Gokalps-Mac-mini:1000GVCFs sky$ gatk SelectVariants -V 1000G_CEU_chr16.vcf.gz -O 1000G_CEU_AFfilt_chr16.vcf.gz -select "AF > 0.0"
Using GATK jar /Users/sky/scripts/gatk-package-4.0.9.0-local.jar
Running:
    java -Dsamjdk.use_async_io_read_samtools=false -Dsamjdk.use_async_io_write_samtools=true -Dsamjdk.use_async_io_write_tribble=false -Dsamjdk.compression_level=2 -jar /Users/sky/scripts/gatk-package-4.0.9.0-local.jar SelectVariants -V 1000G_CEU_chr16.vcf.gz -O 1000G_CEU_AFfilt_chr16.vcf.gz -select AF > 0.0
14:35:45.842 INFO  NativeLibraryLoader - Loading libgkl_compression.dylib from jar:file:/Users/sky/scripts/gatk-package-4.0.9.0-local.jar!/com/intel/gkl/native/libgkl_compression.dylib
Sep 24, 2018 2:35:47 PM shaded.cloud_nio.com.google.auth.oauth2.ComputeEngineCredentials runningOnComputeEngine
WARNING: Failed to detect whether we are running on Google Compute Engine.
java.net.ConnectException: No route to host (connect failed)
    at java.net.PlainSocketImpl.socketConnect(Native Method)
    at java.net.AbstractPlainSocketImpl.doConnect(AbstractPlainSocketImpl.java:350)
    at java.net.AbstractPlainSocketImpl.connectToAddress(AbstractPlainSocketImpl.java:206)
    at java.net.AbstractPlainSocketImpl.connect(AbstractPlainSocketImpl.java:188)
    at java.net.SocksSocketImpl.connect(SocksSocketImpl.java:392)
    at java.net.Socket.connect(Socket.java:589)
    at sun.net.NetworkClient.doConnect(NetworkClient.java:175)
    at sun.net.www.http.HttpClient.openServer(HttpClient.java:463)
    at sun.net.www.http.HttpClient.openServer(HttpClient.java:558)
    at sun.net.www.http.HttpClient.<init>(HttpClient.java:242)
    at sun.net.www.http.HttpClient.New(HttpClient.java:339)
    at sun.net.www.http.HttpClient.New(HttpClient.java:357)
    at sun.net.www.protocol.http.HttpURLConnection.getNewHttpClient(HttpURLConnection.java:1220)
    at sun.net.www.protocol.http.HttpURLConnection.plainConnect0(HttpURLConnection.java:1156)
    at sun.net.www.protocol.http.HttpURLConnection.plainConnect(HttpURLConnection.java:1050)
    at sun.net.www.protocol.http.HttpURLConnection.connect(HttpURLConnection.java:984)
    at shaded.cloud_nio.com.google.api.client.http.javanet.NetHttpRequest.execute(NetHttpRequest.java:104)
    at shaded.cloud_nio.com.google.api.client.http.HttpRequest.execute(HttpRequest.java:981)
    at shaded.cloud_nio.com.google.auth.oauth2.ComputeEngineCredentials.runningOnComputeEngine(ComputeEngineCredentials.java:210)
    at shaded.cloud_nio.com.google.auth.oauth2.DefaultCredentialsProvider.tryGetComputeCredentials(DefaultCredentialsProvider.java:290)
    at shaded.cloud_nio.com.google.auth.oauth2.DefaultCredentialsProvider.getDefaultCredentialsUnsynchronized(DefaultCredentialsProvider.java:207)
    at shaded.cloud_nio.com.google.auth.oauth2.DefaultCredentialsProvider.getDefaultCredentials(DefaultCredentialsProvider.java:124)
    at shaded.cloud_nio.com.google.auth.oauth2.GoogleCredentials.getApplicationDefault(GoogleCredentials.java:127)
    at shaded.cloud_nio.com.google.auth.oauth2.GoogleCredentials.getApplicationDefault(GoogleCredentials.java:100)
    at com.google.cloud.ServiceOptions.defaultCredentials(ServiceOptions.java:304)
    at com.google.cloud.ServiceOptions.<init>(ServiceOptions.java:278)
    at com.google.cloud.storage.StorageOptions.<init>(StorageOptions.java:83)
    at com.google.cloud.storage.StorageOptions.<init>(StorageOptions.java:31)
    at com.google.cloud.storage.StorageOptions$Builder.build(StorageOptions.java:78)
    at org.broadinstitute.hellbender.utils.gcs.BucketUtils.setGlobalNIODefaultOptions(BucketUtils.java:360)
    at org.broadinstitute.hellbender.cmdline.CommandLineProgram.instanceMainPostParseArgs(CommandLineProgram.java:183)
    at org.broadinstitute.hellbender.cmdline.CommandLineProgram.instanceMain(CommandLineProgram.java:211)
    at org.broadinstitute.hellbender.Main.runCommandLineProgram(Main.java:160)
    at org.broadinstitute.hellbender.Main.mainEntry(Main.java:203)
    at org.broadinstitute.hellbender.Main.main(Main.java:289)

14:35:47.079 INFO  SelectVariants - ------------------------------------------------------------
14:35:47.079 INFO  SelectVariants - The Genome Analysis Toolkit (GATK) v4.0.9.0
14:35:47.080 INFO  SelectVariants - For support and documentation go to https://software.broadinstitute.org/gatk/
14:35:47.080 INFO  SelectVariants - Executing as sky@Gokalps-Mac-mini.local on Mac OS X v10.13.6 x86_64
14:35:47.080 INFO  SelectVariants - Java runtime: Java HotSpot(TM) 64-Bit Server VM v1.8.0_181-b13
14:35:47.080 INFO  SelectVariants - Start Date/Time: September 24, 2018 2:35:45 PM EET
14:35:47.080 INFO  SelectVariants - ------------------------------------------------------------
14:35:47.081 INFO  SelectVariants - ------------------------------------------------------------
14:35:47.082 INFO  SelectVariants - HTSJDK Version: 2.16.1
14:35:47.082 INFO  SelectVariants - Picard Version: 2.18.13
14:35:47.082 INFO  SelectVariants - HTSJDK Defaults.COMPRESSION_LEVEL : 2
14:35:47.082 INFO  SelectVariants - HTSJDK Defaults.USE_ASYNC_IO_READ_FOR_SAMTOOLS : false
14:35:47.082 INFO  SelectVariants - HTSJDK Defaults.USE_ASYNC_IO_WRITE_FOR_SAMTOOLS : true
14:35:47.082 INFO  SelectVariants - HTSJDK Defaults.USE_ASYNC_IO_WRITE_FOR_TRIBBLE : false
14:35:47.082 INFO  SelectVariants - Deflater: IntelDeflater
14:35:47.082 INFO  SelectVariants - Inflater: IntelInflater

I am getting this strange error message from time to time. It is clear that I am using the gatk local however something is wrong with google compute engine checks I guess.

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Hi team,
I tried to run the following code:

"time ./gatk-4.0.8.1/gatk --java-options "-Xmx4g" Mutect2 -R Mus_musculus.GRCm38.dna.toplevel.fa -I M2.bam -I RMS1.bam -tumor M2 -normal RMS1 -O T2_N1_new.vcf"

and I got the error says "Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit exceeded", may I ask if this is because my required memory is not enough? If so, how much memory to you think I need to ask for mutech2 (I required 80gb memory for now, and my two data are 40gb)?

Thanks!

Hi there,

Hope someone can shed some light on this issue.

I have problem running picard-tools MarkDuplicates. I get an error "No space left on device". Having a bit of a search I found people mention that it might be an issue with the tmpdir folder specified. However the folder I'm using for tmpdir is massive (72GB). Looking a bit more at the error log, I found the retain data points before spilling to disk line.

It had a number that matched very closely to the number of records read before the error message. (28872640 vs 29,000,000)

INFO 2015-09-03 15:53:32 MarkDuplicates Will retain up to 28872640 data points before spilling to disk.
...
INFO 2015-09-03 15:55:50 MarkDuplicates Read 29,000,000 records. Elapsed time: 00:02:18s. Time for last 1,000,000: 4s. Last read position: chr7:39,503,936
INFO 2015-09-03 15:55:50 MarkDuplicates Tracking 195949 as yet unmatched pairs. 13309 records in RAM.
[Thu Sep 03 15:55:53 EST 2015] picard.sam.markduplicates.MarkDuplicates done. Elapsed time: 2.35 minutes.
Runtime.totalMemory()=6107234304
To get help, see http://broadinstitute.github.io/picard/index.html#GettingHelp
Exception in thread "main" htsjdk.samtools.util.RuntimeIOException: java.io.IOException: No space left on device
at htsjdk.samtools.util.SortingCollection.spillToDisk(SortingCollection.java:245)
at htsjdk.samtools.util.SortingCollection.add(SortingCollection.java:165)
at picard.sam.markduplicates.MarkDuplicates.buildSortedReadEndLists(MarkDuplicates.java:281)
at picard.sam.markduplicates.MarkDuplicates.doWork(MarkDuplicates.java:114)
at picard.cmdline.CommandLineProgram.instanceMain(CommandLineProgram.java:206)
at picard.cmdline.PicardCommandLine.instanceMain(PicardCommandLine.java:95)
at picard.cmdline.PicardCommandLine.main(PicardCommandLine.java:105)
Caused by: java.io.IOException: No space left on device
at java.io.FileOutputStream.writeBytes(Native Method)
at java.io.FileOutputStream.write(FileOutputStream.java:318)
at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
at org.xerial.snappy.SnappyOutputStream.dump(SnappyOutputStream.java:127)
at org.xerial.snappy.SnappyOutputStream.flush(SnappyOutputStream.java:100)
at org.xerial.snappy.SnappyOutputStream.close(SnappyOutputStream.java:137)
at htsjdk.samtools.util.SortingCollection.spillToDisk(SortingCollection.java:236)
... 6 more

I had a play around with the memory option of java (-Xmx??g) when I issue my MarkDuplicates call, and I see that increase in memory increase the number of data points before spilling to disk. This then increase the number of records read before my "No sapce left in device" error.

eg -Xmx16g gave me 59674689 data points before spilling to disk and I got up to 60,000,000 records read before "no space left on device" error.

I know I can increase my memory to allow for more records, but there is a limit to doing that if I have a huge bam.

What I would like to know is what does "Will retain up to 28872640 data points before spilling to disk." actually mean. I thought it was a safe guard for memory usage, where if the number of records/data point is excceeded then some will be written to file, thus allowing more records to be read. This mean you can still process a large bam with only a small amount of memory. But it does not seem to work that way from what I'm seeing.

My entry "java -Xmx16g -Djavaio.tmpdir=/short/a32/working_temp -jar $PICARD_TOOLS_DIR/picard.jar MarkDuplicates INPUT=output.bam OUTPUT=output.marked.bam METRICS_FILE=metrics CREATE_INDEX=true VALIDATION_STRINGENCY=LENIENT
"

Hope you can help and thanks for your assistance in advance.
Eddie

Hello,

I've annotated a SAM file with UMI information (using fgbio's AnnotateBamWithUmis) and am trying to mark duplicates with the tool "UmiAwareMarkDuplicatesWithMateCigar".

My command is as follows:
java -jar ../Tools/picard.jar UmiAwareMarkDuplicatesWithMateCigar I=aligned_umi.sam O=aligned_umi_dup.sam M=aligned_M.txt UMI_METRICS=aligned_umi_met.txt

The version of Picard is 2.18.12-SNAPSHOT

However I receive the following error:

Exception in thread "main" java.lang.NullPointerException at picard.sam.markduplicates.UmiAwareDuplicateSetIterator.process(UmiAwareDuplicateSetIterator.java:138) at picard.sam.markduplicates.UmiAwareDuplicateSetIterator.next(UmiAwareDuplicateSetIterator.java:117) at picard.sam.markduplicates.UmiAwareDuplicateSetIterator.next(UmiAwareDuplicateSetIterator.java:53) at picard.sam.markduplicates.SimpleMarkDuplicatesWithMateCigar.doWork(SimpleMarkDuplicatesWithMateCigar.java:133) at picard.sam.markduplicates.UmiAwareMarkDuplicatesWithMateCigar.doWork(UmiAwareMarkDuplicatesWithMateCigar.java:141) at picard.cmdline.CommandLineProgram.instanceMain(CommandLineProgram.java:277) at picard.cmdline.PicardCommandLine.instanceMain(PicardCommandLine.java:103) at picard.cmdline.PicardCommandLine.main(PicardCommandLine.java:113)

I'm unsure why I'm getting this error. I can do the duplicate marking using the original MarkDuplicates tool without issue:
java -jar ../Tools/picard.jar MarkDuplicates INPUT=aligned_umi.sam OUTPUT=aligned_umi_dup.sam METRICS_FILE=aligned_M.txt BARCODE_TAG=RX

But I really want to be able to use the MAX_EDIT_DISTANCE_TO_JOIN parameter with the UmiAwareMarkDuplicatesWithMateCigar tool, and I'm also interested in the UMI_METICS statistics that this tool should report.

Thanks in advance for your help.
Devin

Hi there,

I am trying to combine gVCFs using CombineGVCFs and get the error: "Key END found in VariantContext field INFO at chr1:10439 but this key isn't defined in the VCFHeader".

The gVCFs are generated using HaplotypeCaller GATK4:
gatk --java-options "-Xmx50G" HaplotypeCaller -R Homo_sapiens_assembly38.fasta -I x.bam -O x.g.vcf.gz

In the gVCF headers are "contig=<ID=chr1,length=248956422>" and the position "chr1 10439 . AC A 359.73 . AC=1;AF=0.500;AN=2;BaseQRankSum=0.836;ClippingRankSum=0.000;DP=36;ExcessHet=3.0103;FS=2.063;MLEAC=1;MLEAF=0.500;MQ=37.15;MQRankSum=0.425;QD=12.85;ReadPosRankSum=-0.401;SOR=1.445 GT:AD:DP:GQ:PL 0/1:7,21:28:99:397,0,129"

My question is: What Key is the error referring to (maybe: contig=<ID=chr1,length=248956422>) and how should it be defined in the header giving the above position?

Is there any obvious solution to this?

Thanks you!

I am fresh to GATK .
I want to run this workflow: intel-gatk3-4-germline-snps-indels ,but I dont know how to get the dataset,if I can get the pulic data or do something to get the dataset,
Thank u very much to answer my question as quickly as u read this question.

I have tried to run gatk4-data-processing with the test data you provide, with no error tips, finally I got the outputs.
Then ,I tried to run this workflow with whole genome data of one person,however,it always reported some error like this:
OpenJDK 64-Bit Server VM warning: INFO: os::commit_memory(0x000000079af80000, 1695023104, 0) failed; error='Cannot allocate memory'
(errno=12)
I read the processing-for-variant-discovery-gatk4.wdl, and guess that the reason may be I do not have enough memory to support it.
However, I can not get more memory or change another better mechine, may I do something else to run this workflow with my data?
When It call this task:BaseRecalibrator,it will been shut down:

Is there any method to change the scatter function?
Thank u very much to give me some suggestions,I will be appricate for your suggestions.

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$ java -version
openjdk version "1.8.0_181"
OpenJDK Runtime Environment (build 1.8.0_181-8u181-b13-0ubuntu0.16.04.1-b13)
OpenJDK 64-Bit Server VM (build 25.181-b13, mixed mode)

$ uname -a
Linux dell 4.15.0-29-generic #31~16.04.1-Ubuntu SMP Wed Jul 18 08:54:04 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

command1

1) $ /soft/gatk-4.0.8.1/gatk MergeVcfs -I a.vcf -I b.vcf -I c.vcf -O fam.vcf

ERROR

Using GATK jar /soft/gatk-4.0.8.1/gatk-package-4.0.8.1-local.jar
Running:
java -Dsamjdk.use_async_io_read_samtools=false -Dsamjdk.use_async_io_write_samtools=true -Dsamjdk.use_async_io_write_tribble=false -Dsamjdk.compression_level=2 -jar /soft/gatk-4.0.8.1/gatk-package-4.0.8.1-local.jar MergeVcfs -I a.vcf -I b.vcf -I c.vcf -O fam.vcf
17:58:21.598 INFO NativeLibraryLoader - Loading libgkl_compression.so from jar:file:/soft/gatk-4.0.8.1/gatk-package-4.0.8.1-local.jar!/com/intel/gkl/native/libgkl_compression.so
[Wed Sep 05 17:58:22 CST 2018] MergeVcfs --INPUT a.vcf --INPUT b.vcf --INPUT c.vcf --OUTPUT fam.vcf --VERBOSITY INFO --QUIET false --VALIDATION_STRINGENCY STRICT --COMPRESSION_LEVEL 2 --MAX_RECORDS_IN_RAM 500000 --CREATE_INDEX true --CREATE_MD5_FILE false --GA4GH_CLIENT_SECRETS client_secrets.json --help false --version false --showHidden false --USE_JDK_DEFLATER false --USE_JDK_INFLATER false
[Wed Sep 05 17:58:22 CST 2018] Executing as yu@dell on Linux 4.15.0-29-generic amd64; OpenJDK 64-Bit Server VM 1.8.0_181-8u181-b13-0ubuntu0.16.04.1-b13; Deflater: Intel; Inflater: Intel; Provider GCS is available; Picard version: Version:4.0.8.1
[Wed Sep 05 17:58:22 CST 2018] picard.vcf.MergeVcfs done. Elapsed time: 0.01 minutes.
Runtime.totalMemory()=1430781952
To get help, see http://broadinstitute.github.io/picard/index.html#GettingHelp
java.lang.IllegalArgumentException: Input file b.vcf has sample entries that don't match the other files.
at picard.vcf.MergeVcfs.doWork(MergeVcfs.java:194)
at picard.cmdline.CommandLineProgram.instanceMain(CommandLineProgram.java:282)
at org.broadinstitute.hellbender.cmdline.PicardCommandLineProgramExecutor.instanceMain(PicardCommandLineProgramExecutor.java:25)
at org.broadinstitute.hellbender.Main.runCommandLineProgram(Main.java:160)
at org.broadinstitute.hellbender.Main.mainEntry(Main.java:203)
at org.broadinstitute.hellbender.Main.main(Main.java:289)

command2

2) $ java -jar /soft/picard.jar MergeVcfs I=a.vcf I=b.vcf I=c.vcf O=fam.vcf

ERROR

18:01:26.772 INFO NativeLibraryLoader - Loading libgkl_compression.so from jar:file:/soft/gatk-4.0.8.1/picard.jar!/com/intel/gkl/native/libgkl_compression.so
[Wed Sep 05 18:01:26 CST 2018] MergeVcfs INPUT=[a.vcf, b.vcf, c.vcf] OUTPUT=fam.vcf VERBOSITY=INFO QUIET=false VALIDATION_STRINGENCY=STRICT COMPRESSION_LEVEL=5 MAX_RECORDS_IN_RAM=500000 CREATE_INDEX=true CREATE_MD5_FILE=false GA4GH_CLIENT_SECRETS=client_secrets.json USE_JDK_DEFLATER=false USE_JDK_INFLATER=false
[Wed Sep 05 18:01:26 CST 2018] Executing as yu@dell on Linux 4.15.0-29-generic amd64; OpenJDK 64-Bit Server VM 1.8.0_181-8u181-b13-0ubuntu0.16.04.1-b13; Deflater: Intel; Inflater: Intel; Provider GCS is not available; Picard version: 2.18.12-SNAPSHOT
[Wed Sep 05 18:01:26 CST 2018] picard.vcf.MergeVcfs done. Elapsed time: 0.00 minutes.
Runtime.totalMemory()=2058354688
To get help, see http://broadinstitute.github.io/picard/index.html#GettingHelp
Exception in thread "main" java.lang.IllegalArgumentException: Input file b.vcf has sample entries that don't match the other files.
at picard.vcf.MergeVcfs.doWork(MergeVcfs.java:194)
at picard.cmdline.CommandLineProgram.instanceMain(CommandLineProgram.java:277)
at picard.cmdline.PicardCommandLine.instanceMain(PicardCommandLine.java:103)
at picard.cmdline.PicardCommandLine.main(PicardCommandLine.java:113)

Dear GATK Team,

While running GATK ASEReadCounter using 3.4 and 3.7, i am getting errors related to known sites vcf file.

As per article I have done alignment and processed bam file.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0762-6

Here is my command

java -jar GATK/3.4/GenomeAnalysisTK.jar \
-T ASEReadCounter \
-I ERR188021.rg.md.bam \
-R hs37d5.fa \
-sites ALL.phase1_release_v3.20101123.snps_indels_sv.sites.gdid.gdannot.v2.vcf.gz \
-o ERR188021.ASEReadCounter_results_ver2.csv \
-U ALLOW_N_CIGAR_READS

I have downloaded geuvadis genotype data from geuvadis browser and indexed the ALL sites file.

Error:

ERROR MESSAGE: The provided VCF file is malformed at approximately line number 512: The VCF specification does not allow for whitespace in the INFO field. Offending field value was "AA=.;AC=51;AF=0.02;AFR_AF=0.02;ALLELE=A;AMR_AF=0.02;AN=2184;AVGPOST=0.9975;DAF_GLOBAL=.;ERATE=0.0004;EUR_AF=0.04;GENE_TRCOUNT_AFFECTED=1;GENE_TRCOUNT_TOTAL=1;GERP=.;LDAF=0.0238;RSQ=0.9610;SEVERE_GENE=ENSG00000197049;SEVERE_IMPACT=NON_SYNONYMOUS_CODON;SNPSOURCE=LOWCOV;THETA=0.0007;TR_AFFECTED=FULL;VT=SNP;ANNOTATION_CLASS=NON_SYNONYMOUS_CODON,ACTIVE_CHROM,NC_TRANSCRIPT_VARIANT&INTRON_VARIANT;A_A_CHANGE=F/I,.,.;A_A_LENGTH=169,.,.;A_A_POS=118,.,.;CELL=.,GM12878,.;CHROM_STATE=.,11,.;EXON_NUMBER=1/1,.,.;GENE_ID=ENSG00000197049,.,ENSG00000237491;GENE_NAME=AL669831.1,.,RP11-206L10.6;HGVS=c.352N>A,.,n.37+7285N>A;INTRON_NUMBER=.,.,1/2;POLYPHEN=probably damaging:0.982,.,-:-;SIFT=-:-,.,-:-;TR_BIOTYPE=PROTEIN_CODING,.,PROCESSED_TRANSCRIPT;TR_ID=ENST00000358533,.,ENST00000429505;TR_LENGTH=1194,.,441;TR_POS=438,.,.;TR_STRAND=1,.,1", for input source: ALL.phase1_release_v3.20101123.snps_indels_sv.sites.gdid.gdannot.v2.vcf.gz

I tried to remove spaces in INFO field of vcf and ran again the same with no success.

Could you please help me to resolve this issue.

Thanks in Advance
Fazulur Rehaman

I am using the SLURM-DRMAA bridge and the pipeline throws an obstinate error. The obstinate error is "org.ggf.drmaa.InternalException: slurm_submit_batch_job: Invalid account or account/partition combination specified", but there are other errors when trying things differently. So it makes sense to ask here. If I could get access to the script being submitted to the cluster then I could investigate what is it that the SLURM system hates in my parameter specifications. Can I do that? From the error listing, since the Java machine catches the error it basically fails by delegating the error message to my local system, I would argue that it is bad practice.

Note that running things locally with -run and no -jobRunner specs works, but I want to use the cluster. My system admin says that since the processing stops at the pipeline level and there is no SLURM submission he cannot really help me and suggested me to try different combinations. Here is what I have tried and the errors.

- -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative "-A sens2016011-bianca" -jobNative "-p node" fails with org.ggf.drmaa.InternalException: slurm_submit_batch_job: Invalid account or account/partition combination specified
- -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative "-A sens2016011-bianca" -jobNative "-p core" -jobNative "-n 1" fails with org.ggf.drmaa.InternalException: slurm_submit_batch_job: Invalid account or account/partition combination specified
- -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative "-A sens2016011-bianca" -jobNative "-p core" -jobNative "-n 1" -jobNative "-t 20:00" same error as above
- -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative "-A sens2016011-bianca" fails with Too many cores requested for -p core partition. Minimum cpus requested is 4294967294. To use more than  16 cores, request -p node.
- -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative "-A sens2016011-bianca" -jobNative "-N 1" same as above
- -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative A sens2016011-bianca -jobNative p core -jobNative N 1 fails with Unable to submit job: Invalid native specification: A sens2016011-bianca p core N 1
- -run -jobRunner Drmaa fails with Use the flag -A to specify an active project with allocation on this cluster.

Here is the full error listing:

$ java -Xmx4g -cp /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/SVToolkit.jar:/proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/gatk/GenomeAnalysisTK.jar:/proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/gatk/Queue.jar org.broadinstitute.gatk.queue.QCommandLine -S /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/qscript/SVPreprocess.q -S /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/qscript/SVQScript.q -gatk /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/gatk/GenomeAnalysisTK.jar -configFile /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/conf/genstrip_parameters.txt -R /sw/data/uppnex/GATK/2.8/b37/human_g1k_v37.fasta -I /proj/sens2016011/nobackup/melt/data/bam_links/00028285.sorted.bam -md meta -bamFilesAreDisjoint true -jobLogDir /proj/sens2016011/nobackup/genomestrip/tests/logs -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative "-A sens2016011-bianca" -jobNative "-p core" -jobNative "-n 1" -jobNative "-t 20:00"
INFO  17:10:33,709 QScriptManager - Compiling 2 QScripts 
INFO  17:11:13,568 QScriptManager - Compilation complete 
INFO  17:11:13,936 HelpFormatter - ---------------------------------------------------------------------- 
INFO  17:11:13,936 HelpFormatter - Queue v3.7.GS-r1748-0-g74bfe0b, Compiled 2018/04/10 10:30:23 
INFO  17:11:13,936 HelpFormatter - Copyright (c) 2012 The Broad Institute 
INFO  17:11:13,936 HelpFormatter - For support and documentation go to http://www.broadinstitute.org/gatk 
INFO  17:11:13,937 HelpFormatter - Program Args: -S /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/qscript/SVPreprocess.q -S /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/qscript/SVQScript.q -gatk /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/gatk/GenomeAnalysisTK.jar -configFile /proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/conf/genstrip_parameters.txt -R /sw/data/uppnex/GATK/2.8/b37/human_g1k_v37.fasta -I /proj/sens2016011/nobackup/melt/data/bam_links/00028285.sorted.bam -md meta -bamFilesAreDisjoint true -jobLogDir /proj/sens2016011/nobackup/genomestrip/tests/logs -run -jobRunner Drmaa -gatkJobRunner Drmaa -jobNative -A sens2016011-bianca -jobNative -p core -jobNative -n 1 -jobNative -t 20:00 
INFO  17:11:13,937 HelpFormatter - Executing as sergiun@sens2016011-bianca.uppmax.uu.se on Linux 3.10.0-862.3.2.el7.x86_64 amd64; OpenJDK 64-Bit Server VM 1.8.0_171-b10. 
INFO  17:11:13,938 HelpFormatter - Date/Time: 2018/08/28 17:11:13 
INFO  17:11:13,938 HelpFormatter - ---------------------------------------------------------------------- 
INFO  17:11:13,938 HelpFormatter - ---------------------------------------------------------------------- 
INFO  17:11:13,953 QCommandLine - Scripting SVPreprocess 
INFO  17:11:15,238 QCommandLine - Added 190 functions 
INFO  17:11:15,257 QGraph - Generating graph. 
INFO  17:11:15,351 QGraph - Running jobs. 
INFO  17:11:17,092 FunctionEdge - Starting:  'java'  '-Xmx2048m'  '-XX:+UseParallelOldGC'  '-XX:ParallelGCThreads=4'  '-XX:GCTimeLimit=50'  '-XX:GCHeapFreeLimit=10'  '-Djava.io.tmpdir=/castor/project/proj_nobackup/genomestrip/tests/batch/.queue/tmp'  '-cp' '/proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/SVToolkit.jar:/proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/gatk/GenomeAnalysisTK.jar:/proj/sens2016011/nobackup/genomestrip/lib/svtoolkit/lib/gatk/Queue.jar'  'org.broadinstitute.sv.apps.ComputeGenomeSizes'  '-O' '/castor/project/proj_nobackup/genomestrip/tests/batch/meta/genome_sizes.txt'  '-R' '/sw/data/uppnex/GATK/2.8/b37/human_g1k_v37.fasta'    
INFO  17:11:17,093 FunctionEdge - Output written to /proj/sens2016011/nobackup/genomestrip/tests/logs/SVPreprocess-5.out 
ERROR 17:11:17,119 Retry - Caught error during attempt 1 of 4. 
org.ggf.drmaa.InternalException: slurm_submit_batch_job: Invalid account or account/partition combination specified
        at org.broadinstitute.gatk.utils.jna.drmaa.v1_0.JnaSession.checkError(JnaSession.java:400)
        at org.broadinstitute.gatk.utils.jna.drmaa.v1_0.JnaSession.checkError(JnaSession.java:392)
        at org.broadinstitute.gatk.utils.jna.drmaa.v1_0.JnaSession.runJob(JnaSession.java:79)
        at org.broadinstitute.gatk.queue.engine.drmaa.DrmaaJobRunner.runJob(DrmaaJobRunner.scala:115)
        at org.broadinstitute.gatk.queue.engine.drmaa.DrmaaJobRunner$$anonfun$start$1.apply$mcV$sp(DrmaaJobRunner.scala:93)
        at org.broadinstitute.gatk.queue.engine.drmaa.DrmaaJobRunner$$anonfun$start$1.apply(DrmaaJobRunner.scala:91)
        at org.broadinstitute.gatk.queue.engine.drmaa.DrmaaJobRunner$$anonfun$start$1.apply(DrmaaJobRunner.scala:91)
        at org.broadinstitute.gatk.queue.util.Retry$.attempt(Retry.scala:50)
        at org.broadinstitute.gatk.queue.engine.drmaa.DrmaaJobRunner.start(DrmaaJobRunner.scala:91)
        at org.broadinstitute.gatk.queue.engine.FunctionEdge.start(FunctionEdge.scala:101)
        at org.broadinstitute.gatk.queue.engine.QGraph.startOneJob(QGraph.scala:646)
        at org.broadinstitute.gatk.queue.engine.QGraph.runJobs(QGraph.scala:507)
        at org.broadinstitute.gatk.queue.engine.QGraph.run(QGraph.scala:168)
        at org.broadinstitute.gatk.queue.QCommandLine.execute(QCommandLine.scala:170)
        at org.broadinstitute.gatk.utils.commandline.CommandLineProgram.start(CommandLineProgram.java:256)
        at org.broadinstitute.gatk.utils.commandline.CommandLineProgram.start(CommandLineProgram.java:158)
        at org.broadinstitute.gatk.queue.QCommandLine$.main(QCommandLine.scala:61)
        at org.broadinstitute.gatk.queue.QCommandLine.main(QCommandLine.scala)
ERROR 17:11:17,121 Retry - Retrying in 1.0 minute. 

Hi there,

I've removed INDELS from a multi-sample vcf from HaplotypeCaller using SelectVariants. However, the ALT 'SNPs' are more than a single nucleotide substitution. Eg.

TTTTTTGTTTTTTGTTTT,GTTTTTGTTTT,G
TTTTTTTA,*
TTTTTTTAG,*
TTTTTTTATTTTTCATTTA,*
TTTTTGTTTTTTTA,TC,*

Q1) What is the meaning of the * symbol?
Q2) Is it to be expected that these SNPs are more than a single nucleotide substitution?

Thanks,
Tom

I experience Issues with GenotypeGVCFs and GenomicsDB input in the final GATK4 release using the official Docker image. This does not occur using the 4.beta.6 release. It looks like there has been a bug in an earlier beta release with the same error message which got fixed. Is my issue related to that old bug or just results in the same error message? What can I do to debug the issue?

2018-01-10T12:15:04.154516155Z terminate called after throwing an instance of 'VariantQueryProcessorException'
2018-01-10T12:15:04.154547266Z   what():  VariantQueryProcessorException : Could not open array genomicsdb_array at workspace: /keep/d22f668d4f44631d98bc650d582975ca+1399/chr22_db
2018-01-10T12:15:04.154561314Z 
2018-01-10T12:15:04.620517615Z Using GATK wrapper script /gatk/build/install/gatk/bin/gatk
2018-01-10T12:15:04.620517615Z Running:
2018-01-10T12:15:04.620517615Z     /gatk/build/install/gatk/bin/gatk GenotypeGVCFs -V gendb:///keep/d22f668d4f44631d98bc650d582975ca+1399/chr22_db --output chr22_db.vcf --reference /keep/db91e5f04cbd9018e42708316c28e82d+2160/hg19.fa

Hello

I used the GATK Best Practices workflow for variant calling on RNAseq, but I have a question regarding the filter recommendations for the variant filtering step. According to your workflow, you recommend using the filters '–window 35, –cluster 3, FS > 30.0 and QD < 2.0'. However, in https://software.broadinstitute.org/gatk/documentation/article.php?id=3225, the recommended arguments to use with VariantFiltration for SNPs are 'QD < 2.0, MQ < 40.0, FS > 60.0, SOR > 3.0, MQRankSum > -12.5 and ReadPosRankSum < -8.0'. Is this more stringent option only recommended for DNA-seq data? What is the reason for the different recommendations?

Thank you!

This document outlines the basic operation of the HaplotypeCaller run in its default mode on a single sample, and does not cover the additional processing and calculations done when it is run in "GVCF mode" (with -ERC GVCF or -ERC BP_RESOLUTION) or when it is run on multiple samples. For more details and discussion of the GVCF workflow, see the Best Practices documentation on germline short variant discovery as well as the HaplotypeCaller manuscript on bioRxiv.

Overview

The core operations performed by HaplotypeCaller can be grouped into these major steps:

1. Define active regions. The program determines which regions of the genome it needs to operate on, based on the presence of significant evidence for variation.

2. Determine haplotypes by re-assembly of the active region. For each ActiveRegion, the program builds a De Bruijn-like graph to reassemble the ActiveRegion and identifies what are the possible haplotypes present in the data. The program then realigns each haplotype against the reference haplotype using the Smith-Waterman algorithm in order to identify potentially variant sites.

3. Determine likelihoods of the haplotypes given the read data. For each ActiveRegion, the program performs a pairwise alignment of each read against each haplotype using the PairHMM algorithm. This produces a matrix of likelihoods of haplotypes given the read data. These likelihoods are then marginalized to obtain the likelihoods of alleles per read for each potentially variant site.

4. Assign sample genotypes. For each potentially variant site, the program applies Bayes’ rule, using the likelihoods of alleles given the read data to calculate the posterior likelihoods of each genotype per sample given the read data observed for that sample. The most likely genotype is then assigned to the sample.

1. Define active regions

In this first step, the program traverses the sequencing data to identify regions of the genomes in which the samples being analyzed show substantial evidence of variation relative to the reference. The resulting areas are defined as “active regions”, and will be passed on to the next step. Areas that do not show any variation beyond the expected levels of background noise will be skipped in the next step. This aims to accelerate the analysis by not wasting time performing reassembly on regions that are identical to the reference anyway.

To define these active regions, the program operates in three phases. First, it computes an activity score for each individual genome position, yielding the raw activity profile, which is a wave function of activity per position. Then, it applies a smoothing algorithm to the raw profile, which is essentially a sort of averaging process, to yield the actual activity profile. Finally, it identifies local maxima where the activity profile curve rises above the preset activity threshold, and defines appropriate intervals to encompass the active profile within the preset size constraints. For more details on how the activity profile is computed and processed, as well as what options are available to modify the active region parameters, please see this article.

Once this process is complete, the program applies a few post-processing steps to finalize the the active regions (see detailed doc above). The final output of this process is a list of intervals corresponding to the active regions which will be processed in the next step.

2. Determine haplotypes by local assembly of the active region.

The goal of this step is to reconstruct the possible sequences of the real physical segments of DNA present in the original sample organism. To do this, the program goes through each active region and uses the input reads that mapped to that region to construct complete sequences covering its entire length, which are called haplotypes. This process will typically generate several different possible haplotypes for each active region due to:

• real diversity on polyploid (including CNV) or multi-sample data
• possible allele combinations between variant sites that are not totally linked within the active region
• sequencing and mapping errors

In order to generate a list of possible haplotypes, the program first builds an assembly graph for the active region using the reference sequence as a template. Then, it takes each read in turn and attempts to match it to a segment of the graph. Whenever portions of a read do not match the local graph, the program adds new nodes to the graph to account for the mismatches. After this process has been repeated with many reads, it typically yields a complex graph with many possible paths. However, because the program keeps track of how many reads support each path segment, we can select only the most likely (well-supported) paths. These likely paths are then used to build the haplotype sequences which will be used for scoring and genotyping in the next step.

The assembly and haplotype determination procedure is described in full detail in this method article.

Once the haplotypes have been determined, each one is realigned against the original reference sequence in order to identify potentially variant sites. This produces the set of sites that will be processed in the next step. A subset of these sites will eventually be emitted as variant calls to the output VCF.

3. Evaluating the evidence for haplotypes and variant alleles

Now that we have all these candidate haplotypes, we need to evaluate how much evidence there is in the data to support each one of them. So the program takes each individual read and aligns it against each haplotype in turn (including the reference haplotype) using the PairHMM algorithm, which takes into account the information we have about the quality of the data (i.e. the base quality scores and indel quality scores). This outputs a score for each read-haplotype pairing, expressing the likelihood of observing that read given that haplotype.

Those scores are then used to calculate out how much evidence there is for individual alleles at the candidate sites that were identified in the previous step. The process is called marginalization over alleles and produces the actual numbers that will finally be used to assign a genotype to the sample in the next step.

For further details on the pairHMM output and the marginalization process, see this document.

4. Assigning per-sample genotypes

The previous step produced a table of per-read allele likelihoods for each candidate variant site under consideration. Now, all that remains to do is to evaluate those likelihoods in aggregate to determine what is the most likely genotype of the sample at each site. This is done by applying Bayes' theorem to calculate the likelihoods of each possible genotype, and selecting the most likely. This produces a genotype call as well as the calculation of various metrics that will be annotated in the output VCF if a variant call is emitted.

For further details on the genotyping calculations, see this document.

This concludes the overview of how HaplotypeCaller works.

Hello, helpful experts of GATK --

I'm running into an issue using GenomicsDBImport on our cluster. It's creating a lot of large "temporary" files on the compute nodes, which remain even after GATK has finished running. The filenames are of this format: "libtiledbgenomicsdb984630309282992004.so". Since I'm working with a transcriptome, I'm running GenomicsDBImport over quite a few different contigs and these files are really adding up. I can manually delete them, but this presents a problem for running batched jobs over multiple days. On one node they took up 90G over a weekend run, which was enough to impact node performance (causing our HPC person to ask me what was going on).

Is there anything I can do about this? I tried GenotypeGVCFs and it was unworkably slow. Wondering if there's a flag I'm missing or something else that will tell GenomicsDBImport to delete these files once it's done with them. I want to be a good cluster user, so it would be great to know if there's an alternative user-side strategy to manually deleting them periodically.

Thank you!
Carolyn

i am running the example workflow locally following instrctions here:
https://gatkforums.broadinstitute.org/gatk/discussion/12521/how-to-execute-workflows-from-the-gatk-workflows-git-organization

I got an error. what went wrong? Is this due to the bam file itself? thanks

szong@gphost08 gatk-workflows]$ /gsc/software/linux-x86_64/jre1.8.0_66/bin/java -jar cromwell-33.1.jar run ./seq-format-validation/validate-bam.wdl --inputs ./seq-format-validation/validate-bam.inputs.json[2018-09-24 16:53:11,97] [info] Running with database db.url = jdbc:hsqldb:mem:5a8b3c34-4e77-4260-b284-156e4ff20c55;shutdown=false;hsqldb.tx=mvcc
[2018-09-24 16:53:21,55] [info] Running migration RenameWorkflowOptionsInMetadata with a read batch size of 100000 and a write batch size of 100000
[2018-09-24 16:53:21,57] [info] [RenameWorkflowOptionsInMetadata] 100%
[2018-09-24 16:53:21,67] [info] Running with database db.url = jdbc:hsqldb:mem:3e6a191d-dc8a-4990-b523-dec612287786;shutdown=false;hsqldb.tx=mvcc
[2018-09-24 16:53:22,19] [info] Slf4jLogger started
[2018-09-24 16:53:22,61] [info] Workflow heartbeat configuration:
{
"cromwellId" : "cromid-d687857",
"heartbeatInterval" : "2 minutes",
"ttl" : "10 minutes",
"writeBatchSize" : 10000,
"writeThreshold" : 10000
}
[2018-09-24 16:53:22,68] [info] Metadata summary refreshing every 2 seconds.
[2018-09-24 16:53:22,72] [info] WriteMetadataActor configured to flush with batch size 200 and process rate 5 seconds.
[2018-09-24 16:53:22,72] [info] KvWriteActor configured to flush with batch size 200 and process rate 5 seconds.
[2018-09-24 16:53:22,72] [info] CallCacheWriteActor configured to flush with batch size 100 and process rate 3 seconds.
[2018-09-24 16:53:24,02] [info] JobExecutionTokenDispenser - Distribution rate: 50 per 1 seconds.
[2018-09-24 16:53:24,04] [info] SingleWorkflowRunnerActor: Submitting workflow
[2018-09-24 16:53:24,12] [info] Unspecified type (Unspecified version) workflow 92883a94-765a-414d-a0aa-9fa3f5a2e6af submitted
[2018-09-24 16:53:24,19] [info] SingleWorkflowRunnerActor: Workflow submitted 92883a94-765a-414d-a0aa-9fa3f5a2e6af
[2018-09-24 16:53:24,19] [info] 1 new workflows fetched
[2018-09-24 16:53:24,19] [info] WorkflowManagerActor Starting workflow 92883a94-765a-414d-a0aa-9fa3f5a2e6af
[2018-09-24 16:53:24,20] [info] WorkflowManagerActor Successfully started WorkflowActor-92883a94-765a-414d-a0aa-9fa3f5a2e6af
[2018-09-24 16:53:24,20] [info] Retrieved 1 workflows from the WorkflowStoreActor
[2018-09-24 16:53:24,21] [warn] SingleWorkflowRunnerActor: received unexpected message: Done in state RunningSwraData
[2018-09-24 16:53:24,23] [warn] Couldn't find a suitable DSN, defaulting to a Noop one.
[2018-09-24 16:53:24,24] [info] Using noop to send events.
[2018-09-24 16:53:24,27] [info] WorkflowStoreHeartbeatWriteActor configured to flush with batch size 10000 and process rate 2 minutes.
[2018-09-24 16:53:24,30] [info] MaterializeWorkflowDescriptorActor [92883a94]: Parsing workflow as WDL draft-2
[2018-09-24 16:53:25,35] [info] MaterializeWorkflowDescriptorActor [92883a94]: Call-to-Backend assignments: ValidateBamsWf.ValidateBAM -> Local
[2018-09-24 16:53:25,50] [warn] Local [92883a94]: Key/s [memory, cpu, disks] is/are not supported by backend. Unsupported attributes will not be part of job executions.
[2018-09-24 16:53:31,01] [info] WorkflowExecutionActor-92883a94-765a-414d-a0aa-9fa3f5a2e6af [92883a94]: Starting ValidateBamsWf.ValidateBAM
[2018-09-24 16:53:32,48] [warn] BackgroundConfigAsyncJobExecutionActor [92883a94ValidateBamsWf.ValidateBAM:0:1]: Unrecognized runtime attribute keys: disks, cpu, memory
[2018-09-24 16:53:33,50] [info] BackgroundConfigAsyncJobExecutionActor [92883a94ValidateBamsWf.ValidateBAM:0:1]: /gatk/gatk \
ValidateSamFile \
--INPUT /cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/inputs/362257838/NA12878_24RG_small.hg38.bam \
--OUTPUT NA12878_24RG_small.hg38.validation_SUMMARY.txt \
--MODE SUMMARY
[2018-09-24 16:53:33,56] [info] BackgroundConfigAsyncJobExecutionActor [92883a94ValidateBamsWf.ValidateBAM:0:1]: executing: # make sure there is no preexisting Docker CID file
rm -f /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/docker_cid

run as in the original configuration without --rm flag (will remove later)

docker run \
--cidfile /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/docker_cid \
-i \
\
--entrypoint /bin/bash \
-v /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0:/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0 \
broadinstitute/gatk@sha256:497185dbf683c3ae5deda0c0b42e641fad04d724d0cb887dd7d601fe30b96ca2 /cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/script

get the return code (working even if the container was detached)

rc=$(docker wait cat /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/docker_cid)

remove the container after waiting

docker rm cat /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/docker_cid

return exit code

exit $rc
[2018-09-24 16:53:37,77] [info] BackgroundConfigAsyncJobExecutionActor [92883a94ValidateBamsWf.ValidateBAM:0:1]: job id: 123319
[2018-09-24 16:53:37,78] [info] BackgroundConfigAsyncJobExecutionActor [92883a94ValidateBamsWf.ValidateBAM:0:1]: Status change from - to Done
[2018-09-24 16:53:38,56] [error] WorkflowManagerActor Workflow 92883a94-765a-414d-a0aa-9fa3f5a2e6af failed (during ExecutingWorkflowState): Job ValidateBamsWf.ValidateBAM:0:1 exited with return code -1 which has not been declared as a valid return code. See 'continueOnReturnCode' runtime attribute for more details.
Check the content of stderr for potential additional information: /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/stderr.
Could not retrieve content: /projects/da_workspace/software/gatk-workflows/cromwell-executions/ValidateBamsWf/92883a94-765a-414d-a0aa-9fa3f5a2e6af/call-ValidateBAM/shard-0/execution/stderr
[2018-09-24 16:53:38,56] [info] WorkflowManagerActor WorkflowActor-92883a94-765a-414d-a0aa-9fa3f5a2e6af is in a terminal state: WorkflowFailedState
[2018-09-24 16:53:45,29] [info] SingleWorkflowRunnerActor workflow finished with status 'Failed'.
[2018-09-24 16:53:47,74] [info] Workflow polling stopped
[2018-09-24 16:53:47,76] [info] Shutting down WorkflowStoreActor - Timeout = 5 seconds
[2018-09-24 16:53:47,76] [info] Shutting down WorkflowLogCopyRouter - Timeout = 5 seconds
[2018-09-24 16:53:47,77] [info] Shutting down JobExecutionTokenDispenser - Timeout = 5 seconds
[2018-09-24 16:53:47,77] [info] Aborting all running workflows.
[2018-09-24 16:53:47,77] [info] JobExecutionTokenDispenser stopped
[2018-09-24 16:53:47,77] [info] WorkflowStoreActor stopped
[2018-09-24 16:53:47,78] [info] WorkflowLogCopyRouter stopped
[2018-09-24 16:53:47,78] [info] Shutting down WorkflowManagerActor - Timeout = 3600 seconds
[2018-09-24 16:53:47,78] [info] WorkflowManagerActor All workflows finished
[2018-09-24 16:53:47,78] [info] WorkflowManagerActor stopped
[2018-09-24 16:53:47,78] [info] Connection pools shut down
[2018-09-24 16:53:47,79] [info] Shutting down SubWorkflowStoreActor - Timeout = 1800 seconds
[2018-09-24 16:53:47,79] [info] Shutting down JobStoreActor - Timeout = 1800 seconds
[2018-09-24 16:53:47,79] [info] Shutting down CallCacheWriteActor - Timeout = 1800 seconds
[2018-09-24 16:53:47,79] [info] SubWorkflowStoreActor stopped
[2018-09-24 16:53:47,79] [info] Shutting down ServiceRegistryActor - Timeout = 1800 seconds
[2018-09-24 16:53:47,79] [info] Shutting down DockerHashActor - Timeout = 1800 seconds
[2018-09-24 16:53:47,79] [info] Shutting down IoProxy - Timeout = 1800 seconds
[2018-09-24 16:53:47,79] [info] JobStoreActor stopped
[2018-09-24 16:53:47,79] [info] CallCacheWriteActor Shutting down: 0 queued messages to process
[2018-09-24 16:53:47,79] [info] KvWriteActor Shutting down: 0 queued messages to process
[2018-09-24 16:53:47,79] [info] WriteMetadataActor Shutting down: 0 queued messages to process
[2018-09-24 16:53:47,79] [info] CallCacheWriteActor stopped
[2018-09-24 16:53:47,79] [info] DockerHashActor stopped
[2018-09-24 16:53:47,79] [info] IoProxy stopped
[2018-09-24 16:53:47,80] [info] ServiceRegistryActor stopped
[2018-09-24 16:53:47,83] [info] Database closed
[2018-09-24 16:53:47,83] [info] Stream materializer shut down
Workflow 92883a94-765a-414d-a0aa-9fa3f5a2e6af transitioned to state Failed
[2018-09-24 16:53:47,89] [info] Automatic shutdown of the async connection
[2018-09-24 16:53:47,89] [info] Gracefully shutdown sentry threads.
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