, AlignmentContext context) {
// we track the processed bp and expose this for modules instead of wasting CPU power on
// calculating
// the same thing over and over in evals that want the processed bp
synchronized (this) {
nProcessedLoci += context.getSkippedBases() + (ref == null ? 0 : 1);
}
if (tracker != null) {
String aastr =
(ancestralAlignments == null)
? null
: new String(
ancestralAlignments
.getSubsequenceAt(
ref.getLocus().getContig(),
ref.getLocus().getStart(),
ref.getLocus().getStop())
.getBases());
// // update the dynamic stratifications
// for (final VariantContext vc : tracker.getValues(evals, ref.getLocus())) {
// // don't worry -- DynamicStratification only work with one eval object
// for ( final DynamicStratification ds : dynamicStratifications ) {
// ds.update(vc);
// }
// }
// --------- track --------- sample - VariantContexts -
HashMap<RodBinding<VariantContext>, HashMap<String, Collection<VariantContext>>> evalVCs =
variantEvalUtils.bindVariantContexts(
tracker, ref, evals, byFilterIsEnabled, true, perSampleIsEnabled, mergeEvals);
HashMap<RodBinding<VariantContext>, HashMap<String, Collection<VariantContext>>> compVCs =
variantEvalUtils.bindVariantContexts(
tracker, ref, comps, byFilterIsEnabled, false, false, false);
// for each eval track
for (final RodBinding<VariantContext> evalRod : evals) {
final Map<String, Collection<VariantContext>> emptyEvalMap = Collections.emptyMap();
final Map<String, Collection<VariantContext>> evalSet =
evalVCs.containsKey(evalRod) ? evalVCs.get(evalRod) : emptyEvalMap;
// for each sample stratifier
for (final String sampleName : sampleNamesForStratification) {
Collection<VariantContext> evalSetBySample = evalSet.get(sampleName);
if (evalSetBySample == null) {
evalSetBySample = new HashSet<VariantContext>(1);
evalSetBySample.add(null);
}
// for each eval in the track
for (VariantContext eval : evalSetBySample) {
// deal with ancestral alleles if requested
if (eval != null && aastr != null) {
eval = new VariantContextBuilder(eval).attribute("ANCESTRALALLELE", aastr).make();
}
// for each comp track
for (final RodBinding<VariantContext> compRod : comps) {
// no sample stratification for comps
final HashMap<String, Collection<VariantContext>> compSetHash = compVCs.get(compRod);
final Collection<VariantContext> compSet =
(compSetHash == null || compSetHash.size() == 0)
? Collections.<VariantContext>emptyList()
: compVCs.get(compRod).values().iterator().next();
// find the comp
final VariantContext comp = findMatchingComp(eval, compSet);
for (EvaluationContext nec :
getEvaluationContexts(
tracker, ref, eval, evalRod.getName(), comp, compRod.getName(), sampleName)) {
// eval against the comp
synchronized (nec) {
nec.apply(tracker, ref, context, comp, eval);
}
// eval=null against all comps of different type that aren't bound to another eval
for (VariantContext otherComp : compSet) {
if (otherComp != comp && !compHasMatchingEval(otherComp, evalSetBySample)) {
synchronized (nec) {
nec.apply(tracker, ref, context, otherComp, null);
}
}
}
}
}
}
}
if (mergeEvals) break; // stop processing the eval tracks
}
}
return null;
}
/**
* Given specific eval and comp VCs and the sample name, return an iterable over all of the
* applicable state keys.
*
* <p>this code isn't structured yet for efficiency. Here we currently are doing the following
* inefficient algorithm:
*
* <p>for each strat: get list of relevant states that eval and comp according to strat add this
* list of states to a list of list states
*
* <p>then
*
* <p>ask the strat manager to look up all of the keys associated with the combinations of these
* states. For example, suppose we have a single variant S. We have active strats EvalRod,
* CompRod, and Novelty. We produce a list that looks like:
*
* <p>L = [[Eval], [Comp], [All, Novel]]
*
* <p>We then go through the strat manager tree to produce the keys associated with these states:
*
* <p>K = [0, 1] where EVAL x COMP x ALL = 0 and EVAL x COMP x NOVEL = 1
*
* <p>It's clear that a better
*
* <p>TODO -- create an inline version that doesn't create the intermediate list of list
*
* @param tracker
* @param ref
* @param eval
* @param evalName
* @param comp
* @param compName
* @param sampleName
* @return
*/
protected Collection<EvaluationContext> getEvaluationContexts(
final RefMetaDataTracker tracker,
final ReferenceContext ref,
final VariantContext eval,
final String evalName,
final VariantContext comp,
final String compName,
final String sampleName) {
final List<List<Object>> states = new LinkedList<List<Object>>();
for (final VariantStratifier vs : stratManager.getStratifiers()) {
states.add(vs.getRelevantStates(ref, tracker, comp, compName, eval, evalName, sampleName));
}
return stratManager.values(states);
}
@Requires({"comp != null", "evals != null"})
private boolean compHasMatchingEval(
final VariantContext comp, final Collection<VariantContext> evals) {
// find all of the matching comps
for (final VariantContext eval : evals) {
if (eval != null
&& doEvalAndCompMatch(comp, eval, requireStrictAlleleMatch) != EvalCompMatchType.NO_MATCH)
return true;
}
// nothing matched
return false;
}
private enum EvalCompMatchType {
NO_MATCH,
STRICT,
LENIENT
}
@Requires({"eval != null", "comp != null"})
private EvalCompMatchType doEvalAndCompMatch(
final VariantContext eval, final VariantContext comp, boolean requireStrictAlleleMatch) {
if (comp.getType() == VariantContext.Type.NO_VARIATION
|| eval.getType() == VariantContext.Type.NO_VARIATION)
// if either of these are NO_VARIATION they are LENIENT matches
return EvalCompMatchType.LENIENT;
if (comp.getType() != eval.getType()) return EvalCompMatchType.NO_MATCH;
// find the comp which matches both the reference allele and alternate allele from eval
final Allele altEval =
eval.getAlternateAlleles().size() == 0 ? null : eval.getAlternateAllele(0);
final Allele altComp =
comp.getAlternateAlleles().size() == 0 ? null : comp.getAlternateAllele(0);
if ((altEval == null && altComp == null)
|| (altEval != null
&& altEval.equals(altComp)
&& eval.getReference().equals(comp.getReference()))) return EvalCompMatchType.STRICT;
else return requireStrictAlleleMatch ? EvalCompMatchType.NO_MATCH : EvalCompMatchType.LENIENT;
}
private VariantContext findMatchingComp(
final VariantContext eval, final Collection<VariantContext> comps) {
// if no comps, return null
if (comps == null || comps.isEmpty()) return null;
// if no eval, return any comp
if (eval == null) return comps.iterator().next();
// find all of the matching comps
VariantContext lenientMatch = null;
for (final VariantContext comp : comps) {
switch (doEvalAndCompMatch(comp, eval, requireStrictAlleleMatch)) {
case STRICT:
return comp;
case LENIENT:
if (lenientMatch == null) lenientMatch = comp;
break;
case NO_MATCH:
// do nothing
}
}
// nothing matched, just return lenientMatch, which might be null
return lenientMatch;
}
public Integer treeReduce(Integer lhs, Integer rhs) {
return null;
}
@Override
public Integer reduceInit() {
return null;
}
@Override
public Integer reduce(Integer value, Integer sum) {
return null;
}
/**
* Output the finalized report
*
* @param result an integer that doesn't get used for anything
*/
public void onTraversalDone(Integer result) {
logger.info("Finalizing variant report");
// go through the evaluations and finalize them
for (final EvaluationContext nec : stratManager.values())
for (final VariantEvaluator ve : nec.getVariantEvaluators()) ve.finalizeEvaluation();
VariantEvalReportWriter.writeReport(
out,
stratManager,
stratManager.getStratifiers(),
stratManager.get(0).getVariantEvaluators());
}
// Accessors
public Logger getLogger() {
return logger;
}
public double getMinPhaseQuality() {
return MIN_PHASE_QUALITY;
}
public int getSamplePloidy() {
return ploidy;
}
public double getMendelianViolationQualThreshold() {
return MENDELIAN_VIOLATION_QUAL_THRESHOLD;
}
public static String getAllSampleName() {
return ALL_SAMPLE_NAME;
}
public List<RodBinding<VariantContext>> getKnowns() {
return knowns;
}
public List<RodBinding<VariantContext>> getEvals() {
return evals;
}
public boolean isSubsettingToSpecificSamples() {
return isSubsettingSamples;
}
public Set<String> getSampleNamesForEvaluation() {
return sampleNamesForEvaluation;
}
public int getNumberOfSamplesForEvaluation() {
if (sampleNamesForEvaluation != null && !sampleNamesForEvaluation.isEmpty())
return sampleNamesForEvaluation.size();
else {
return numSamplesFromArgument;
}
}
public Set<String> getSampleNamesForStratification() {
return sampleNamesForStratification;
}
public List<RodBinding<VariantContext>> getComps() {
return comps;
}
public Set<SortableJexlVCMatchExp> getJexlExpressions() {
return jexlExpressions;
}
public long getnProcessedLoci() {
return nProcessedLoci;
}
public Set<String> getContigNames() {
final TreeSet<String> contigs = new TreeSet<String>();
for (final SAMSequenceRecord r :
getToolkit()
.getReferenceDataSource()
.getReference()
.getSequenceDictionary()
.getSequences()) {
contigs.add(r.getSequenceName());
}
return contigs;
}
/**
* getToolkit is protected, so we have to pseudo-overload it here so eval / strats can get the
* toolkit
*
* @return
*/
public GenomeAnalysisEngine getToolkit() {
return super.getToolkit();
}
public boolean ignoreAC0Sites() {
return !keepSitesWithAC0;
}
}
