I need to produce a set of all DNA-level variants for cancer patients to serve as exclusion sites for RNA editing site finding. Currently have have matched Tumor-Normal WES data for these patients. Which of the following would be the most appropriate way to produce these?
Hello,
Here I have a question about downsample_to_coverage in HaplotypeCaller. I found -dcov cannot be used in HaplotypeCaller and I tried to change the values of parameters maxReadsInRegionPerSample and minReadsPerAlignStart to change the coverage level, but what I got the coverage of result files is still default coverage level.
so I wanna ask what parameter in HaplotypeCaller could change the level of coverage? if they are above two parameters, then how could I increase the downsample_coverage?
Hello,
I have a cohort of several sibling pairs, and I am interested in finding Germline SNPs for each pair. Furthermore, I understand from the best practices that a joint analysis using the Haplotype Caller works very well. I am not sure how to proceed. Is it reasonable/possible to do the following?:
1) Haplotype Caller to find SNPs between each pair <- is this possible with the Haplotype caller? It seems like it is meant to handle one or more samples against a single reference sequence.
2) Joint genotyping using then haplotype caller
Thank you in advance, any help appreciated,
Ramiro
We have some important news to share with you regarding the licensing of GATK and MuTect. The licensing agreement between us and Appistry will end effective April 15, 2015; from that point on, the tools will continue to be licensed through Broad for commercial entities that will be running the GATK and MuTect internally or as part of their own hardware offering. Current licensed users will transition to Broad Institute when their current license expires.
For our academic and non-profit GATK and MuTect users, the licensing transition will be essentially transparent. You will still be able to use the GATK and MuTect for free, and access the source code through the existing public repository. The support forum and documentation website will also remain operational and freely accessible to all as they been previously.
Since our commercial users will now get their license --and their support!-- directly from Broad, they can expect to see some clear benefits:
Better access.
We have heard from our licensed user community that you would like greater access to the GATK support team. Licensing through Broad will remove intermediaries and allow all GATK and MuTect users --including those who purchase a license-- more direct contact with and support from our team.
Most current tools.
Getting your GATK license through Broad license will give you access to the most cutting-edge tools and features available without sacrificing support. Under Broad licensing, you will still have the option of purchasing a license for either of two packages, “GATK” or “GATK + Cancer Tools”.
Up-to-date Best Practices recommendations.
You will get our latest recommendations, informed by the latest in our internal analysis and internal R&D work, directly from us. So you can be confident that you have access to the freshest information at all times.
Our development team is driven by the goal of building tools to enable better science and push the boundaries of genome analysis. Revenue from GATK and MuTect licensing enables these goals by directly feeding into GATK and MuTect development, in the form of critical codebase maintenance and bug fixing work, as well as expansion of the support team. This enables us to keep pace with the growth of the user community and the ever-increasing demand for GATK and MuTect support.
This is a significant new milestone in the life of GATK and MuTect, and we recognize that there are going to be a lot of questions and discussions on this topic since it will affect many of you in the research community. We’ve put together some FAQs (below the fold) that we hope will answer your most pressing questions; feel free to comment and suggest additional points that you think should be covered there.
Note that we are still working on defining some of the finer points of the support model and pricing structure, so we can’t address those quite yet -- but feel free to email softwarelicensing@broadinstitute.org if you have some burning question and/or concern that you’d like to discuss regarding licensing and/or pricing in particular. Rest assured that once the model has been finalized, we will make the full details (including pricing) available on our website in order to ensure full transparency.
Academic/non-profit users: No change. The licensing terms remain the same and the GATK remains free to use for not-for-profit research and educational purposes. The current free user support model will remain available through the online forum.
Currently licensed Commercial/for-profit users: Appistry will continue to provide full GATK support for the remainder of your current license term. After that point, Broad Institute can offer you a GATK license directly. This will offer you immediate access to the latest version of GATK. We can work directly with you on your specific licensing questions at softwarelicensing@broadinstitute.org. For support questions, GATK product upgrade information or other suggestions, please comment in the discussion below or send us a private message.
Prospective commercial/for-profit users: Broad Institute can offer you a GATK license directly. This will offer you immediate access to the latest version of GATK. We can work directly with you on your specific licensing questions at softwarelicensing@broadinstitute.org. For support questions, GATK product upgrade information or other suggestions, please comment in the discussion below or send us a private message.
No. There will only be one version for all users. We will provide our licensed users total support for the very latest version. This means they will always be able to use the most cutting-edge tools and features available without sacrificing support.
Part of the Broad Institute’s mission is to share our tools and research as broadly as possible to enable others to do transformational research. We started developing GATK several years ago and, since then, have constantly upgraded it, thanks to the hard work and dedication of many talented programmers, developers and genomic researchers in our group and beyond. That is why GATK remains the most advanced, accurate and reliable toolkit for variant discovery available anywhere (if we do say so ourselves). But please understand that building, maintaining, testing and constantly improving GATK is neither easy nor free. This is why we charge commercial users a licensing fee and funnel these resources back into upgrades of the tool itself – it allows us to continue to offer GATK for free to academic and non-profit organizations while ensuring it is always the best-of-the-best in an emerging and rapidly-changing field of research.
In addition to the GATK package, we will also offer a package that bundles GATK with MuTect and ContEst. That package will not include the SomaticIndelDetector, but a replacement for that functionality is in preparation.
No. Tools originating from the Picard toolkit will remain free and fully open-source for everyone. We are preparing to integrate them into a part of GATK that will be under a BSD license.
We are preparing to enable this in order to facilitate sharing of scientific methods, but we need input from the community first. To that end, we’d like to invite researchers who are developing or have developed such pipeline images to contact us in order to discuss options. We are envisioning simple technical solutions to ensure that users of these images are made fully aware of their own legal responsibility relative to the GATK licensing status, in a way that minimizes the burden on the researchers who distribute them.
Hi, I am running PhaseByTransition on a set of 250+ family trios. I am getting this error message:
Sample lgsnd32563jz3 found in data sources but not in pedigree files with STRICT pedigree validation
However, the sample is in the pedigree file. I tried adding the flag recommended in previous forums - --pedigreeValidationType SILENT, but then all the trios were excluded.
Can you advise me on what the problem could be? Here is my command:
java -Xmx8g -jar GenomeAnalysisTK.jar -T PhaseByTransmission -R /Volumes/Thunderbolt/ref_genome/human_g1k_v37.fasta -V /Volumes/Passport2/July2016/merged_vcf/Trios_GATK_all.dbID.db.eff.vcf -ped /Users/Yam/Desktop/Trios.ped -o /Volumes/Passport2/July2016/merged_vcf/Trios_GATK_all.dbID.db.eff_phased.vcf
Hello
After hard filter a callset that is too small for VQSR I wanted to make sure that it is okay to still use CalculatePostProb on a targeted panels (i.e. 10-50 genes).
James
Version 3.1.1. Human normal samples.
I couldnt find AlleleBalance and AlleleBalanceBySample tags in my vcf outputs. Tags are not found even for single variant
I tried HaplotypeCaller with -all or directly with -A AlleleBalance or -A AlleleBalanceBySample.
Also I tried Variantannotator with -all or -A AlleleBalance or -A AlleleBalanceBySample.
Any help will be apreciated
GATK-Queue is command-line scripting framework for defining multi-stage genomic analysis pipelines combined with an execution manager that runs those pipelines from end-to-end. Often processing genome data includes several steps to produces outputs, for example our BAM to VCF calling pipeline include among other things:
Local realignment around indels
Emitting raw SNP calls
Running these tools one by one in series may often take weeks for processing, or would require custom scripting to try and optimize using parallel resources.
With a Queue script users can semantically define the multiple steps of the pipeline and then hand off the logistics of running the pipeline to completion. Queue runs independent jobs in parallel, handles transient errors, and uses various techniques such as running multiple copies of the same program on different portions of the genome to produce outputs faster.
You have two options: download the binary distribution (prepackaged, ready to run program) or build it from source.
This is obviously the easiest way to go. Links are on the Downloads page. Just get the Queue package; no need to get the GATK package separately as GATK is bundled in with Queue.
Briefly, here's what you need to know/do:
Queue is part of the GATK repository. Download the source from the public repository on Github. Run the following command:
git clone https://github.com/broadgsa/gatk.git
IMPORTANT NOTE: These instructions refer to the MIT-licensed version of the GATK+Queue source code. With that version, you will be able to build Queue itself, as well as the public portion of the GATK (the core framework), but that will not include the GATK analysis tools. If you want to use Queue to pipeline the GATK analysis tools, you need to clone the 'protected' repository. Please note however that part of the source code in that repository (the 'protected' module) is under a different license which excludes for-profit use, modification and redistribution.
Move to the git root directory and use maven to build the source.
mvn clean verify
All dependencies will be managed by Maven as needed.
See this article on how to test your installation of Queue.
See this article on running Queue for the first time for full details.
Queue arguments can be listed by running with --help
java -jar dist/Queue.jar --help
To list the arguments required by a QScript, add the script with -S and run with --help.
java -jar dist/Queue.jar -S script.scala --help
Note that by default queue runs in a "dry" mode, as explained in the link above. After verifying the generated commands execute the pipeline by adding -run.
See QFunction and Command Line Options for more info on adjusting Queue options.
Queue pipelines are written as Scala 2.8 files with a bit of syntactic sugar, called QScripts.
Every QScript includes the following steps:
New instances of CommandLineFunctions are created
Input and output arguments are specified on each function
add() to Queue for dispatch and monitoringThe basic command-line to run the Queue pipelines on the command line is
java -jar Queue.jar -S <script>.scala
See the main article Queue QScripts for more info on QScripts.
Most QScripts are analysis pipelines that are custom-built for specific projects, and we currently do not offer any QScripts as supported analysis tools. However, we do provide some example scripts that you can use as basis to write your own QScripts (see below).
The latest version of the example files are available in the Sting github repository under public/scala/qscript/examples
Queue automatically generates GATKReport-formatted runtime information about executed jobs. See this presentation for a general introduction to QJobReport.
Note that Queue attempts to generate a standard visualization using an R script in the GATK public/R repository. You must provide a path to this location if you want the script to run automatically. Additionally the script requires the gsalib to be installed on the machine, which is typically done by providing its path in your .Rprofile file:
bm8da-dbe ~/Desktop/broadLocal/GATK/unstable % cat ~/.Rprofile
.libPaths("/Users/depristo/Desktop/broadLocal/GATK/unstable/public/R/")
Note that gsalib is available from the CRAN repository so you can install it with the canonical R package install command.
The system only provides information about commands that have just run. Resuming from a partially completed job will only show the information for the jobs that just ran, and not for any of the completed commands. This is due to a structural limitation in Queue, and will be fixed when the Queue infrastructure improves
This feature only works for command line and LSF execution models. SGE should be easy to add for a motivated individual but we cannot test this capabilities here at the Broad. Please send us a patch if you do extend Queue to support SGE.
Queue emits a queue.dot file to help visualize your commands. You can open this file in programs like DOT, OmniGraffle, etc to view your pipelines. By default the system will print out your LSF command lines, but this can be too much in a complex pipeline.
To clarify your pipeline, override the dotString() function:
class CountCovariates(bamIn: File, recalDataIn: File, args: String = "") extends GatkFunction {
@Input(doc="foo") var bam = bamIn
@Input(doc="foo") var bamIndex = bai(bamIn)
@Output(doc="foo") var recalData = recalDataIn
memoryLimit = Some(4)
override def dotString = "CountCovariates: %s [args %s]".format(bamIn.getName, args)
def commandLine = gatkCommandLine("CountCovariates") + args + " -l INFO -D /humgen/gsa-hpprojects/GATK/data/dbsnp_129_hg18.rod -I %s --max_reads_at_locus 20000 -cov ReadGroupCovariate -cov QualityScoreCovariate -cov CycleCovariate -cov DinucCovariate -recalFile %s".format(bam, recalData)
}
Here we only see CountCovariates my.bam [-OQ], for example, in the dot file. The base quality score recalibration pipeline, as visualized by DOT, can be viewed here:
Hello,
I am trying to run Mutect2 in tumor only mode without a matching normal. However, when I did this, MuTect2 produced output VCFs that had a full header, but had no variant calls.
Here is a sample command that I ran to produce variant calls for chromosome 1:
module load gatk/3.5.0; java -Xmx10g -Djava.io.tmpdir=$TMP -jar GenomeAnalysisTK.jar -T MuTect2 -R $REFERENCE_GENOME_FA --dbsnp $DBSNP_VCF --cosmic $COSMIC_VCF -dt NONE --input_file:tumor $TUMOR_BAM --intervals chr1:1-249250621 -o $OUTPUT_VCF
Note: I ran a similar command to this (same input files, etc.) using MuTect v1.1.4 and it produced a complete VCF.
Can you please explain if there is anything I need to change?
Thank you,
Jeremy
I have a potential bug running GATK GenotypeGVCFs. It complains that there is a DP in the INFO field, but in my haplotypecaller-generated -mg.g.vcf.gz's I do not have a DP in the info, I do have DP in the FORMAT field though, but that's present in the headers as shown below the error output.
Any idea what could be the problem?
INFO 18:30:12,694 HelpFormatter - --------------------------------------------------------------------------------
INFO 18:30:12,698 HelpFormatter - The Genome Analysis Toolkit (GATK) v3.3-0-geee94ec, Compiled 2015/03/09 14:27:22
INFO 18:30:12,699 HelpFormatter - Copyright (c) 2010 The Broad Institute
INFO 18:30:12,699 HelpFormatter - For support and documentation go to http://www.broadinstitute.org/gatk
INFO 18:30:12,706 HelpFormatter - Program Args: -l INFO -T GenotypeGVCFs -R /net/NGSanalysis/ref/Mus_musculus.GRCm38/index/bwa/Mus_musculus.GRCm38.dna.primary_assembly.fa -o /dev/stdout -ploidy 2 --num_threads 32 --intervals:targets,BED /net/NGSanalysis/ref/Mus_musculus.GRCm38/bed/SeqCap/ex100/110624_MM10_exome_L2R_D02_EZ_HX1-ex100.bed --max_alternate_alleles 20 -V:3428_10_14_SRO_185_TGGCTTCA-mg,VCF 3428_10_14_SRO_185_TGGCTTCA-mg.g.vcf.gz -V:3428_11_14_SRO_186_TGGTGGTA-mg,VCF 3428_11_14_SRO_186_TGGTGGTA-mg.g.vcf.gz -V:3428_12_13_SRO_422_TTCACGCA-mg,VCF 3428_12_13_SRO_422_TTCACGCA-mg.g.vcf.gz -V:3428_13_13_SRO_492_AACTCACC-mg,VCF 3428_13_13_SRO_492_AACTCACC-mg.g.vcf.gz -V:3428_14_13_SRO_493_AAGAGATC-mg,VCF 3428_14_13_SRO_493_AAGAGATC-mg.g.vcf.gz -V:3428_15_14_SRO_209_AAGGACAC-mg,VCF 3428_15_14_SRO_209_AAGGACAC-mg.g.vcf.gz -V:3428_16_14_SRO_218_AATCCGTC-mg,VCF 3428_16_14_SRO_218_AATCCGTC-mg.g.vcf.gz -V:3428_17_14_SRO_201_AATGTTGC-mg,VCF 3428_17_14_SRO_201_AATGTTGC-mg.g.vcf.gz -V:3428_18_13_SRO_416_ACACGACC-mg,VCF 3428_18_13_SRO_416_ACACGACC-mg.g.vcf.gz -V:3428_19_14_SRO_66_ACAGATTC-mg,VCF 3428_19_14_SRO_66_ACAGATTC-mg.g.vcf.gz -V:3428_1_13_SRO_388_GTCGTAGA-mg,VCF 3428_1_13_SRO_388_GTCGTAGA-mg.g.vcf.gz -V:3428_20_14_SRO_68_AGATGTAC-mg,VCF 3428_20_14_SRO_68_AGATGTAC-mg.g.vcf.gz -V:3428_21_14_SRO_210_AGCACCTC-mg,VCF 3428_21_14_SRO_210_AGCACCTC-mg.g.vcf.gz -V:3428_22_14_SRO_256_AGCCATGC-mg,VCF 3428_22_14_SRO_256_AGCCATGC-mg.g.vcf.gz -V:3428_23_14_SRO_270_AGGCTAAC-mg,VCF 3428_23_14_SRO_270_AGGCTAAC-mg.g.vcf.gz -V:3428_24_13_SRO_452_ATAGCGAC-mg,VCF 3428_24_13_SRO_452_ATAGCGAC-mg.g.vcf.gz -V:3428_2_13_SRO_399_GTCTGTCA-mg,VCF 3428_2_13_SRO_399_GTCTGTCA-mg.g.vcf.gz -V:3428_3_13_SRO_461_GTGTTCTA-mg,VCF 3428_3_13_SRO_461_GTGTTCTA-mg.g.vcf.gz -V:3428_4_13_SRO_462_TAGGATGA-mg,VCF 3428_4_13_SRO_462_TAGGATGA-mg.g.vcf.gz -V:3428_5_13_SRO_465_TATCAGCA-mg,VCF 3428_5_13_SRO_465_TATCAGCA-mg.g.vcf.gz -V:3428_6_13_SRO_402_TCCGTCTA-mg,VCF 3428_6_13_SRO_402_TCCGTCTA-mg.g.vcf.gz -V:3428_7_13_SRO_474_TCTTCACA-mg,VCF 3428_7_13_SRO_474_TCTTCACA-mg.g.vcf.gz -V:3428_8_13_SRO_531_TGAAGAGA-mg,VCF 3428_8_13_SRO_531_TGAAGAGA-mg.g.vcf.gz -V:3428_9_14_SRO_166_TGGAACAA-mg,VCF 3428_9_14_SRO_166_TGGAACAA-mg.g.vcf.gz
INFO 18:30:12,714 HelpFormatter - Executing as roel@utonium.nki.nl on Linux 2.6.32-504.12.2.el6.x86_64 amd64; Java HotSpot(TM) 64-Bit Server VM 1.7.0_75-b13.
INFO 18:30:12,714 HelpFormatter - Date/Time: 2015/05/06 18:30:12
INFO 18:30:12,715 HelpFormatter - --------------------------------------------------------------------------------
INFO 18:30:12,715 HelpFormatter - --------------------------------------------------------------------------------
INFO 18:30:15,963 GenomeAnalysisEngine - Strictness is SILENT
INFO 18:30:16,109 GenomeAnalysisEngine - Downsampling Settings: Method: BY_SAMPLE, Target Coverage: 1000
INFO 18:30:29,705 IntervalUtils - Processing 101539431 bp from intervals
WARN 18:30:29,726 IndexDictionaryUtils - Track 3428_10_14_SRO_185_TGGCTTCA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,727 IndexDictionaryUtils - Track 3428_11_14_SRO_186_TGGTGGTA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,727 IndexDictionaryUtils - Track 3428_12_13_SRO_422_TTCACGCA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,728 IndexDictionaryUtils - Track 3428_13_13_SRO_492_AACTCACC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,728 IndexDictionaryUtils - Track 3428_14_13_SRO_493_AAGAGATC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,728 IndexDictionaryUtils - Track 3428_15_14_SRO_209_AAGGACAC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,729 IndexDictionaryUtils - Track 3428_16_14_SRO_218_AATCCGTC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,729 IndexDictionaryUtils - Track 3428_17_14_SRO_201_AATGTTGC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,730 IndexDictionaryUtils - Track 3428_18_13_SRO_416_ACACGACC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,730 IndexDictionaryUtils - Track 3428_19_14_SRO_66_ACAGATTC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,730 IndexDictionaryUtils - Track 3428_1_13_SRO_388_GTCGTAGA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,731 IndexDictionaryUtils - Track 3428_20_14_SRO_68_AGATGTAC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,731 IndexDictionaryUtils - Track 3428_21_14_SRO_210_AGCACCTC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,731 IndexDictionaryUtils - Track 3428_22_14_SRO_256_AGCCATGC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,732 IndexDictionaryUtils - Track 3428_23_14_SRO_270_AGGCTAAC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,732 IndexDictionaryUtils - Track 3428_24_13_SRO_452_ATAGCGAC-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,732 IndexDictionaryUtils - Track 3428_2_13_SRO_399_GTCTGTCA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,733 IndexDictionaryUtils - Track 3428_3_13_SRO_461_GTGTTCTA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,733 IndexDictionaryUtils - Track 3428_4_13_SRO_462_TAGGATGA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,733 IndexDictionaryUtils - Track 3428_5_13_SRO_465_TATCAGCA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,734 IndexDictionaryUtils - Track 3428_6_13_SRO_402_TCCGTCTA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,734 IndexDictionaryUtils - Track 3428_7_13_SRO_474_TCTTCACA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,734 IndexDictionaryUtils - Track 3428_8_13_SRO_531_TGAAGAGA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
WARN 18:30:29,735 IndexDictionaryUtils - Track 3428_9_14_SRO_166_TGGAACAA-mg doesn't have a sequence dictionary built in, skipping dictionary validation
INFO 18:30:29,749 MicroScheduler - Running the GATK in parallel mode with 32 total threads, 1 CPU thread(s) for each of 32 data thread(s), of 64 processors available on this machine
INFO 18:30:29,878 GenomeAnalysisEngine - Preparing for traversal
INFO 18:30:29,963 GenomeAnalysisEngine - Done preparing for traversal
INFO 18:30:29,964 ProgressMeter - [INITIALIZATION COMPLETE; STARTING PROCESSING]
INFO 18:30:29,965 ProgressMeter - | processed | time | per 1M | | total | remaining
INFO 18:30:29,966 ProgressMeter - Location | sites | elapsed | sites | completed | runtime | runtime
INFO 18:30:30,562 GenotypeGVCFs - Notice that the -ploidy parameter is ignored in GenotypeGVCFs tool as this is automatically determined by the input variant files
INFO 18:31:00,420 ProgressMeter - 1:4845033 0.0 30.0 s 50.3 w 0.0% 46.7 h 46.7 h
##### ERROR ------------------------------------------------------------------------------------------
##### ERROR stack trace
java.lang.IllegalStateException: Key DP found in VariantContext field INFO at 1:4839315 but this key isn't defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.
at htsjdk.variant.vcf.VCFEncoder.fieldIsMissingFromHeaderError(VCFEncoder.java:176)
at htsjdk.variant.vcf.VCFEncoder.encode(VCFEncoder.java:115)
at htsjdk.variant.variantcontext.writer.VCFWriter.add(VCFWriter.java:221)
at org.broadinstitute.gatk.engine.io.storage.VariantContextWriterStorage.add(VariantContextWriterStorage.java:182)
at org.broadinstitute.gatk.engine.io.stubs.VariantContextWriterStub.add(VariantContextWriterStub.java:269)
at org.broadinstitute.gatk.tools.walkers.variantutils.GenotypeGVCFs.reduce(GenotypeGVCFs.java:351)
at org.broadinstitute.gatk.tools.walkers.variantutils.GenotypeGVCFs.reduce(GenotypeGVCFs.java:119)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano$TraverseLociReduce.apply(TraverseLociNano.java:291)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano$TraverseLociReduce.apply(TraverseLociNano.java:280)
at org.broadinstitute.gatk.utils.nanoScheduler.NanoScheduler.executeSingleThreaded(NanoScheduler.java:279)
at org.broadinstitute.gatk.utils.nanoScheduler.NanoScheduler.execute(NanoScheduler.java:245)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano.traverse(TraverseLociNano.java:144)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano.traverse(TraverseLociNano.java:92)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano.traverse(TraverseLociNano.java:48)
at org.broadinstitute.gatk.engine.executive.ShardTraverser.call(ShardTraverser.java:98)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
##### ERROR ------------------------------------------------------------------------------------------
##### ERROR A GATK RUNTIME ERROR has occurred (version 3.3-0-geee94ec):
##### ERROR
##### ERROR This might be a bug. Please check the documentation guide to see if this is a known problem.
##### ERROR If not, please post the error message, with stack trace, to the GATK forum.
##### ERROR Visit our website and forum for extensive documentation and answers to
##### ERROR commonly asked questions http://www.broadinstitute.org/gatk
##### ERROR
##### ERROR MESSAGE: Key DP found in VariantContext field INFO at 1:4839315 but this key isn't defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.
##### ERROR ------------------------------------------------------------------------------------------
for f in *.g.vcf.gz; do echo -e "\n-- $f --"; zcat "$f" | sed -n -r "/^#.*DP/p;/^1\t4839315\t/{p;q;}"; done
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839317 GT:DP:GQ:MIN_DP:PL 0/0:22:0:21:0,0,432
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839317 GT:DP:GQ:MIN_DP:PL 0/0:20:0:20:0,0,410
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839315 GT:DP:GQ:MIN_DP:PL 0/0:29:0:29:0,0,773
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839315 GT:DP:GQ:MIN_DP:PL 0/0:25:2:25:0,3,790
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839316 GT:DP:GQ:MIN_DP:PL 0/0:33:0:33:0,0,837
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839315 GT:DP:GQ:MIN_DP:PL 0/0:23:31:23:0,31,765
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GA G,<NON_REF> 0 . ClippingRankSum=-0.578;MLEAC=0,0;MLEAF=0.00,0.00 GT:DP:GQ:PL:SB 0/0:21:39:0,39,488,60,491,512:20,0,0,0
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839315 GT:DP:GQ:MIN_DP:PL 0/0:18:0:18:0,0,514
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839316 GT:DP:GQ:MIN_DP:PL 0/0:29:0:29:0,0,810
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839316 GT:DP:GQ:MIN_DP:PL 0/0:33:0:33:0,0,812
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839317 GT:DP:GQ:MIN_DP:PL 0/0:28:0:25:0,0,624
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GA G,<NON_REF> 0.08 . ClippingRankSum=-0.189;MLEAC=1,0;MLEAF=0.500,0.00 GT:DP:GQ:PL:SB 0/1:17:20:20,0,311,62,320,382:14,0,3,0
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GA G,<NON_REF> 6.76 . ClippingRankSum=-0.374;MLEAC=1,0;MLEAF=0.500,0.00 GT:DP:GQ:PL:SB 0/1:25:43:43,0,401,102,417,519:20,0,3,2
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GA G,<NON_REF> 0 . ClippingRankSum=-1.095;MLEAC=0,0;MLEAF=0.00,0.00 GT:DP:GQ:PL:SB 0/0:23:1:0,1,395,56,406,460:19,0,0,0
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839317 GT:DP:GQ:MIN_DP:PL 0/0:28:0:28:0,0,626
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GA G,<NON_REF> 5.99 . ClippingRankSum=-0.584;MLEAC=1,0;MLEAF=0.500,0.00 GT:DP:GQ:PL:SB 0/1:18:42:42,0,293,84,305,388:13,1,3,1
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839317 GT:DP:GQ:MIN_DP:PL 0/0:22:0:22:0,0,558
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G GA,<NON_REF> 6.76 . ClippingRankSum=0.850;MLEAC=1,0;MLEAF=0.500,0.00 GT:DP:GQ:PL:SB 0/1:19:43:43,0,262,87,274,361:12,3,4,0
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GA G,<NON_REF> 16.82 . ClippingRankSum=-0.784;MLEAC=1,0;MLEAF=0.500,0.00 GT:DP:GQ:PL:SB 0/1:21:54:54,0,352,102,367,470:16,0,4,1
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839317 GT:DP:GQ:MIN_DP:PL 0/0:26:0:25:0,0,419
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839316 GT:DP:GQ:MIN_DP:PL 0/0:30:0:30:0,0,771
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839315 GT:DP:GQ:MIN_DP:PL 0/0:34:77:34:0,78,1136
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . G <NON_REF> . . END=4839316 GT:DP:GQ:MIN_DP:PL 0/0:26:0:20:0,0,397
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP observed within the GVCF block">
1 4839315 . GAA G,GA,<NON_REF> 22.75 . ClippingRankSum=-2.181;MLEAC=0,1,0;MLEAF=0.00,0.500,0.00 GT:DP:GQ:PL:SB 0/2:11:22:60,22,209,0,87,104,63,153,113,176:4,2,3,0
Hi all,
I have Type 2B RAD data from many individuals from several populations of my non-model species, mapped using Bowtie 0.12.8 to a reference database made by extracting all potential RAD sites from the available genome. I would like to run a first-pass UnifiedGenotyper run on a single individual, but even on a supercomputer and using the -nct and -nt flags, GATK says it will need 4.9 weeks to finish!
A collaborator suggested that GATK may just not handle many reference contigs well, but I have already reduced my reference database from the 1.6 million possible tags to the 95,000 tags that were seen at least 100x across all my individuals.
Does GATK respond to the number of contigs like this? Are there any tips you can give me to reduce the amount of time necessary to something more reasonable?
Thank you!
Roxana
Here we outline how to generate an unmapped BAM (uBAM) from either a FASTQ or aligned BAM file. We use Picard's FastqToSam to convert a FASTQ (Option A) or Picard's RevertSam to convert an aligned BAM (Option B).
(A) Convert FASTQ to uBAM and add read group information using FastqToSam
(B) Convert aligned BAM to uBAM and discard problematic records using RevertSam
Tutorial data reads were originally aligned to the advanced tutorial bundle's human_g1k_v37_decoy.fasta reference and to 10:91,000,000-92,000,000.
Our dictionary entry on read groups discusses the importance of assigning appropriate read group fields that differentiate samples and factors that contribute to batch effects, e.g. flow cell lane. Be sure your read group fields conform to the recommendations.
This post provides an example command for AddOrReplaceReadGroups.
Picard's FastqToSam transforms a FASTQ file to an unmapped BAM, requires two read group fields and makes optional specification of other read group fields. In the command below we note which fields are required for GATK Best Practices Workflows. All other read group fields are optional.
java -Xmx8G -jar picard.jar FastqToSam \
FASTQ=6484_snippet_1.fastq \ #first read file of pair
FASTQ2=6484_snippet_2.fastq \ #second read file of pair
OUTPUT=6484_snippet_fastqtosam.bam \
READ_GROUP_NAME=H0164.2 \ #required; changed from default of A
SAMPLE_NAME=NA12878 \ #required
LIBRARY_NAME=Solexa-272222 \ #required
PLATFORM_UNIT=H0164ALXX140820.2 \
PLATFORM=illumina \ #recommended
SEQUENCING_CENTER=BI \
RUN_DATE=2014-08-20T00:00:00-0400
Some details on select parameters:
For paired reads, specify each FASTQ file with FASTQ and FASTQ2 for the first read file and the second read file, respectively. Records in each file must be queryname sorted as the tool assumes identical ordering for pairs. The tool automatically strips the /1 and /2 read name suffixes and adds SAM flag values to indicate reads are paired. Do not provide a single interleaved fastq file, as the tool will assume reads are unpaired and the SAM flag values will reflect single ended reads.
For single ended reads, specify the input file with FASTQ.
QUALITY_FORMAT is detected automatically if unspecified.SORT_ORDER by default is queryname.PLATFORM_UNIT is often in run_barcode.lane format. Include if sample is multiplexed.RUN_DATE is in Iso8601 date format.Paired reads will have SAM flag values that reflect pairing and the fact that the reads are unmapped as shown in the example read pair below.
Original first read
@H0164ALXX140820:2:1101:10003:49022/1
ACTTTAGAAATTTACTTTTAAGGACTTTTGGTTATGCTGCAGATAAGAAATATTCTTTTTTTCTCCTATGTCAGTATCCCCCATTGAAATGACAATAACCTAATTATAAATAAGAATTAGGCTTTTTTTTGAACAGTTACTAGCCTATAGA
+
-FFFFFJJJJFFAFFJFJJFJJJFJFJFJJJ<<FJJJJFJFJFJJJJ<JAJFJJFJJJJJFJJJAJJJJJJFFJFJFJJFJJFFJJJFJJJFJJFJJFJAJJJJAJFJJJJJFFJJ<<<JFJJAFJAAJJJFFFFFJJJAJJJF<AJFFFJ
Original second read
@H0164ALXX140820:2:1101:10003:49022/2
TGAGGATCACTAGATGGGGGAGGGAGAGAAGAGATGTGGGCTGAAGAACCATCTGTTGGGTAATATGTTTACTGTCAGTGTGATGGAATAGCTGGGACCCCAAGCGTCAGTGTTACACAACTTACATCTGTTGATCGACTGTCTATGACAG
+
AA<FFJJJAJFJFAFJJJJFAJJJJJ7FFJJ<F-FJFJJJFJJFJJFJJF<FJJA<JF-AFJFAJFJJJJJAAAFJJJJJFJJF-FF<7FJJJJJJ-JA<<J<F7-<FJFJJ7AJAF-AFFFJA--J-F######################
After FastqToSam
H0164ALXX140820:2:1101:10003:49022 77 * 0 0 * * 0 0 ACTTTAGAAATTTACTTTTAAGGACTTTTGGTTATGCTGCAGATAAGAAATATTCTTTTTTTCTCCTATGTCAGTATCCCCCATTGAAATGACAATAACCTAATTATAAATAAGAATTAGGCTTTTTTTTGAACAGTTACTAGCCTATAGA -FFFFFJJJJFFAFFJFJJFJJJFJFJFJJJ<<FJJJJFJFJFJJJJ<JAJFJJFJJJJJFJJJAJJJJJJFFJFJFJJFJJFFJJJFJJJFJJFJJFJAJJJJAJFJJJJJFFJJ<<<JFJJAFJAAJJJFFFFFJJJAJJJF<AJFFFJ RG:Z:H0164.2
H0164ALXX140820:2:1101:10003:49022 141 * 0 0 * * 0 0 TGAGGATCACTAGATGGGGGAGGGAGAGAAGAGATGTGGGCTGAAGAACCATCTGTTGGGTAATATGTTTACTGTCAGTGTGATGGAATAGCTGGGACCCCAAGCGTCAGTGTTACACAACTTACATCTGTTGATCGACTGTCTATGACAG AA<FFJJJAJFJFAFJJJJFAJJJJJ7FFJJ<F-FJFJJJFJJFJJFJJF<FJJA<JF-AFJFAJFJJJJJAAAFJJJJJFJJF-FF<7FJJJJJJ-JA<<J<F7-<FJFJJ7AJAF-AFFFJA--J-F###################### RG:Z:H0164.2
We use Picard's RevertSam to remove alignment information and generate an unmapped BAM (uBAM). For our tutorial file we have to call on some additional parameters that we explain below. This illustrates the need to cater the tool's parameters to each dataset. As such, it is a good idea to test the reversion process on a subset of reads before committing to reverting the entirety of a large BAM. Follow the directions in this How to to create a snippet of aligned reads corresponding to a genomic interval.
We use the following parameters.
java -Xmx8G -jar /path/picard.jar RevertSam \
I=6484_snippet.bam \
O=6484_snippet_revertsam.bam \
SANITIZE=true \
MAX_DISCARD_FRACTION=0.005 \ #informational; does not affect processing
ATTRIBUTE_TO_CLEAR=XT \
ATTRIBUTE_TO_CLEAR=XN \
ATTRIBUTE_TO_CLEAR=AS \ #Picard release of 9/2015 clears AS by default
ATTRIBUTE_TO_CLEAR=OC \
ATTRIBUTE_TO_CLEAR=OP \
SORT_ORDER=queryname \ #default
RESTORE_ORIGINAL_QUALITIES=true \ #default
REMOVE_DUPLICATE_INFORMATION=true \ #default
REMOVE_ALIGNMENT_INFORMATION=true #default
To process large files, also designate a temporary directory.
TMP_DIR=/path/shlee #sets environmental variable for temporary directory
We invoke or change multiple RevertSam parameters to generate an unmapped BAM
We remove nonstandard alignment tags with the ATTRIBUTE_TO_CLEAR option. Standard tags cleared by default are NM, UQ, PG, MD, MQ, SA, MC, and AS tags (AS for Picard releases starting 9/2015). Additionally, the OQ tag is removed by the default RESTORE_ORIGINAL_QUALITIES parameter. Remove all other nonstandard tags by specifying each with the ATTRIBUTE_TO_CLEAR option. For example, we clear the XT tag using this option for our tutorial file so that it is free for use by other tools, e.g. MarkIlluminaAdapters. To list all tags within a BAM, use the command below.
samtools view input.bam | cut -f 12- | tr '\t' '\n' | cut -d ':' -f 1 | awk '{ if(!x[$1]++) { print }}'
For the tutorial file, this gives RG, OC, XN, OP and XT tags as well as those removed by default. We remove all of these except the RG tag. See your aligner's documentation and the Sequence Alignment/Map Format Specification for descriptions of tags.
Additionally, we invoke the SANITIZE option to remove reads that cause problems for certain tools, e.g. MarkIlluminaAdapters. Downstream tools will have problems with paired reads with missing mates, duplicated records, and records with mismatches in length of bases and qualities. Any paired reads file subset for a genomic interval requires sanitizing to remove reads with lost mates that align outside of the interval.
In this command, we've set MAX_DISCARD_FRACTION to a more strict threshold of 0.005 instead of the default 0.01. Whether or not this fraction is reached, the tool informs you of the number and fraction of reads it discards. This parameter asks the tool to additionally inform you of the discarded fraction via an exception as it finishes processing.
Exception in thread "main" picard.PicardException: Discarded 0.787% which is above MAX_DISCARD_FRACTION of 0.500%
Some comments on options kept at default:
SORT_ORDER=queryname
For paired read files, because each read in a pair has the same query name, sorting results in interleaved reads. This means that reads in a pair are listed consecutively within the same file. We make sure to alter the previous sort order. Coordinate sorted reads result in the aligner incorrectly estimating insert size from blocks of paired reads as they are not randomly distributed.
RESTORE_ORIGINAL_QUALITIES=true
Restoring original base qualities to the QUAL field requires OQ tags listing original qualities. The OQ tag uses the same encoding as the QUAL field, e.g. ASCII Phred-scaled base quality+33 for tutorial data. After restoring the QUAL field, RevertSam removes the tag.
REMOVE_ALIGNMENT_INFORMATION=true will remove program group records and alignment flag and tag information. For example, flags reset to unmapped values, e.g. 77 and 141 for paired reads. The parameter also invokes the default ATTRIBUTE_TO_CLEAR parameter which removes standard alignment tags. RevertSam ignores ATTRIBUTE_TO_CLEAR when REMOVE_ALIGNMENT_INFORMATION=false.
Below we show below a read pair before and after RevertSam from the tutorial data. Notice the first listed read in the pair becomes reverse-complemented after RevertSam. This restores how reads are represented when they come off the sequencer--5' to 3' of the read being sequenced.
For 6484_snippet.bam, SANITIZE removes 2,202 out of 279,796 (0.787%) reads, leaving us with 277,594 reads.
Original BAM
H0164ALXX140820:2:1101:10003:23460 83 10 91515318 60 151M = 91515130 -339 CCCATCCCCTTCCCCTTCCCTTTCCCTTTCCCTTTTCTTTCCTCTTTTAAAGAGACAAGGTCTTGTTCTGTCACCCAGGCTGGAATGCAGTGGTGCAGTCATGGCTCACTGCCGCCTCAGACTTCAGGGCAAAAGCAATCTTTCCAGCTCA :<<=>@AAB@AA@AA>6@@A:>,*@A@<@??@8?9>@==8?:?@?;?:><??@>==9?>8>@:?>>=>;<==>>;>?=?>>=<==>>=>9<=>??>?>;8>?><?<=:>>>;4>=>7=6>=>>=><;=;>===?=>=>>?9>>>>??==== MC:Z:60M91S MD:Z:151 PG:Z:MarkDuplicates RG:Z:H0164.2 NM:i:0 MQ:i:0 OQ:Z:<FJFFJJJJFJJJJJF7JJJ<F--JJJFJJJJ<J<FJFF<JAJJJAJAJFFJJJFJAFJAJJAJJJJJFJJJJJFJJFJJJJFJFJJJJFFJJJJJJJFAJJJFJFJFJJJFFJJJ<J7JJJJFJ<AFAJJJJJFJJJJJAJFJJAFFFFA UQ:i:0 AS:i:151
H0164ALXX140820:2:1101:10003:23460 163 10 91515130 0 60M91S = 91515318 339 TCTTTCCTTCCTTCCTTCCTTGCTCCCTCCCTCCCTCCTTTCCTTCCCCCCCCCCCCCCCCCTCCCCCCCCCCCCCCCCCTCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCTTCCCCTCTCCCACCCCTCTCTCCCCCCCTCCCACCC :0;.=;8?7==?794<<;:>769=,<;0:=<0=:9===/,:-==29>;,5,98=599;<=########################################################################################### SA:Z:2,33141573,-,37S69M45S,0,1; MC:Z:151M MD:Z:48T4T6 PG:Z:MarkDuplicates RG:Z:H0164.2 NM:i:2 MQ:i:60 OQ:Z:<-<-FA<F<FJF<A7AFAAJ<<AA-FF-AJF-FA<AFF--A-FA7AJA-7-A<F7<<AFF########################################################################################### UQ:i:49 AS:i:50
After RevertSam
H0164ALXX140820:2:1101:10003:23460 77 * 0 0 * * 0 0 TGAGCTGGAAAGATTGCTTTTGCCCTGAAGTCTGAGGCGGCAGTGAGCCATGACTGCACCACTGCATTCCAGCCTGGGTGACAGAACAAGACCTTGTCTCTTTAAAAGAGGAAAGAAAAGGGAAAGGGAAAGGGAAGGGGAAGGGGATGGG AFFFFAJJFJAJJJJJFJJJJJAFA<JFJJJJ7J<JJJFFJJJFJFJFJJJAFJJJJJJJFFJJJJFJFJJJJFJJFJJJJJFJJJJJAJJAJFAJFJJJFFJAJAJJJAJ<FFJF<J<JJJJFJJJ--F<JJJ7FJJJJJFJJJJFFJF< RG:Z:H0164.2
H0164ALXX140820:2:1101:10003:23460 141 * 0 0 * * 0 0 TCTTTCCTTCCTTCCTTCCTTGCTCCCTCCCTCCCTCCTTTCCTTCCCCCCCCCCCCCCCCCTCCCCCCCCCCCCCCCCCTCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCTTCCCCTCTCCCACCCCTCTCTCCCCCCCTCCCACCC <-<-FA<F<FJF<A7AFAAJ<<AA-FF-AJF-FA<AFF--A-FA7AJA-7-A<F7<<AFF########################################################################################### RG:Z:H0164.2
Hi,
Is the bwa of the bwaspark tool is the latest 0.7.15 version or not?
Thanks!
Hi,
I observed this issue while analyzing a known dataset. Gene names in two files that MutSig uses (gene.covariates.txt and exome_full192.coverage.txt) are not according to HGNC and some of the gene names are still according to old nomenclature. But most of the variant annotation programs such as oncotator or VEP uses HGNC symbols for gene annotation. This discrepancy causes MutSig to not recognize well known oncogenes in the MAF file and ignores them.
For example, KMT2D in Esopghageal Squmaous Carcinoma is frequently mutated, but MutSig covariates file doesn;t have this gene. Instead they have MLL2/MLL4 which are synonyms for KMT2D. This causes mutsig to ignore KMT2D from analysis. I tried to convert gene names in these two files into HGNC symbols, but MutSig doesn't recognize these altered files and the result contains NaN values for expr, reptime and hic columns.
Is there anyway to fix this ?
I think this should be addressed, since this is one of the most widely used program and someone doing a denovo analysis might miss important genes.
P.S; I have also posted this on CGA and CGA forum doesn't seem to be active.
In the following final VCF (produced by following the gVCF workflow) I get quite a number of positions reported as missing (1.2M out of ~24M bases). This of course isn't unexpected, however upon closer inspection I cannot see a reason why HC would call some of these positions as missing genotypes. Take for instance this 11bp extract from a gVCF.
Supercontig_1.1 613355 . A <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:78,0:78:18:0,18,270
Supercontig_1.1 613356 . G <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:77,0:77:9:0,9,135
Supercontig_1.1 613357 . G <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:78,0:78:0:0,0,0
Supercontig_1.1 613358 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:79,0:79:0:0,0,0
Supercontig_1.1 613359 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:79,0:79:0:0,0,0
Supercontig_1.1 613360 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:78,0:78:0:0,0,0
Supercontig_1.1 613361 . G <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:77,0:77:0:0,0,0
Supercontig_1.1 613362 . C CT,<NON_REF> 3242.73 . DP=82;ExcessHet=3.0103;MLEAC=2,0;MLEAF=1,0;RAW_MQ=295200 GT:AD:DP:GQ:PGT:PID:PL:SB 1/1:0,74,0:74:99:0|1:613362_C_CT:3280,223,0,3280,223,3280:0,0,44,30
Supercontig_1.1 613363 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:4,72:76:0:0,0,0
Supercontig_1.1 613364 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:76,0:76:99:0,120,1800
Supercontig_1.1 613365 . T <NON_REF> . . . GT:AD:DP:GQ:PL 0/0:76,0:76:99:0,120,1800Here is the subsequent 11bp stretch after running the GenotypeGVCFs on the gVCF:
Supercontig_1.1 613355 . A . . PASS AN=2;DP=78;VariantType=NO_VARIATION GT:AD:DP:RGQ 0/0:78:78:18
Supercontig_1.1 613356 . G . . PASS AN=2;DP=77;VariantType=NO_VARIATION GT:AD:DP:RGQ 0/0:77:77:9
Supercontig_1.1 613357 . G . . PASS DP=78;VariantType=NO_VARIATION GT:AD:DP:RGQ ./.:78:78:0
Supercontig_1.1 613358 . T . . PASS DP=79;VariantType=NO_VARIATION GT:AD:DP:RGQ ./.:79:79:0
Supercontig_1.1 613359 . T . . PASS DP=79;VariantType=NO_VARIATION GT:AD:DP:RGQ ./.:79:79:0
Supercontig_1.1 613360 . T . . PASS DP=78;VariantType=NO_VARIATION GT:AD:DP:RGQ ./.:78:78:0
Supercontig_1.1 613361 . G . . PASS DP=77;VariantType=NO_VARIATION GT:AD:DP:RGQ ./.:77:77:0
Supercontig_1.1 613362 . C CT 3242.73 PASS AC=2;AF=1;AN=2;DP=82;ExcessHet=3.0103;FS=0;MLEAC=2;MLEAF=1;MQ=60;QD=30.85;SOR=1.134;VariantType=INSERTION.NumRepetitions_3.EventLength_1.RepeatExpansion_T GT:AD:DP:GQ:PGT:PID:PL 1/1:0,74:74:99:1|1:613362_C_CT:3280,223,0
Supercontig_1.1 613363 . T . . PASS DP=76;VariantType=NO_VARIATION GT:AD:DP:RGQ ./.:4:76:0
Supercontig_1.1 613364 . T . . PASS AN=2;DP=76;VariantType=NO_VARIATION GT:AD:DP:RGQ 0/0:76:76:99
Supercontig_1.1 613365 . T . . PASS AN=2;DP=76;VariantType=NO_VARIATION GT:AD:DP:RGQ 0/0:76:76:99Positions 613,357-61 are all assigned a missing genotype (which I assume is because the genotype likelihoods for these positions are all equally likely according to HC). However, examining the raw bam output, I can see that ALL the reads covering these positions are 100% hom-ref, and this is also the case when examining the BAMOUT from HC. Could anyone explain why I get these no-calls which appear to me to be erroneous? All the mapping qualities are very high as are the base qualities.
The input script is like this:
java -jar /nfs/home/gatk/GenomeAnalysisTK.jar
-T GenotypeGVCFs
-R /nfs/home/tool/gatk/bundle/2.8/hg19/ucsc.hg19.fasta
--variant /home/sample/sample4/AD_1.g.vcf
--variant /home/sample/sample4/AD_2.g.vcf
--variant /home/sample/sample4/AD_3.g.vcf
--variant /home/sample/sample4/AD_4.g.vcf
-o /home/sample/sample4/AD_raw_variants.vcf
It was running smoothly, but suddenly it stopped at chr19. The screen is like this:
INFO 17:47:12,334 ProgressMeter - chr19:11563684 5.32049E7 7.6 h 8.6 m 85.1% 9.0 h 80.0 m
INFO 17:47:42,335 ProgressMeter - chr19:12766650 5.3285579E7 7.6 h 8.6 m 85.2% 9.0 h 79.8 m
INFO 17:47:47,835 GATKRunReport - Uploaded run statistics report to AWS S3
java.lang.NumberFormatException: For input string: "495"
at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
at java.lang.Integer.parseInt(Integer.java:481)
at java.lang.Integer.parseInt(Integer.java:527)
at org.broadinstitute.gatk.tools.walkers.variantutils.ReferenceConfidenceVariantContextMerger.merge(ReferenceConfidenceVariantContextMerger.java:161)
at org.broadinstitute.gatk.tools.walkers.variantutils.GenotypeGVCFs.map(GenotypeGVCFs.java:257)
at org.broadinstitute.gatk.tools.walkers.variantutils.GenotypeGVCFs.map(GenotypeGVCFs.java:129)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano$TraverseLociMap.apply(TraverseLociNano.java:267)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano$TraverseLociMap.apply(TraverseLociNano.java:255)
at org.broadinstitute.gatk.utils.nanoScheduler.NanoScheduler.executeSingleThreaded(NanoScheduler.java:274)
at org.broadinstitute.gatk.utils.nanoScheduler.NanoScheduler.execute(NanoScheduler.java:245)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano.traverse(TraverseLociNano.java:144)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano.traverse(TraverseLociNano.java:92)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano.traverse(TraverseLociNano.java:48)
at org.broadinstitute.gatk.engine.executive.LinearMicroScheduler.execute(LinearMicroScheduler.java:99)
at org.broadinstitute.gatk.engine.GenomeAnalysisEngine.execute(GenomeAnalysisEngine.java:315)
at org.broadinstitute.gatk.engine.CommandLineExecutable.execute(CommandLineExecutable.java:121)
at org.broadinstitute.gatk.utils.commandline.CommandLineProgram.start(CommandLineProgram.java:248)
at org.broadinstitute.gatk.utils.commandline.CommandLineProgram.start(CommandLineProgram.java:155)
at org.broadinstitute.gatk.engine.CommandLineGATK.main(CommandLineGATK.java:106)
jiangli@darwin:~$ ^C
The .g.vcf files are created by CombineGVCFs. So, is there anything wrong in these gVCF files?
Best Wishes,
River Lee
Hi,
Here is my log file, what does " java.lang.ClassNotFoundException: org.xerial.snappy.LoadSnappy" mean? Thanks
Using GATK jar /home/kh3/gatk-4.alpha.2-36-g13333ba-SNAPSHOT/gatk-package-4.alpha.2-36-g13333ba-SNAPSHOT-local.jar
Running:
java -jar /home/kh3/gatk-4.alpha.2-36-g13333ba-SNAPSHOT/gatk-package-4.alpha.2-36-g13333ba-SNAPSHOT-local.jar FastqToSam -SM test -F1 /home/kh3/data/Illumina/GATK4/Platinum/Coriell_12891_GCC
AAT_L001_R1_001.fastq.gz -F2 /home/kh3/data/Illumina/GATK4/Platinum/Coriell_12891_GCCAAT_L001_R2_001.fastq.gz -O /home/kh3/data/Illumina/GATK4/Platinum/TEST/test.spark.bam -SO coordinate -R /hom
e/kh3/Resources/genome_b37/genome.2bit --STRIP_UNPAIRED_MATE_NUMBER true --VALIDATION_STRINGENCY LENIENT -PL ILLUMINA --MAX_RECORDS_IN_RAM 6624267
19:28:00.322 INFO IntelGKLUtils - Trying to load Intel GKL library from:
jar:file:/home/kh3/gatk-4.alpha.2-36-g13333ba-SNAPSHOT/gatk-package-4.alpha.2-36-g13333ba-SNAPSHOT-local.jar!/com/intel/gkl/native/libIntelGKL.so
19:28:00.371 INFO IntelGKLUtils - Intel GKL library loaded from classpath.
[September 15, 2016 7:28:00 PM EDT] org.broadinstitute.hellbender.tools.picard.sam.FastqToSam --FASTQ /home/kh3/data/Illumina/GATK4/Platinum/Coriell_12891_GCCAAT_L001_R1_001.fastq.gz --FASTQ2 /home/
kh3/data/Illumina/GATK4/Platinum/Coriell_12891_GCCAAT_L001_R2_001.fastq.gz --output /home/kh3/data/Illumina/GATK4/Platinum/TEST/test.spark.bam --SAMPLE_NAME test --PLATFORM ILLUMINA --SORT_ORDER
coordinate --STRIP_UNPAIRED_MATE_NUMBER true --VALIDATION_STRINGENCY LENIENT --MAX_RECORDS_IN_RAM 6624267 --reference /home/kh3/Resources/genome_b37/genome.2bit --READ_GROUP_NAME A --MIN_Q 0 --MAX_
Q 93 --ALLOW_AND_IGNORE_EMPTY_LINES false --COMPRESSION_LEVEL 5 --CREATE_INDEX false --CREATE_MD5_FILE false --help false --version false --verbosity INFO --QUIET false --use_jdk_deflater false
[September 15, 2016 7:28:00 PM EDT] Executing as kh3@rgcaahauva08091.rgc.aws.regeneron.com on Linux 3.13.0-91-generic amd64; Java HotSpot(TM) 64-Bit Server VM 1.8.0_101-b13; Version: Version:4.alpha.
2-36-g13333ba-SNAPSHOT
19:28:00.397 INFO FastqToSam - Defaults.BUFFER_SIZE : 131072
19:28:00.398 INFO FastqToSam - Defaults.COMPRESSION_LEVEL : 5
19:28:00.398 INFO FastqToSam - Defaults.CREATE_INDEX : false
19:28:00.398 INFO FastqToSam - Defaults.CREATE_MD5 : false
19:28:00.398 INFO FastqToSam - Defaults.CUSTOM_READER_FACTORY :
19:28:00.398 INFO FastqToSam - Defaults.EBI_REFERENCE_SERVICE_URL_MASK : http://www.ebi.ac.uk/ena/cram/md5/%s
19:28:00.398 INFO FastqToSam - Defaults.NON_ZERO_BUFFER_SIZE : 131072
19:28:00.398 INFO FastqToSam - Defaults.REFERENCE_FASTA : null
19:28:00.398 INFO FastqToSam - Defaults.SAM_FLAG_FIELD_FORMAT : DECIMAL
19:28:00.398 INFO FastqToSam - Defaults.USE_ASYNC_IO_READ_FOR_SAMTOOLS : false
19:28:00.398 INFO FastqToSam - Defaults.USE_ASYNC_IO_WRITE_FOR_SAMTOOLS : false
19:28:00.398 INFO FastqToSam - Defaults.USE_ASYNC_IO_WRITE_FOR_TRIBBLE : false
19:28:00.398 INFO FastqToSam - Defaults.USE_CRAM_REF_DOWNLOAD : false
19:28:00.398 INFO FastqToSam - Deflater IntelDeflater
19:28:00.398 INFO FastqToSam - Initializing engine
19:28:00.398 INFO FastqToSam - Done initializing engine
19:28:00.489 INFO FastqToSam - Auto-detected quality format as: Standard.
19:28:03.741 INFO FastqToSam - Processed 1,000,000 records. Elapsed time: 00:00:03s. Time for last 1,000,000: 3s. Last read position: /
19:28:08.820 INFO FastqToSam - Processed 2,000,000 records. Elapsed time: 00:00:08s. Time for last 1,000,000: 5s. Last read position: /
19:28:12.281 INFO FastqToSam - Processed 3,000,000 records. Elapsed time: 00:00:11s. Time for last 1,000,000: 3s. Last read position: /
19:28:17.976 INFO FastqToSam - Processed 4,000,000 records. Elapsed time: 00:00:17s. Time for last 1,000,000: 5s. Last read position: /
19:28:23.149 INFO FastqToSam - Processed 5,000,000 records. Elapsed time: 00:00:22s. Time for last 1,000,000: 5s. Last read position: /
19:28:25.998 INFO FastqToSam - Processed 6,000,000 records. Elapsed time: 00:00:25s. Time for last 1,000,000: 2s. Last read position: /
19:29:16.029 INFO FastqToSam - Shutting down engine
[September 15, 2016 7:29:16 PM EDT] org.broadinstitute.hellbender.tools.picard.sam.FastqToSam done. Elapsed time: 1.26 minutes.
Runtime.totalMemory()=4369416192
Exception in thread "main" java.lang.NoClassDefFoundError: org/xerial/snappy/LoadSnappy
at htsjdk.samtools.util.SnappyLoader.(SnappyLoader.java:86)
at htsjdk.samtools.util.SnappyLoader.(SnappyLoader.java:52)
at htsjdk.samtools.util.TempStreamFactory.getSnappyLoader(TempStreamFactory.java:42)
at htsjdk.samtools.util.TempStreamFactory.wrapTempOutputStream(TempStreamFactory.java:74)
at htsjdk.samtools.util.SortingCollection.spillToDisk(SortingCollection.java:223)
at htsjdk.samtools.util.SortingCollection.add(SortingCollection.java:166)
at htsjdk.samtools.SAMFileWriterImpl.addAlignment(SAMFileWriterImpl.java:192)
at org.broadinstitute.hellbender.tools.picard.sam.FastqToSam.doPaired(FastqToSam.java:222)
at org.broadinstitute.hellbender.tools.picard.sam.FastqToSam.makeItSo(FastqToSam.java:181)
at org.broadinstitute.hellbender.tools.picard.sam.FastqToSam.doWork(FastqToSam.java:156)
at org.broadinstitute.hellbender.cmdline.CommandLineProgram.runTool(CommandLineProgram.java:109)
at org.broadinstitute.hellbender.cmdline.CommandLineProgram.instanceMainPostParseArgs(CommandLineProgram.java:167)
at org.broadinstitute.hellbender.cmdline.PicardCommandLineProgram.instanceMain(PicardCommandLineProgram.java:61)
at org.broadinstitute.hellbender.Main.instanceMain(Main.java:76)
at org.broadinstitute.hellbender.Main.main(Main.java:92)
Caused by: java.lang.ClassNotFoundException: org.xerial.snappy.LoadSnappy
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 15 more
I'm starting to get a weird warning message which appears four times at the end of any GATK command run as a qsub job on a Linux SGE.
INFO 15:57:27,936 HttpMethodDirector - I/O exception (javax.net.ssl.SSLException) caught when processing request: java.lang.RuntimeException: Unexpected error: java.security.InvalidAlgorithmParameterException: the trustAnchors parameter must be non-empty
Sysadmin here thinks it could've been broken by a java update. I'm using GATK 3.4 so I know, I know, I should get it updated.
The bam was created using FastqToSam, so it is unaligned. I think that's the reason why the read contig list was empty. Is there any solution for it?Thanks!
Running:
/home/kh3/Softwares/gatk/build/install/gatk/bin/gatk BwaSpark -I /home/kh3/data/Illumina/GATK4/Platinum/TEST/test.spark.sam -R /home/kh3/Resources/genome_b37/genome.fa -t 16 -O /home/kh3/data/Illumina/GATK4/Platinum/TEST/test.spark.bam
23:30:44.246 INFO IntelGKLUtils - Trying to load Intel GKL library from:
jar:file:/home/kh3/Softwares/gatk/build/install/gatk/lib/gkl-0.1.2.jar!/com/intel/gkl/native/libIntelGKL.so
23:30:44.295 INFO IntelGKLUtils - Intel GKL library loaded from classpath.
[September 15, 2016 11:30:44 PM EDT] org.broadinstitute.hellbender.tools.spark.bwa.BwaSpark --output /home/kh3/data/Illumina/GATK4/Platinum/TEST/test.spark.bam --threads 16 --reference /home/kh3/Resources/genome_b37/genome.fa --input /home/kh3/data/Illumina/GATK4/Platinum/TEST/test.spark.sam --fixedChunkSize 100000 --readValidationStringency SILENT --interval_set_rule UNION --interval_padding 0 --interval_exclusion_padding 0 --bamPartitionSize 0 --disableSequenceDictionaryValidation false --shardedOutput false --numReducers 0 --sparkMaster local[*] --help false --version false --verbosity INFO --QUIET false --use_jdk_deflater false --disableAllReadFilters false
[September 15, 2016 11:30:44 PM EDT] Executing as kh3@rgcaahauva08091.rgc.aws.regeneron.com on Linux 3.13.0-91-generic amd64; Java HotSpot(TM) 64-Bit Server VM 1.8.0_101-b13; Version: Version:4.alpha.2-45-ga30af5a-SNAPSHOT
23:30:44.320 INFO BwaSpark - Defaults.BUFFER_SIZE : 131072
23:30:44.320 INFO BwaSpark - Defaults.COMPRESSION_LEVEL : 1
23:30:44.320 INFO BwaSpark - Defaults.CREATE_INDEX : false
23:30:44.320 INFO BwaSpark - Defaults.CREATE_MD5 : false
23:30:44.321 INFO BwaSpark - Defaults.CUSTOM_READER_FACTORY :
23:30:44.321 INFO BwaSpark - Defaults.EBI_REFERENCE_SERVICE_URL_MASK : http://www.ebi.ac.uk/ena/cram/md5/%s
23:30:44.321 INFO BwaSpark - Defaults.NON_ZERO_BUFFER_SIZE : 131072
23:30:44.321 INFO BwaSpark - Defaults.REFERENCE_FASTA : null
23:30:44.321 INFO BwaSpark - Defaults.SAM_FLAG_FIELD_FORMAT : DECIMAL
23:30:44.321 INFO BwaSpark - Defaults.USE_ASYNC_IO_READ_FOR_SAMTOOLS : false
23:30:44.321 INFO BwaSpark - Defaults.USE_ASYNC_IO_WRITE_FOR_SAMTOOLS : true
23:30:44.321 INFO BwaSpark - Defaults.USE_ASYNC_IO_WRITE_FOR_TRIBBLE : false
23:30:44.321 INFO BwaSpark - Defaults.USE_CRAM_REF_DOWNLOAD : false
23:30:44.321 INFO BwaSpark - Deflater IntelDeflater
23:30:44.321 INFO BwaSpark - Initializing engine
23:30:44.321 INFO BwaSpark - Done initializing engine
23:30:44.756 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
23:30:46.883 INFO BwaSpark - Shutting down engine
[September 15, 2016 11:30:46 PM EDT] org.broadinstitute.hellbender.tools.spark.bwa.BwaSpark done. Elapsed time: 0.04 minutes.
Runtime.totalMemory()=498597888
A USER ERROR has occurred: Input files reference and reads have incompatible contigs: No overlapping contigs found.
reference contigs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, Y, MT, GL000207.1, GL000226.1, GL000229.1, GL000231.1, GL000210.1, GL000239.1, GL000235.1, GL000201.1, GL000247.1, GL000245.1, GL000197.1, GL000203.1, GL000246.1, GL000249.1, GL000196.1, GL000248.1, GL000244.1, GL000238.1, GL000202.1, GL000234.1, GL000232.1, GL000206.1, GL000240.1, GL000236.1, GL000241.1, GL000243.1, GL000242.1, GL000230.1, GL000237.1, GL000233.1, GL000204.1, GL000198.1, GL000208.1, GL000191.1, GL000227.1, GL000228.1, GL000214.1, GL000221.1, GL000209.1, GL000218.1, GL000220.1, GL000213.1, GL000211.1, GL000199.1, GL000217.1, GL000216.1, GL000215.1, GL000205.1, GL000219.1, GL000224.1, GL000223.1, GL000195.1, GL000212.1, GL000222.1, GL000200.1, GL000193.1, GL000194.1, GL000225.1, GL000192.1, NC_007605, hs37d5]
reads contigs = []
Here is the content of my input bam:
@HD VN:1.5 SO:coordinate
@RG ID:A SM:test PL:ILLUMINA
@CDDDDFHHHGHJHGHHIJJJGEGHGGFHGJIIIGIGHGGGGIGHAHGIGIBFCA(=FGGJIE;CG;AHFHFECD RG:Z:A
@FFFFFHFHFHJIIIIIJJJAEHIHIDF?BGCHEGCG*?DHGDB=DHHE@@GHGHG@EHJE>AE?B,776>A88 RG:Z:A
@FFFFFDHHGHJJJIIGGIIJGJIIGIJIBGGBDEBBFGHGGDEIDBF0CGBHIGC7=CEE=AEE@5?>(>C;; RG:Z:A
I am wondering if there is a public repository from which one can build a panel of normals?
A couple of questions about that:
1. Must all the sample for a PON come from the same platform?
2. if not, is there a publicly available PON?
Many thanks