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Merge pull request #1029 from broadinstitute/rhl_vqslod_definition 

Make VQSLOD definition accurate
This commit is contained in:
Geraldine Van der Auwera 2015-07-06 19:52:15 -04:00
parent aaf4e33e15 09686f4595
commit c109a953f8
3 changed files with 13 additions and 13 deletions
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2
protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrator.java
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@ -99,7 +99,7 @@ import java.util.*;
* as input, typically HapMap 3 sites and those sites found to be polymorphic on the Omni 2.5M SNP chip array (in humans). This adaptive
* error model can then be applied to both known and novel variation discovered in the call set of interest to evaluate the
* probability that each call is real. The score that gets added to the INFO field of each variant is called the VQSLOD. It is
* the log odds ratio of being a true variant versus being false under the trained Gaussian mixture model.
* the log odds of being a true variant versus being false under the trained Gaussian mixture model.
* </p>
*
* <p>VQSR is probably the hardest part of the Best Practices to get right, so be sure to read the

22
protected/gatk-tools-protected/src/test/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrationWalkersIntegrationTest.java
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@ -94,14 +94,14 @@ public class VariantRecalibrationWalkersIntegrationTest extends WalkerTest {
VRTest lowPass = new VRTest(validationDataLocation + "phase1.projectConsensus.chr20.raw.snps.vcf",
"41e2d951a17de433fe378bb3d9ec75d4", // tranches
"04336b2453202f286da05b69e57f66ed", // recal file
"d29fd0bdc1c8c3a171e10d29f7ffeaec"); // cut VCF
"19c77724f08d90896914d3d348807399", // recal file
"c6a186a1a9271f5de35f1e5aeb8749a6"); // cut VCF
VRTest lowPassPlusExomes = new VRTest(validationDataLocation + "phase1.projectConsensus.chr20.raw.snps.vcf",
validationDataLocation + "1kg_exomes_unfiltered.AFR.unfiltered.vcf",
"ce4bfc6619147fe7ce1f8331bbeb86ce", // tranches
"1b33c10be7d8bf8e9accd11113835262", // recal file
"4700d52a06f2ef3a5882719b86911e51"); // cut VCF
"b7cad6a0bbbf0330e0ac712a80c3144f", // recal file
"bee399765991636461599565c9634bcf"); // cut VCF
@DataProvider(name = "VRTest")
public Object[][] createData1() {
@ -196,8 +196,8 @@ public class VariantRecalibrationWalkersIntegrationTest extends WalkerTest {
VRTest bcfTest = new VRTest(privateTestDir + "vqsr.bcf_test.snps.unfiltered.bcf",
"3ad7f55fb3b072f373cbce0b32b66df4", // tranches
"e747c08131d58d9a4800720f6ca80e0c", // recal file
"e5808af3af0f2611ba5a3d172ab2557b"); // cut VCF
"e91a5b25ea1eefdcff488e0326028b51", // recal file
"e6a0c5173d8c8fbd08afdc5e5e7d3a78"); // cut VCF
@DataProvider(name = "VRBCFTest")
public Object[][] createVRBCFTest() {
@ -251,14 +251,14 @@ public class VariantRecalibrationWalkersIntegrationTest extends WalkerTest {
VRTest indelUnfiltered = new VRTest(
validationDataLocation + "combined.phase1.chr20.raw.indels.unfiltered.sites.vcf", // all FILTERs as .
"9a331328370889168a7aa3a625f73620", // tranches
"2cbbd146d68c40200b782e0226f71976", // recal file
"64dd98a5ab80cf5fd9a36eb66b38268e"); // cut VCF
"689c7853fe2e63216da3b0d47e27740e", // recal file
"4147373ec8e0aba7ace3658677007990"); // cut VCF
VRTest indelFiltered = new VRTest(
validationDataLocation + "combined.phase1.chr20.raw.indels.filtered.sites.vcf", // all FILTERs as PASS
"9a331328370889168a7aa3a625f73620", // tranches
"2cbbd146d68c40200b782e0226f71976", // recal file
"c0ec662001e829f5779a9d13b1d77d80"); // cut VCF
"689c7853fe2e63216da3b0d47e27740e", // recal file
"8dd8ea31e419f68d80422b34b14e24e4"); // cut VCF
@DataProvider(name = "VRIndelTest")
public Object[][] createTestVariantRecalibratorIndel() {
@ -316,7 +316,7 @@ public class VariantRecalibrationWalkersIntegrationTest extends WalkerTest {
" -o %s" +
" -tranchesFile " + privateTestDir + "VQSR.mixedTest.tranches" +
" -recalFile " + privateTestDir + "VQSR.mixedTest.recal",
Arrays.asList("03a0ed00af6aac76d39e569f90594a02"));
Arrays.asList("cd42484985179c7f549e652f0f6a94d0"));
final List<File> outputFiles = executeTest("testApplyRecalibrationSnpAndIndelTogether", spec).getFirst();
setPDFsForDeletion(outputFiles);
}

2
public/gatk-utils/src/main/java/org/broadinstitute/gatk/utils/variant/GATKVCFHeaderLines.java
View File

@ -146,7 +146,7 @@ public class GATKVCFHeaderLines {
addInfoLine(new VCFInfoHeaderLine(ORIGINAL_DP_KEY, 1, VCFHeaderLineType.Integer, "Original DP"));
addInfoLine(new VCFInfoHeaderLine(ORIGINAL_CONTIG_KEY, 1, VCFHeaderLineType.String, "Original contig name for the record"));
addInfoLine(new VCFInfoHeaderLine(ORIGINAL_START_KEY, 1, VCFHeaderLineType.Integer, "Original start position for the record"));
addInfoLine(new VCFInfoHeaderLine(VQS_LOD_KEY, 1, VCFHeaderLineType.Float, "Log odds ratio of being a true variant versus being false under the trained gaussian mixture model"));
addInfoLine(new VCFInfoHeaderLine(VQS_LOD_KEY, 1, VCFHeaderLineType.Float, "Log odds of being a true variant versus being false under the trained gaussian mixture model"));
addInfoLine(new VCFInfoHeaderLine(CULPRIT_KEY, 1, VCFHeaderLineType.String, "The annotation which was the worst performing in the Gaussian mixture model, likely the reason why the variant was filtered out"));
addInfoLine(new VCFInfoHeaderLine(POSITIVE_LABEL_KEY, 1, VCFHeaderLineType.Flag, "This variant was used to build the positive training set of good variants"));
addInfoLine(new VCFInfoHeaderLine(NEGATIVE_LABEL_KEY, 1, VCFHeaderLineType.Flag, "This variant was used to build the negative training set of bad variants"));