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Calling variants in RNAseq
Hi @npriedig, as shown in the filtering summary the main reasons you are losing reads are MAPQ and duplicates, so it's a quality control problem. You mention a high mapping rate, but you should check what portion of your mapped reads are actually uniquely mapped, since those are the only reads that will be usable for variant discovery. Regarding duplicates, it's either a problem of true duplication, or the prep method you use generates the appearance of duplication (as in amplicon-based sequencing). If the latter, you can disable the DuplicateRead filer (using -drf) but you should evaluate results carefully if you do.
Hi @npriedig, sorry for the very late response. I thought I had answered this and closed the issue ticket prematurely.
In a nutshell, you have two "problems" leading to filtering out of many reads: one is the high rate of duplication (41.90% of total as indicated in the filtering summary) and the other is the also high rate of reads with a MAPQ below 20 (35.78% of total) which HaplotypeCaller disregards as useless.
Regarding the duplication, one thing to keep in mind is that for RNAseq the calculation of duplication rates is confounded by the possibility that some apparent duplicates are actually the result of higher levels of transcription. We still prefer to filter out duplicates, but some people choose to disable the duplicate filter (using
-drf DuplicateRead).
I would say the other problem, of low MAPQs, is more a concern. It indicates that even though it looks like your data is mapped, there are lots of reads that are not well mapped. If you followed the pre-processing procedure that we recommend, you will have converted all MAPQs of 255 (which are the unique mappers that we can use for variant discovery) to a flat 60. Those reads will automatically pass HC's filter. So I would advise looking at the distribution of MAPQs in your files (both the original file mapped by STAR and the one obtained after pre-processing to check if the problem was there from the start or whether it arose on the way.
It's also worth checking what is the actual depth (DP) at most of your variant sites. It's possible that you don't really have a problem, if you still have sufficient coverage despite all the filtering to make confident calls.
kchang3 Member ✭
I have followed the GATK best practice guideline for preparing the BAM file for mutation calling, and noticed that majority of the mutation fall in 3'UTR region, which isn't something i notice in DNAseq. Is this a common in mutation calling in RNAseq data?
Top mutation types in my data
Missense_Mutation 16.36%
Intron 12.62%
Silent 7.86%
uki156 Member ✭
Hi all, I'm new here, sorry if this has been asked before, but from what I can see it hasn't been so far.
I tried running this workflow and I get an error at the GATK Indel Realigner tool. Basically what the error says is:
Error caching SAM record xxxx, which is usually caused by malformed SAM/BAM files in which multiple identical copies of a read are present.
Now, I could skip this step as Indel Realigner is optional, but I'm guessing that further GATK tools in the workflow will give the same error, as this seems to be a problem with my aligned BAM file coming out of the STAR aligner.
Basically, from what I could gather so far, my BAM file has multiple identical copies of a read, which I am not really sure what the problem is about. I thought the error could be from the Mapping Qualities which aren't proper, so I tried changing the parameter --outSAMmapqUnique in STAR from 255 to 60 as advised for these type of tasks, but I still get the same error.
Also this only happens when I try this analysis with paired-end reads, single-end reads come through ok.
Any ideas?
What kind of data are you working with and how was it sequenced? Did you follow the Best Practices pre-processing steps?
npriedig Member ✭
Hello @Geraldine_VdAuwera, thank you for getting back regarding my issue! What you wrote makes perfect sense and is very clear. I will look very carefully at the MAPQ values to see if I can improve the results.
@kchang3 Sorry for the late reply. This is not something I'm aware of, but we don't deal with RNAseq much so I really can't speak with any authority on the matter. My only thought is that this does smell a bit like an artifact, so maybe consider checking whether the distribution of annotation values and scores of variants that are falling in 3'UTR regions is in line with the rest or not. If there's something wrong with those variants, that might come up as a distinct profile on one or more dimensions.
What is the visualization software used in this tutorial? The one showing the read alignment?
I am getting some error while running variant filtration.
WARN 22:37:17,483 Interpreter - ![0,2]: 'QD < 2.0;' undefined variable QD
I have been trying to find the source of the error but no luck. Never had this kind of issues before with other vcf files when running a standard (best practices). Also, the QD field is indeed present in the raw.vcf file.
Any suggestions is appreciated.
Have a look at my response in this thread.
Thanks @Sheila !
It really helped clear my concerns.
Hi, I'm new here. We are planning to make SNP calling in RNA seq data. We don't have a reference genome, we want to compare SNP between different species with different ploidy levels.
We need to build a transcriptome reference or can we work with a related species?
Any suggestions is really appreciated.
Thank you very much.
Hi @Carolina_O,
Our tools rely on reference-aligned sequences and calling variants against the reference. So depending on what type of variants you are interested in, e.g. those that vary from the transcriptome or those that vary between species, I will tentatively say that either should work for you. I suppose calling variants against the transcriptome may be simpler as alignment records would be kept minimal.
Your project sounds complicated and we have some forum threads that should be of interest to you, e.g. here, here and here. Also, see this FAQ. I'm sure there are more discussions that would interest you and you should find them by searching the forum.Post edited by shlee on
Thank you very much Shlee!
vyellapa Member ✭
Curious if the haplotype caller canbe replaced with Mutect to identify low VAF variants?
Have a look at this thread which should help.
jeales Member ✭
The STAR documentation strongly suggests supplying a GTF file of transcript annotations when building the genome index.
Is there any reason that step 1 does not supply a human GTF?
I was hoping that if I supplied a GTF then the splice junctions used in the second pass would be more stable across multiple samples
Is it possible to annotate the resulting VCF with gene name and RS number (for human samples)? Which part of the analysis needs tweaking for this, the STAR alignment? Can you please provide an example?
If I am also interested in phasing of those SNPs that called from this pipeline, can I just enable --emitRefConfidence GVCF? Or I just use the exactly same pipeline, use ReadsBackedPhasing?
You can use VariantAnnotator with --dbsnp, --resource, and --snpEffFile after calling variants with HaplotypeCaller.
Yes, the latest version of HaplotypeCaller does phasing automatically, either in normal mode or in GVCF mode
You only need to run ReadBackedPhasing if you want to merge MNPs.
@jeales I think this recommendation to use a GTF file came up after we wrote up our docs on this. Ultimately we defer to Alex Dobin for the most up to date way to run STAR since he is the author of that program. We'll try to make that more clear in the docs.
Hi @Sheila ,
Thanks for replying. However, in my case, it did not automatically perform phasing. I used this command to run on my RNA Seq data:
java -jar GenomeAnalysisTK.jar -T HaplotypeCaller -R $genomeFasta -I $Input_BAM -dontUseSoftClippedBases -stand_call_conf 0 -stand_emit_conf 0 -o $Output_VCF
In the output VCF, I did not see SNPs being phased. And I saw this message from GATK:
HaplotypeCaller - Disabling physical phasing, which is supported only for reference-model confidence output
Do you know why?
If I want it to do phasing, what should I use?(I know it is RNAseq based, phasing can be conducted just locally, but I need that.) Now I just used seperated steps: first use UnifiedGenotype, then ReadBackedPhase. Do you have any suggestion?
Oh, I see. I'm sorry I mislead you. It seems phasing is only supported in GVCF mode. Right now, we recommend HaplotypeCaller in normal mode only for RNA-seq, because we have not done testing for the GVCF workflow. However, some users have reported using the GVCF workflow for RNA-seq data.
You can either try the GVCF workflow and thoroughly test your results, or run HaplotypeCaller then ReadBackedPhasing afterwards.
J_R NCMember ✭
Yet another question, sorry. Is there more documentation unpacking why there's an RNA-specific recommendation of hard-filtering clustered SNPs?
Hi Joanna,
I think this article is all we have for now. There may be more to come in the future.
Question about GATK 3.7 and RNA-Seq pipeline, is it safe to say the command "-stand_emit_conf 20.0" for STEP 6 has been removed from the pipeline commands? I got a warning this command was deprecated and so I removed it, otherwise the pipeline won't run.
Just read your updates on the new version---- Which state you removed it.... My faultPost edited by mjtiv on
We'll update the article too, thanks for pointing this out.
@Geraldine_VdAuwera @Sheila @shlee
Regarding the filtering of VCF from RNAseq using SNPcluster:
java -jar GenomeAnalysisTK.jar -T VariantFiltration -R hg_19.fasta -V input.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o output.vcf
Is there a method or functionality to prepare the bed file based on this SNP cluster?
I would rather want to filter the reads from the bam file and run Denovo assembly and blast search to find other potential mapping areas for the reads on the genome.
Since, this problem addresses
paralogous alignmentin which one set of reads (mostly containing reference allele) will be true alignment while the other would be paralogs; I was thinking if reads can be filtered in following manner.
- select the site that have SNP clusters and prepare bed file.
- A. filter reads based on that bed file.
- B. Or, only select the reads that contains dominantly non-reference SNP cluster, suggesting that is the paralogous read. This way only paralogous alignment will be filtered.
Hi @everestial007,
If by paralogous alignments you are referring to reads that can multiply map within a given genome, then I think low or zero mapping scores (MAPQ) in a region would be the basis for your analysis workflow. I have to say that it's not clear to me how filtering on FisherStrand helps towards identifying regions with low/zero MAPQ reads. I do see that QualByDepth, being normalized by depth supporting a variant, could help but this metric also factors for quality. It's not clear then how quality would correlate with regions with reads with low or zero MAPQ scores. I'm just learning about these annotation metrics so perhaps I've misunderstood your approach.
Can you please define what you mean by SNP cluster? We have some filters that target reads that are likely contaminant (GATK engine read filters here) as well as sites with clustered events (in MuTect2).
Let me answer the parts of your questions that I can.
Within Picard and GATK tools, we typically use interval lists more so than bed files. Here are some tools that can help you prepare an intervals list:
- Picard VcfToIntervalList
- Picard IntervalListTools
- Picard BedToInteralList
To convert an intervals list to a bed file, please see the awk command in Tutorial#7156, right before the start of section 3.
You might also be interested in Picard's MakeSitesOnlyVcf, to simplfy the starting VCF. Also, it appears Picard's FilterVcf may also do your FS/QD filtering.
All this being said, to subset out reads to filter reads based on sites within a VCF you really don't need to create a bed/intervals file. You can simply supply the vcf with the
-Lparameter, e.g.
-L file.vcf, in conjunction with a GATK tool, e.g. PrintReads. The
-Loption also accepts interval lists and bed files. You might want to double-check if all that is needed is overlap to subset reads or if you need to supply padding (
--interval_padding) to cover the entirety of reads and perhaps change the merging approach (
--interval_set_rule). These are defined in the GATK engine parameters that you can use with all GATK tools.
P.S. @everestial007. I've just confirmed with our engineer that you can print out all reads that touch the interval/site. No need to expand intervals.
In your RNA-Seq pipeline it is mentioned known issues may occur with HaplotypeCaller with different alleles that are far from the 0.5 ratio which will influence ASE studies. The suggestion is to use a "DNA-aware" mode of the caller to fix this issue. Could you specifically explain more about this "DNA-aware" mode and how to perform it in HaplotypeCaller or if necessary the entire pipeline? Thank You
@mjtiv What you're referring to is a hypothetical feature we were considering but never had the opportunity to build. The idea was to use a DNA normal to control for estimating baseline expectations.
Judy ChinaMember ✭
I have the same problem with "MESSAGE: Error caching SAM record xxx, which is usually caused by malformed SAM/BAM files in which multiple identical copies of a read are present." during IndelRealigner, but I have tried adding command line -rf NotPrimaryAlignment , it doesn't work. Is there anybody having some solutions?
@Judy please do NOT post the same comment multiple times. It clutters the forum and wastes our time.
Have you validated your bam file?
kulbirsinghsandhu winnipegMember ✭
Hi everyone, I am doing trying to find variants using my RNAseq data (it was generated for expression analysis). I had 8 samples which were indexed and sequenced in a single lane. I received 16 FASTQ paired end files from 8 samples after sequencing. These 8 samples are derived from 2 vareities and 2 conditions. I am interested in finding variants among these 2 plant varaities. The fastq files were trimmed for adaptor sequences and only paired sequences were retained by using Trimmomatic. My question is that during the first and second alignment step with STAR, can i use all the fastq files for alignment at the same time? This will get me one output sam file to take into next steps upto base recalibration. Or should I align each read pair separately and then go upto base recalibration steps separetly for each read pair, and merge before variant calling step? Please suggest. Thanks, Kulbir
Hi Kulbir,
I think this thread will help.
Yogesh south koreaMember ✭
I am Trying to find SNP in the transcriptome. I have finished the Split'N'Trim and reassign mapping qualities step. after this can I do indel realingment using these command: But sorted bam file is needed, can I sort the bam file using samtool:
/home/samtools/samtool sort dedup.bam >sorted.dedub.bam
Generate intervals of interest from sample alignments:
java -jar GenomeAnalysisTK.jar \
-T RealignerTargetCreator \
-nt 4 \
-R refgenome.fa \
-I **sorted.dedup.bam **\
-o realign.intervals
Realign (multiple sequence alignment):
java -jar GenomeAnalysisTK.jar \
-T IndelRealigner \
-R refgenome.fa\
-targetIntervals realign.intervals \
-I sample1.sorted.dedup.bam \
-o sample1.sorted.dedup.realigned.bam
Fix mate pair info in BAM (PICARD):
java -jar $PICARDDIR/picard.jar FixMateInformation \
INPUT=sample1.sorted.dedup.realigned.bam \
OUTPUT=sample1.sorted.dedup.realigned.fixmate.bam \
SO=coordinate \
The above question is duplicated at http://gatkforums.broadinstitute.org/gatk/discussion/9597/indel-realingment-in-rnaseq-reads#latest.
Govardhan IndiaMember ✭
I performed variant calling using RNA-seq in our cohort as we had poor quality exome seq data and thanks to GATK for this pipeline I identified commonly identified hot-spot mutations.
We are planning to include these findings in upcoming publication and I have question about cutting this pipeline.
Should I just cite three GATK publication??
Also, did anyone published anything using same pipeline??
Thanks in advance.
Hi Gova,
We recommend citing both the Best Practices pipeline (from the GATK site) and one of the GATK papers.
With variant calling step (HaplotypeCaller), how is strand direction taken into account? The reason I am asking is because I am trying to compare RNA-seq variant data produced using this pipeline to genotyping data that is always (+) strand.
I think the variants are always reported in context of the reference genome you supplied. The RNAseq transcripts may come from either + or - strand but, variants are always reported in context of the ref allele.
Hope you already know this now. But, just to verify that assertion I made earlier: https://groups.google.com/forum/#!topic/rna-star/ehHFgV_2cjU
See the response on similar problem, from
Alex Dobinthe author of STAR-Rna.
Hi, has this pipeline been tested with the HISAT2 aligner? If so, how did it compare to the results with STAR?
According to this article, "For RNA-seq, we evaluated all the major software packages that are specialized in RNAseq alignment, and we found that we were able to achieve the highest sensitivity to both SNPs and, importantly, indels, using STAR aligner. ". You can find more details under "1. Mapping to the reference"
According to this article, "For RNA-seq, we evaluated all the major software packages that are specialized in RNAseq alignment, and we found that we were able to achieve the highest sensitivity to both SNPs and, importantly, indels, using STAR aligner. ". You can find more details under "1. Mapping to the reference"
Yes, I noticed that, but I am it is not very clear to me when this note was added to the page as it was first created in 2014 when HISAT2 was not available yet. It says it was last updated in 2017, but were the newer aligners tested as well?
Thanks for the clarification!
I am not sure if there has been any testing that recently. I will check with the team and get back to you.
Of course, STAR is what we recommend, but you are free to use whatever aligner you have determined is best for your needs
Thank you! I appreciate you check-in with the team!
Hi @lshepard,
I believe testing was done between TopHat and Star and the decision to use Star had in part to do with its output of compatible metadata, e.g. CIGAR string representations.
That's right, a comparison was made on several packages including TopHat and Star; criteria included sensitivity metrics and interoperability of results (well-behaved output format). Wrt sensitivity we found that Star gave the best overal results across SNPs and indels, though iirc other packages may have provided slightly better results over SNPs alone. That was several years ago though, and we have not reevaluated this since as RNAseq has not been a development priority. This is likely to change in the near future however.
Sounds good, thank you for giving the details of the analysis performed! I might do some of these comparisons myself in the near future.
krdav Member, Broadie
Somebody else has already done a similar comparisons that indeed does claim better results using TopHat2:
uff Member
Hi Geraldine, I am planning to do variant calling in RNA-seq data but haven't prepared the sample library yet. Could you please give me some advice on the minimum sequencing depth required to find meaningful true variants?
@krdav said:
Somebody else has already done a similar comparisons that indeed does claim better results using TopHat2:
Interesting, thanks for pointing it out. I will need to check the details, but if that is indeed the case then I would expect potentially better results with HISAT2. Thanks again!
We don't have any specific recommendations, but GATK tools are tested on 30X data.
dsap Member
Hi @Geraldine_VdAuwera and @Sheila,
It has been several years since this article was originally was posted. You described this possibility: "We also plan to add functionality to process DNAseq and RNAseq data from the same samples simultaneously"
Is HaplotypeCaller yet capable of using WGS and RNA-seq data simultaneously to improve SNP/Indel calling?
Hi Dan,
I don't think there has been much done in that area, so it is not a good idea to run on both DNA and RNA together. Let me confirm with the team and get back to you.
Unfortunately we haven't implemented that functionality, sorry.
jgould GouldMember ✭✭
Is there a WDL available that implements this workflow?
I am checking with the team and will get back to you.
@jgould We currently don't have a WDL for variant calling on RNAseq; it's on the roadmap of workflows we aim to provide and support in early 2018.
Hello GATK team I am new in the use of this toolkit and therefore I have some questions. I am working with RNA paired end read sequences. My goal is to find SNPs in 2 strains of trout to be able to compare them, from each strain I have 8 fish sampled, each fish with 6 different tissues. I already went through the best practices workflow and I have several questions about the STAR steps. My first steps are the following:
picard CreateSequenceDictionary R= genome.fna O= genome.dict
samtools faidx ReferenceGenome/genome.fna
STAR --runMode genomeGenerate --genomeDir GenomeDir –genomeFastaFiles ReferenceGenome/genome.fna --sjdbGTFfile ReferenceGenome/Annotations.gff --limitGenomeGenerateRAM 140000000000 --runThreadN 8
STAR --runMode alignReads --genomeDir GenomeDir --readFilesCommand zcat --readFilesIn Forelle/${R1} Forelle/${R2} --outFileNamePrefix ${sample}_ --runThreadN 8 /
I understand that I have to do the previous step for every tissue independently. Therefore I would be generating 96 SJ.out.tab files.
For the second pass how can I create the genome indexes if I have 96 files of spliced junctions? or do I have to repeat this process independently 96 times so that it will be like creating 96 genome directories?
STAR --runMode genomeGenerate --genomeDir GenomeDir2 --genomeFastaFiles ReferenceGenome/genome.fna --sjdbFileChrStartEnd ${sample}_SJ.out.tab --sjdbOverhang 64 --limitGenomeGenerateRAM 140000000000 --runThreadN 8 /
STAR --genomeDir GenomeDir2 --readFilesCommand zcat --readFilesIn Forelle/$R1 Forelle/$R2 --outFileNamePrefix ${sample}2pass --runThreadN 8 /
I am not sure I understand what you mean by "For the second pass how can I create the genome indexes if I have 96 files of spliced junctions? or do I have to repeat this process independently 96 times so that it will be like creating 96 genome directories?" What do you mean by "genome indexes"? Are you aligning to one reference genome?
Hi Sheila,
Yes, I am aligning to one reference genome. Therefore, since I understand that the alignment has to be done tissue by tissue, at the end of the first pass I have 96 SJ.out.tab files. But I am unsure about how to include them for the second pass.
Ah, I see. Thanks for the clarification. We have an example of how to run the alignment step here. I think you will have to re-run for 96 files. You may want to ask the STAR team for any other questions about the usage, as we cannot help much with that.
P.S. For your samples, are you trying to determine differences in tissues? I am assuming so. You may also find this thread helpful.
flynnchen Member
Hi GATK Team,
It seems like you guys didn't use GTF annotations in the STAR index mapping. Would you guys recommend using GTF for mapping RNA to reference using STAR? Some of our co-workers suggested that it might be an option. Would that improve the variant calling quality?
bhaas Broad InstituteMember, Broadie ✭
Is there an updated version of this protocol for use with gatk-4, as some of the options in gatk have changed.
I think this thread may be helpful.
The Best Practices are in development for GATK4, so we don't have full documentation for it yet.
Vrushali Member ❠
The tools RealignerTargetCreator and IndelRealigner are obsolete in GATK4. Are there any replacements for these tools in GATK4?
Also, there are two option -BQSR (used with PrintReads) and -stand_emit_conf (used with HaplotypeCaller) that are not available with GATK4. What should be done in these cases?
Have a look at this thread. However, there are plans to port those tool now, and you can keep track of it here.
For BQSR, have a look at this thread. For stand_emit_conf, have a look at this blog post to find out why it is gone.
Question about pipeline, I just got into a conversation with a lab mate about using a gtf file in an RNA analysis pipelines and realized that GATK's Best Practices for RNA-Seq does NOT appear to use a reference gtf file in the analysis (slight heart attack thinking need to re-run everything). Could you expand on this gtf free option? Also, I did look back at my notes and see I did use gtf file to help create the STAR genome index reference file (hopefully doesn't mess things ups).
Any feedback on this topic would be greatly appreciated and fingers crossed its not bad news.
Have a look at this thread.
lfall Member
I am trying to call SNPs in my RNA-seq data using the GATK pipeline, but I am running into the same error over and over. The alignments were done using STAR and I was able to make it through the workflow steps 1 (mapping to reference (STAR)) and two (Add read groups, sort, mark duplicates, create index (Picard)). When I get to the Split'N'Trim step, I get an error. I have tried changing and removing just about everything I can think of in the command but I still get this error saying that the argument is wrong. I've checked so many times on the GATK best practices to see if my scripts are wrong, but they seem to match what is written on the workflow. I'm very sorry if this question has an obvious answer, I have tried so many things and I can't seem to find the answer anywhere! Any help is very much appreciated Thank you!
This is the script I am running:
SBATCH --ntasks 2
SBATCH --mem 24G
SBATCH --mail-type ALL
SBATCH -J split.n.trim
module load GATK/3.8-0-Java-1.8.0_121
module load Java/1.8.0_152
module load R/3.2.5-IGB-gcc-4.9.4
module load Python/2.7.13-IGB-gcc-4.9.4
java -jar GenomeAnalysisTK.jar \
-T SplitNCigarReads \
-R /home/a-m/lfall/Fusarium_rnaseq/data/genome/NCBI/GCF_000240135.3_ASM24013v3_genomic.fna \
-I BHS.dedupped.bam \
-o BHS.split.bam \
-rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS
This is the error I get after running this script:
[[email protected] rg.duplicates.index]$ more slurm-622962.out
To execute GATK run: java -jar $EBROOTGATK/GenomeAnalysisTK.jar
The following have been reloaded with a version change:
1) Java/1.8.0_121 => Java/1.8.0_152
The following have been reloaded with a version change:
1) Java/1.8.0_152 => Java/1.8.0_121
INFO 16:00:00,666 HelpFormatter - ----------------------------------------------------------------------------------
INFO 16:00:00,685 HelpFormatter - The Genome Analysis Toolkit (GATK) v3.8-0-ge9d806836, Compiled 2017/07/28 21:26:50
INFO 16:00:00,685 HelpFormatter - Copyright (c) 2010-2016 The Broad Institute
INFO 16:00:00,685 HelpFormatter - For support and documentation go to https://software.broadinstitute.org/gatk
INFO 16:00:00,685 HelpFormatter - [Thu May 10 16:00:00 CDT 2018] Executing on Linux 3.10.0-693.11.1.el7.x86_64 amd64
INFO 16:00:00,685 HelpFormatter - Java HotSpot(TM) 64-Bit Server VM 1.8.0_121-b13
INFO 16:00:00,688 HelpFormatter - Program Args: -T SplitNCigarReads -R /home/a-m/lfall/Fusarium_rnaseq/data/genome/NCBI/GCF_000240135.3_ASM24013v3_geno
mic.fna -I BHS.dedupped.bam -o BHS.split.bam -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS
INFO 16:00:00,694 HelpFormatter - Executing as [email protected] on Linux 3.10.0-693.11.1.el7.x86_64 amd64; Java HotSpot(TM) 64-Bit Server VM 1.8.0_12
INFO 16:00:00,699 HelpFormatter - Date/Time: 2018/05/10 16:00:00
INFO 16:00:00,699 HelpFormatter - ----------------------------------------------------------------------------------
INFO 16:00:00,699 HelpFormatter - ----------------------------------------------------------------------------------
ERROR StatusLogger Unable to create class org.apache.logging.log4j.core.impl.Log4jContextFactory specified in jar:file:/home/a-m/lfall/Fusarium_rnaseq/r
ERROR StatusLogger Log4j2 could not find a logging implementation. Please add log4j-core to the classpath. Using SimpleLogger to log to the console...
INFO 16:00:00,997 GenomeAnalysisEngine - Deflater: IntelDeflater
INFO 16:00:00,997 GenomeAnalysisEngine - Inflater: IntelInflater
INFO 16:00:00,997 GenomeAnalysisEngine - Strictness is SILENT
ERROR A USER ERROR has occurred (version 3.8-0-ge9d806836):
ERROR This means that one or more arguments or inputs in your command are incorrect.
ERROR The error message below tells you what is the problem.
ERROR If the problem is an invalid argument, please check the online documentation guide
ERROR (or rerun your command with --help) to view allowable command-line arguments for this tool.
ERROR Visit our website and forum for extensive documentation and answers to
ERROR commonly asked questions https://software.broadinstitute.org/gatk
ERROR Please do NOT post this error to the GATK forum unless you have really tried to fix it yourself.
ERROR MESSAGE: Invalid command line: Failed to load reference dictionary
Hi @lfall,
The message:
ERROR MESSAGE: Invalid command line: Failed to load reference dictionary
Indicates the tool is unable to access the dictionary associated with the reference. Your reference is
-R /home/a-m/lfall/Fusarium_rnaseq/data/genome/NCBI/GCF_000240135.3_ASM24013v3_genomic.fna
So here are the things I would try:
- Generate a reference dictionary with Picard CreateSequenceDictionary in the same directory as the reference.
- It's possible the tool is having an issue with the
.fnaextension of your reference. Typical reference FASTA extensions are
.fa. Try switching this out, assuming your reference is actually in simple FASTA format. Otherwise, try to convert your FNA file to FASTA.
after creating an index with only 1.sj.out file. I realized that my sam files are missing columns. How can I create the index with multiple sj.out.tab files ?
Hi @kibrahim90, it's not clear what you are asking. Perhaps you will find SAM specifications here helpful.
@shlee said:
Hi @kibrahim90, it's not clear what you are asking. Perhaps you will find SAM specifications here helpful.
how do I run star (aligner) to create an index with multiple sj.out.tab files is what im asking. Its not in the manual. I checked.
@kibrahim90, Star is not a GATK/Picard tool and therefore we do not support it. Please try posting your question to Biostars or SeqAnswers.
CarolineJ Member
Hi team -
Do you have any suggestions for the variant calling step (#6) and the hard filtering step (#7) for multisample RNAseq experiment aligned to a de novo transcriptome? Also, is there a still a benefit to using HaplotypeCaller over UnifiedGenotyper for cases where the reference is a de novo assembled transcriptome?
- GATK best practices for variant calling for a single sample/align to ref. genome
java -jar GenomeAnalysisTK.jar -T HaplotypeCaller -R ref.fasta -I input.bam -dontUseSoftClippedBases -stand_call_conf 20.0 -o output.vcf
My "best guess" at customizing GATK best practices for a multisample experiment with alignment to a de novo transcriptome
java -jar GenomeAnalysisTK.jar -T HaplotypeCaller -R de.novo.transcriptome.fasta -I input_sample1.bam input_sample2 -stand_call_conf 20.0 -o output.vcf
- GATK best practices for hard filtering
java -jar GenomeAnalysisTK.jar -T VariantFiltration -R de.novo.transcriptome.fasta -V input.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o output.vcf
My best guess is that the hard filtering settings would be identical for a multisample/denovo transcriptome ref design.
Unfortunately, our team has not dedicated much time to the RNA pipeline, but we do have some recommendations in the github repo here. I think the team is now working on better Best Practices for RNA-seq, so keep an eye out for those.
When I run the command
mkdir runDir
cd runDir
STAR --genomeDir /scratch/snyder/y/yin168/gatk/test/genomeDir --readFilesIn /scratch/snyder/y/yin168/gatk/test/400_R1.fastq /scratch/snyder/y/yin168/gatk/test/400_R2.fastq --runThreadN 5
I always got an error:
/var/spool/torque/mom_priv/jobs/1411989.snyder-adm.rcac.purdue.edu.SC: line 18: 34161 Segmentation fault
(sometimes it is line 16, instead of line 18)
How should I resolve this issue?
Thanks a lot!
In the gatk RNAseq variant calling pipeline, there is a step for the 2-pass STAR as below:
3) For the 2-pass STAR, a new index is then created using splice junction information contained in the file SJ.out.tab from the first pass:
genomeDir=/path/to/hg19_2pass mkdir $genomeDir STAR --runMode genomeGenerate --genomeDir $genomeDir --genomeFastaFiles hg19.fa --sjdbFileChrStartEnd /path/to/1pass/SJ.out.tab --sjdbOverhang 75 --runThreadN
Here, --sjdbOverhang is set to 75. However, the default value of --sjdbOverhang is 100. Why is it set to 75 instead of 100? Does that make a difference? What value should I set for my analysis.
Thanks a lot!
When running the gatk RNAseq variant calling pipeline, I am not quite sure about whether I should spend time doing 4. Indel Realignment and 5. Base Recalibration.
For 4 - How significant the effect would be if I ignore this step? It says "we only recommend performing the realignment step if you have compute and time to spare (or if it's important not to miss any potential indels)". I am calling SNPs from RNAseq, so how should I evaluate/determine whether it is important not to miss any potential indels? Could you list potential cases in which it is important not to miss any potential indels (or in which it is okay to miss potential indels)?
Also, I see a post saying that, if a haplotype assembly is done (for example, run HaplotypeCaller), then it is not necessary to run Indel Realignment. Is this true? Since I am going to do 6. Variant calling by running HaplotypeCaller, does this mean I do not need to run Indel Realignment at all?
For 5 - How big/marginal the effect would be if I just skip 5. Base Recalibration? When would the qualities have systematic error modes, as mentioned in the explanation for this step? Also, since --known-sites is a parameter I would need to provide in Base Recalibration, but I do not have a "known sites" resource file. Does it still make sense to do this step? If so, how should I generate/prepare a "known sites" resource file?
Thanks.Post edited by XiaoshenYin on
I use the command to filter called variants according to the pipeline:
java -jar ~/bin/GenomeAnalysisTK-3.8-0-ge9d806836/GenomeAnalysisTK.jar -T VariantFiltration -R ../sl_genome/gPmar100.fa.txt -V 400.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o 400_filter_5.vcf --setFilteredGtToNocall
In the filter column, I see "PASS", "snpcluster" and "QD". I would assume "QD" indicates that a specific locus has a poor call (i.e. with QD<2.0) and thus this variant should be filtered. Even though I add "--setFilteredGtToNocall", I still see that the genotype is "0/1". However, the genotype of this variant should be "./." because I use "--setFilteredGtToNocall". I try different options even with the latest gatk 4, but it still does not work. How can I make --setFilteredGtToNocall work and filter out the poor variants or set their genotypes to ./.?
Also, what is the difference between gatk-package-22.214.171.124-local.jar and gatk-package-22.214.171.124-spark.jar? The gatk-package-220.127.116.11-spark.jar does not work on my cluster, so is it okay if I use gatk-package-188.8.131.52-local.jar?
I am calling variants from RNAseq data using this gatk pipeline. I did what the pipeline suggest exactly and did not set ploidy for HaplotypeCaller. I got both "." and "./." in the vcf file. "./." stands for a diploid site at which the genotype cannot be called and "." stands for a haploid site at which the genotype cannot be called. Why do I get both missing haploid and diploid sites? To be more specific, why do I get ".", haploid site at which the genotype cannot be called? Should I keep or remove "." in analysis?
TomWillDo Member ✭Hi there,
Sorry if this has been answered previously, but I can't seem to find it. Previous SNP/Indel hard filters (to serve in place of VQSR) include filters for SOR, but they seem to be missing from the RNA-seq pipeline. If someone could point me towards why they haven't been included, I would be appreciative!
hamaor Member ❠Thanks a lot for this guide!
I used this pipeline to analyze 6 samples of RNA-Seq (3 replicate of treatment and 3 replicate of control).
after generating the filtered vcf, I'm using the GenotypeGVCFs to join treatment replicates together and control replicates together. (and I might want to join the whole dataset).
Technically, 3 vcf files each 1.7G was joined into single 24MB file.
is that reduction normal for the algorithm? can I make any validation to make sure I'm on the safe side?
encoded79 Member ❠Hello there
I used your mutation analysis pipeline for RNA-seq data. However, when i compare the BAMs , pre and post CIGAR hard clipping on the iGV , i still see overlapping reads for intronic regions. Basically, no clipping seems to have been done. This is also reflected on the VCF files, where nearly half of variants are in fact intronic. I appreciate it if you could elaborate on the possible reasons for this. Thanks a lot
encoded79 Member ❠
Hello @shlee ,
Step 3 in the pipeline for calling variants from RNAseq recommends:
java -jar GenomeAnalysisTK.jar -T SplitNCigarReads -R ref.fasta -I dedupped.bam -o split.bam -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS
However, when I try gatk4.0, I did not find "-rf ReassignOneMappingQuality -RMQF 255 -RMQT" or "-U ALLOW_N_CIGAR_READS" in optional arguments, although they are present in gatk3.8. Is it because updates on gatk4.0 make it unnecessary to set those two parameters? In this case, can I use gatk 4.0 without setting those two arguments to run the step of Split'N'Trim and reassign mapping qualities?
seven32 Member ❠What is the correct syntax for calling HaplotypeCaller for RNA-seq data for GATK 4?
Hi, regarding your comment in Oct 2014,
"At this time the RNAseq pipeline is only meant for single-sample processing, yes. We have not finished testing how the tools perform on RNAseq for multiple samples."
Please, is there any update in GATK 4?
I have many samples (RNA Seq) and I would like to merge them in a VCF file.
Thanks in advance.
At 7. Variant filtering:
I see you recommended the following:
--filterExpression "FS > 30.0" -filterName QD --filterExpression "QD < 2.0"
Please, are these for SNPs, Indel or both?
and, what is your opinion if I use the same filterexpression on both SNPs and Indel?
any answer, please!
zjwang ChinaMember ❠> @XiaoshenYin said:
> Hello @shlee ,
> Step 3 in the pipeline for calling variants from RNAseq recommends:
> java -jar GenomeAnalysisTK.jar -T SplitNCigarReads -R ref.fasta -I dedupped.bam -o split.bam -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS
> However, when I try gatk4.0, I did not find "-rf ReassignOneMappingQuality -RMQF 255 -RMQT" or "-U ALLOW_N_CIGAR_READS" in optional arguments, although they are present in gatk3.8. Is it because updates on gatk4.0 make it unnecessary to set those two parameters? In this case, can I use gatk 4.0 without setting those two arguments to run the step of Split'N'Trim and reassign mapping qualities?
> Thanks.
Hello, I have the same question as you, Have you solved this problem? Why do not find "-rf ReassignOneMappingQuality -RMQF 255 -RMQT" or "-U ALLOW_N_CIGAR_READS" in optional arguments
Hello, how should I properly cite the pipeline of Calling variants in RNAseq in a manuscript?
@zjwang Hello, I ended up using the older version, if I remember correctly.


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By: Jack Moore
Each week we'll be providing you with insight into the best (and worst) baseball players to play in your fantasy baseball league.
1. Zack Greinke, SP, LAA: Typically, a move from the NL to the AL is a bad omen for pitchers. However, escaping homer-friendly Miller Park and the shoddy defense behind him in Milwaukee should be a boon for Greinke. Even though he took a loss in his first home start with Anaheim, he was excellent, striking out eight, allowing seven hits and just two earned runs in seven innings Sunday. Look for more of that in front of an Angels defense that is above average at nearly every position on the field and owns the league's best defensive efficiency.
2. Carlos Santana, C, CLE: Santana entered the season considered by many fantasy experts – including this one – to be the top catcher on the board thanks to his rare power at the position. We were dead wrong in the first half, as Santana limped to a .221/.339/.336 line at the break. Those who persevered have been rewarded, as Santana has exploded out of the break, hitting .306/.469/.653 with four homers – one under his first-half total – in the 16 games since. Sometimes with good players all you need is a first sign that they're breaking out of a slump, and for Santana, it's past time to believe.
3. Lorenzo Cain, OF, KCR: Cain was a deep sleeper entering the season but missed much of the first half due to injury. He's back, and in his first 21 games he's hitting .279/.321/.485 with three homers, nine runs and 15 RBI. Don't expect the power to quite keep up, but he has speed to steal bases in addition to a decent average, making him a sharp pickup for leagues deep in the outfield.
4. Alexi Ogando, SP, TEX: With Colby Lewis injured and the Rangers yet to make a major move on the trade market, it appears Ogando is destined for a return to the starting rotation. He suffered a leg injury during his last attempt at starting, but that was on a bunt attempt in a National League stadium, something he won't deal with the rest of the season. Ogando has a 3.49 ERA and 1.118 WHIP career out of the rotation with an excellent 2.93 K/BB, making him a solid under-the-radar rotation option for the stretch run.
5. Paul Maholm, SP, CHC: Maholm became the first Cubs starter to record six straight starts of one run or fewer and at least six innings pitched with Sunday's outing against the Cardinals. That might be a touch unrealistic, but Maholm has had a successful career and is more than capable of producing better-than-average ERAs and WHIPs. He won't give you big strikeout numbers, but for a guy widely available on the waiver wire as August approaches, it's tough to do better. His ERA of 3.74 is eight points higher than last year's total of 3.66 – and his value only goes up if the Cubs deal him before Tuesday's trade deadline, a distinct possibility.
1. Matt Harrison, SP, TEX: Harrison has given up three home runs in his last two starts, leading to nine runs in 13 innings. Somehow, despite pitching in a great hitters' ballpark in Texas, Harrison was allowing just 0.59 home runs per nine innings leading into this two-start stretch. Especially with the hyper-warm Texas weather setting in, don't be surprised if we see even more multi-homer starts from Harrison down the stretch, with his ERA approaching 4.00.
2. James McDonald, SP, PIT: McDonald has been awful in three starts since the break, allowing more walks than strikeouts to go with a 9.00 ERA. Much of his excellent first half was due to pristine control – just 31 walks in 110 innings. Even if that 2.5 BB/9 rate rises up to just 3.0 or 3.5, we could see an average McDonald in the second half instead of the one putting up ace-level numbers. McDonald has a
3.8 career BB, making such a rising tide more likely than not.
3. Bryce Harper, OF, WAS: The talent is undeniable. What Harper has done this year – 9 HR, 13 SB, a .261/.338/.430 line – is all fantastic for a 19-year-old, and he should be near the top of most keeper lists come the end of the season. However, for those playing in redraft leagues, Harper may be the kind of guy whose name is more attractive than the production. Particularly in standard three outfielder leagues, there have been so many productive outfielders that Harper's production is replaceable off the waiver wire – see a guy like Michael Brantley, for example.
4. Alcides Escobar, SS, KCR: Here's the question you have to ask about Escobar: is he a legitimate .300 hitter? If so, he's well worth owning at shortstop. He gives you plus numbers in two categories – 15 steals to go with the average – which is more than most shortstops at this point. But if he isn't – if he is instead a .280 hitter or a .264 hitter (his career mark), what then? He doesn't score runs (40 in 98 games), nor does he provide power or RBI. He's already slipping over the last two weeks (.250/.263/.321) and his 16.5% strikeout rate is not one befitting a .300 hitter. Expect a second-half slide.
5. Francisco Cordero, RP, HOU: Just like in Toronto, don't expect Cordero's reign as the Houston closer to last too long. He already has an 18.00 ERA in his three innings with the club and is up to 6.75 on the year as both an Astro and a Jay. As such, it's better to speculate on his replacement than get hit by the runs he'll most certainly allow this season – go grab Wilton Lopez instead.
Jack Moore is a graduate of the University of Wisconsin-Madison with degrees in Mathematics and Economics. His work can also be found at FanGraphs.com, DisciplesOfUecker.com, RotoWire.com, AdvancedNFLStats.com and ESPN. Follow him on twitter at @jh_moore.

subset: commoncrawl


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Uganda Police Force: Clarification on allegations made by Bweranaho Charles against the Flying Squad (09.05.2018)
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Posted in Africa, Civil Service, Development, Ethics, Governance, Government, Law, Leadership, Politics, Transparency and tagged Abdallah Kitatta, ACP Polly Namaye, ADF, Agnes Ithungu Nyabaghole, Agnes Itungu Mumbere, AIGP Andrew Kaweesi, AIGP Asan Kasingye, Allied Democratic Force, Amos Ngabirano, Amuria, Amuria District, Andrew Felix Kaweesi, Arthur King Timbaganya, Asan Kasingye, Assassination, Babi Kahemba, Babwasi, Baganda Kingdom, Bakonzo, Bamba, Biira Christine Mukirania, Blue Friday, Boda Boda 2010, Boda-Boda, Brig Muhoozi, Brig. Leopold Kyanda, Brig. Sabiiti Muzeei, Brigadier Muhoozi Kaneirugaba, Bugoye., Buhikira Royal Palace, Bundibugyo, Bundibugyo district, Bushenyi, C4DC, Cap. Mike Mukula, Captain Ojara, CDF Gen. Katumba Wamala, Central Government, Charles Etukuri, Charles Mumbere, Charles Wesley Mumbere, Chelimo, Chieftaincy of Military Intelligience, Christine Mukirania, Christopher M.T. 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The TBR Knowledge base release 1. is the first half of our research findings to be published online. The second volume covers the .30 to .458 calibers along with Proprietary cartridge designs and will be published live in due course.
IntroductionThe information you are about to read is not an ordinary, general overview of hunting cartridges. This Knowledge base is the culmination of 20 years intensive field research and as new bullet designs emerge, this research continues. The data base on this website is and will be updated accordingly.
As of 2011, over 7000 head of game were killed and autopsied for the initial body of work. To obtain a set of results, animals were taken at differing ranges, differing shot placement and angles. The tests then had to be retaken several times with each cartridge and each bullet to ensure repeatable results. Naturally, while testing so many cartridges over so many years, firearms expertise occurred by attrition.
In 2007, based on demand, we formed the company Terminal Ballistics Research ltd. TBR has three main divisions including rifle accurizing services, guided and tutorial hunting and the research division which you are now viewing.
Essentially, the goal of our hunting cartridge research is to give shooters a set of expectations regarding both ammunition and firearms. Where a comment is made in the ahead pages stating that a cartridge will do such and such on impact, readers can expect those results to be clinically factual and repeatable in the field rather than based on passing opinion.
To get the most out of this research, readers are encouraged to think about their expectations of themselves, their rifles and their cartridges. By studying the game killing section, the reader is able to start his or her own decision making process based on a solid foundation. In the cartridge section, the reader can make comparisons of various cartridges, look for similar results in his or her own cartridge and by cross referencing, will be able to notice and understand common trends in performance.
There is certainly no single cartridge to suit every hunter as each hunter has such varied needs. Some hunters are recoil shy, some have sustained shoulder injuries. Some folk are primarily woods hunters, others find their game on the open tops. Some hunters have a generous disposable income, others are on a severly limited budget. Regardless of the research presented here, readers must still make their own decisions regarding optimum cartridge usage.
Please use the navigation panel on the left hand side of this page to enter the research pages.


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Calculating MP data on client? Yeah, that would be fair...
Anyway, I thought about this concept a little, and now I think, that it's all possible to actually implement with a good part of math.
Server could calculate, for example, resource data with frequency tied to closest player's range to this resource. It's gain/loss could be multiplied on the delta-time since the last view, so no player presence would be required 24*7 for economy to flow. Though, trail-calculating the gain itself in such a variety of variables may be kind of a tough question to solve, due to complexity of orbital mechanics and local situations in smaller time fractions. Or taking care of such little details, like allowing heightmap-survey redownloads to client only once per hour, as well, as making downloads queue for players for decreasing DDOS risks.
Anyways, even this complex system would be much less lagging, than the one at EVE. Instead of thousands players, this game would likely be supporting only hundreds at the same time. Instead of hundreds variables recalculating 10 times per second, most of the time, only around 50 variables would need to be calculated each 2 seconds. So, processing powers would be required less in thousands of times, turning the server cluster into a single dedicated PC. And it's all because real space games are a lot more slowly paced, than those in arcade space with constant pew-pewing.


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Here are some of the latest health and medical news developments, compiled by the editors of HealthDay:
People Who Have More Sex Make More Money: Study
People who have sex four or more times a week make more money than those who have less sex, according to a new study.
Researchers looked at data from 7,500 people, ages 26 to 50, in Greece and found that the wages of those who had sex four or more times a week were 5 percent higher than those who didn't have sex as often, CBS News reported.
People who weren't having sex made 3.2 percent less money than those who were having sex, according to the study recently published by the Institute for the Study of Labor, an economic research institution in Germany.
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U.S. Adult Obesity Rate Nearly 28 Percent: CDC
The proportion of American adults who were obese in 2012 was nearly 28 percent, which is about the same as it's been since 2008, according to recently released results from the Centers for Disease Control and Prevention annual telephone survey.
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It's no surprise that so many states in the South and Midwest have high obesity rates because many states in those regions have high poverty rates, experts say.
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Vasectomy in 'Best Interests' of Man With Learning Disabilities: U.K. Court
A British man with learning disabilities should have a vasectomy because it is in his "best interests," a court ruled Friday.
The 37-year-old man lives with his parents and has a long-term girlfriend who also has learning disabilities. The couple had a child in 2009 and the man, known only as DE, decided he did not want any more children, the Associated Press reported.
DE's parents applied to the court for the vasectomy and the judge ruled that it was "overwhelmingly in DE's best interests" to have the procedure.
Experts testified that DE is able to consent to a sexual relationship and a vasectomy would give him a measure of independence, the AP reported.
"That 70s Show" Actress Dies
Actress Lisa Robin Kelly, best known for her role as the older sister of the main character on "That 70s Show," has died.
The 43-year-old died in a rehab facility of unknown causes, USA Today reported.
"Unfortunately Lisa Robin Kelly passed away last evening. Lisa had voluntarily checked herself into a treatment facility early this week where she was battling the addiction problems that have plagued her these past few years," Kelly's agent, Craig Wyckoff said in a statement released to People.
"I spoke to her on Monday and she was hopeful and confident, looking forward to putting this part of her life behind her. Last night she lost the battle. Cause of death has not been issued yet and no death certificate has been issued either."
On "That 70s Show," Kelly played Laurie Forman, the older sister of Eric Forman, played by Topher Grace.
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text:
"So we put Fett in the script. And LucasFilm never said no," reveals Tom Veitch, author of the Dark Empire comic series in the late 90's who partnered with artist Cam Kennedy, in a newspaper article published yesterday.
"He helped bring back Boba Fett" by JENNIFER HUBERDEAU
One character Veitch and Kennedy brought back was bounty hunter Boba Fett. In "Star Wars Insider" issue 30, Jon Bradley Snyder interviewed Veitch about the resurrection of Fett, who was "eaten" by the Sarlacc pit monster in "Return of the Jedi."
Veitch replied that he knew they could "bring back the Emperor, but nobody said anything about bringing back Boba Fett. As I worked on the plot, I researched fan feelings in Bennington, where 'Star Wars' role-playing was all the rage. Out of those heated discussions, one theme predominated: Bring back Boba Fett."
"Part of it was the [role-playing] game alluded to it, then there was the movie quote and the armor. We assumed he could have survived," Bliss said.
Kennedy, a fan of Fett, was also influential in bringing the bounty hunter back. Veitch told Snyder, "So we put Fett in the script. And LucasFilm never said no. The part about the Saarlac finding Fett 'indigestible' was a joke, sort of."
Veitch never defined how Boba Fett survived, that was defined later on in "West End Game's Sourcebook."
Read the entire article from the Bennington Banner newspaper. [Link no longer available at benningtonbanner.com.]
More about Dark Empire, including this famous line:
Boba Fett: Greetings, Solo.
Han Solo: Boba Fett! But you're dead!
Boba Fett: The Sarlacc found me somewhat indigestible, Solo.


text: Element Unknown 34 Posted May 30, 2013 (edited) Hey all, 3 weeks out of finishing the intermediate program I'm doing at the moment, will be going for my 1RM at the end of it to see where I'm at for squats, bench and dead's. Generally going to follow this warm-up progression I poached from T-Nation. Wondering what some of you guys are doing when you reach this phase, types of prep and warm-up sets you do. Also, should I be going straight into attempting this from a 5x5 program or should I be training singles for a couple weeks leading up? Edited May 31, 2013 by HumanHefner Share this post Link to post Share on other sites


text:
Daniel McAdams
The LRC Blog
That the "war on terror" is a sick, manipulative joke cannot be more clearly demonstrated by the decision of the US State Department yesterday to remove the Marxist-Islamist death and murder cult, Mujahedeen Khalq (MeK), from the US terror list.
As Glenn Greenwald pointed out earlier this year, the MeK is jointly funded and trained by Israeli and US intelligence to carry out murder and mayhem inside Iran against civilian targets. It is the murderous and violent vanguard of the US and Israeli war against Iran. It launders money fromIsrael and the US through US politicians who on one side of their mouths scream about the need to fight terrorism while on the other they accept millions to lobby for one of the most brutal and bizarre terrorist cults on earth.
When these criminal politicians start speaking about the "war against terrorism" spit on your television screen, as they are the terrorists. They are wealthy terrorists who steal your tax dollars to send overseas and recoup to lobby in favor of bloody killers of civilians in Iran.
For more background, please see this excellent piece by my favorite Iran-watchers, Leverett & Leverett.
See this from the Leveretts piece:
"…once the MEK is formally off the FTO list—a legally defined process that will take a few months to play out—Congress will be appropriating money to support the monafeqin as the vanguard of a new American strategy for regime change in Iran. In the 1990s, similar enthusiasm for Ahmad Chalabi and the Iraqi National Congress—who were about as unpopular among Iraqis as the MEK is among Iranians—led to President Clinton's signing of the Iraq Liberation Act, which paved the way for George W. Bush's decision to invade Iraq in 2003. The chances for such a scenario to play out with regard to Iran over the next few years—with even more disastrous consequences for America's strategic and moral standing—got a lot higher today."


text: Hello everyone! I'm T'erre, i live in Austin, Texas and I drive a 1999 BMW 328i convertible. When I turned 17 years old, my first car was a 1991 Acura Legend Coupe and boy i loved that car. My dad owned a 1995 Acura Legend LS Coupe 6spd in 1995 and I remember telling myself that the Legend was going to be my first car. . . long story short, i sold the car early March of 2010 in search of something with a manual transmission , fun to drive, attractive and it had to be RWD. . . I wanted a BMW. Well, on March 15th 2010 my Mom and Dad surprised and bought me my first BMW for my 20th birthday. it's funny , because when my dad sold his 1995 Legend coupe, he purchased his first BMW in 2004, it was a black 2003 540i sport with a 6speed manual. here's a little info on my car: 1999 BMW 328i convertible purchased with 63,300 miles , currently has 64,980 miles 5spd manual the bad: -the motor for the convertible top , just recently went out. -and the car has a bent nose panel and a small dent on the hood (see picture) Would like to thank the person who did that. . . Thanks for reading. ~T'erre


text:
There are no details yet, but the Walker v Kimberlin case has been dismissed.
Back in June I wrote about how Kimberlin resembled the story Hal Chase who when finally brought before authorities concerning his reputation for throwing games thanks to his manager Christy Mathewson. The result:
Lucky for Chase by the time he came to trial before league WW 1 took Mathewson to France. Without Mathewson whose live testimony would have likely doomed him Chase was able to successfully accuse players who testified in support of the charges of being part of a clique against him (sound familiar?) lied directly about past claims (sound familiar again?) and somehow managed to talk his way into acquittal. As Bill James put it on page 332 of his book:
"He was free, then. It had all be brought out into the open, and he had gotten by with it. This seems to have had a liberating effect on Chase's activities"
The rest is as they say history,
I wasn't in the court room, I don't know how or why the judge did what he did, but the end result will certainly encourage Kimberlin & co to soldier on with even more impunity.
Eventually Chase was banned after his involvement in fixing the 1919 World Series. Apparently it will take something much worse than what has already happened for the law to scream: enough!
It's not going to be pretty.
Update: Stacy McCain promises updates, but the big story is not so much the details but the emboldening of some very bad characters.
Update 2: I'd like to thank the Ray Kroc Memorial Media Center for giving Stacy and Hoegwash the ability to update us in a timely manner.
Update 3 Stacy notes two important things we see:
It's actually worse than that: It's not just bad characters, but bad behavior that have been emboldened. Why shouldn't everyone with a grudge resort to the methods Kimberlin & Co. employed against Walker?
The targeting of political bloggers, the cyberstalking and harassment, are now all now acceptable tactics for which there is evidently no legal discourse. Thanks, Judge Potter.
And Dan Backer notes the following
"The precedent set here is just terrible," Backer said, talking about how Judge Potter ignored Kimberlin's violation of court orders to seal the discovery materials. "Why should anyone comply with discovery?"
Walker is penalized for obeying the Judges rulings while Kimberlin is rewarded for flouting them, sounds like the left on gun control.
As Glenn Reynolds says you get more of behavior you reward, more is coming.
Update 3: Hogewash has a long post, two bits of it:
6. Now that I've had a chance to size up Stacy McCain, I'd like to echo the warning that Glen Reynolds made about not trying to out-crazy Mr. McCain. Team Kimberlin should not expect to be able to divert him from relentlessly blogging about their mischief.
Stacy with a story is like a dog with a bone and this is Stacy's story. He's not going to let it go, as for Mr. Hoge
7. Similarly, they should not expect Hogewash! to let go either. While I consider myself simply to be pigheaded, I have been described as having weapons-grade persistence. I have turned the Vast Hogewash Research Organization loose on Team Kimberlin. The data is rolling in, and, as it becomes useful in bringing the truth to light, it will be published. You can also expect that I won't tell everything as soon as I know it. Somethings need to wait until the time is ripe.
I still don't think this will end well for Kimberlin & Company but regardless one can't forget they have a win in the bank right now and will act accordingly.
Update 4: More from Stacy.


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text:
Many celebrities take time out of their busy schedules throughout the year to do charitable acts. DoSomething.org, the largest not-for-profit in the U.S. for young people and social change, compiles a list every year of the top "20 Celebs Gone Good."
So, who was the most charitable celebrity of 2013?
For the second year in a row, the title of top celebrity do-gooder goes to Taylor Swift!
U.S. & World
The day's top national and international news.
"The country pop star turned her 24th birthday into a gift for others, quietly donating $100,000 of her own money to The Nashville Symphony," the website said of the 24-year-old singer. "Swift also made global headlines when she joined Prince William and Bon Jovi on stage for an impromptu performance of "Livin' On a Prayer" at the Winter White Gala in London to help homeless youth."
PHOTOS: Taylor Swift's famous friends
One Direction made their debut taking the No.2 spot on the list thanks to their volunteer trip to Ghana as part of their efforts with the U.K. charity Comic Relief. Harry Styles and Liam Payne's also worked with a London-based nonprofit called Prizeo for Trekstock.
Beyonce took the No.3 spot, followed by Paul Walker and Macklemore &Ryan Lewis.
Sandra Bullock, Kerry Washington, Ian Somerhalder, Ryan Seacrest and Carrie Underwood rounded out the top 10.


text:
Daily Reading
Fourteenth Sunday in Ordinary Time Gospel MT 11: 25-30
At that time Jesus exclaimed: "I give praise to you, Father, Lord of heaven and earth, for although you have hidden these things from the wise and the learned you have revealed them to little ones. Yes, Father, such has been your gracious will. All things have been handed over to me by my FathRead More
Events in July 2020
- There are no events scheduled during this period.


text:
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Let's Learn
Live food preparation of a soft shelled turtle. Be warned: It involves graphic uncensored killing of a live turtle...
MEGA Download
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The cast try a challenge in which they must use different props for censorship (think of when in movies a fruit bowl etc. covers things up instead of a censor bar/blur).
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--------------> Compilation Thread Link < | >Housei Tumblr <


text:
So I was thinking once again of going back and grinding some old world rep, Alliance Cenarion Circle etc.
Then the thought hit me, when did I stop helping the Humans, Elves, Dwarves and, of course Gnomes?
I hit Outland, I helped them.
I have hit Nortrend, and I am still helping them.
Fat lot of appreciation I get from them though!
It's all new faction this, new faction that.
Surely there is some spillover, maybe 1 in a hundred?
This is particularly frustrating when one is helping those mechanized-Gnomes in Borean Tundra… I mean the Gnomes are in Exile right? So where's the reputation?
Stupid biohazard Gnomes!
Gnomer and Out!


text:
UnNews:Making Money In A Down Economy: Experts Offer Advice
This article is part of UnNews, your source for up-to-the-microsecond misinformation.
12 February 2009
The science of finance is inexact, and financial experts do not often agree on best courses of action - especially in a down economy. But one thing they do seem to agree on is a well-established and recession-proof means of increasing your net worth no matter how Wall Street is faring; sell dubious financial advice to gullible investors.
"Some call it snake oil," said noted financial advisor Cleo Barsook. "But calling it snake oil seems a bit harsh. Snake oil salesmen didn't get degrees in snakonomics." By this statement, he meant that, even though the advice he gives is nowhere near the guarantee of income for you that his own charge for the advice is for him - which is identical to snake oil salesmen's arrangement - his advice, unlike that of his oily counterpart, is at least backed by two years of community college study. He declined to explain what that is actually worth, however.
"The key thing to remember," Barsook explained, "is to always include the disclaimer. That way there is no misunderstanding about the reality of it - that you make more money by telling your customer what he should do than you would make if you yourself did what you are telling him to do. Otherwise why would you be selling it instead of doing it?" When asked to provide details about how to get started implementing this plan, however, Barsook declined, stating that there is an up-front charge for that information.
The idea is taking hold among struggling investors. "I got really tired of my financial advisor making more money off me than his advice was making for me," explained Charles Queem, a local investor. "So I decided I should just do what he does. I bought a book and I'm going to a seminar. Only cost me $2500. Soon, I'll be rolling in it."


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World Bank Blog: Targeting Transparency30/07/2013
The UN has long espoused the promotion of transparency and access to information as core elements of human rights and anticorruption efforts. In 1946, UN Resolution 59(I), adopted in the very first session of the General Assembly declared: "Freedom of information is a fundamental human right and is the touchstone of all the freedoms to which the United Nations is consecrated." Subsequently, the Universal Declaration on Human Rights, and the International Covenant on Civil and Political Rights included the freedom of information as an intrinsic component of the freedom of expression. The UN Convention Against Corruption requires signatories "to take measures to enhance transparency in public administration," And the 2000 Millennium Declaration, the preamble to the 2015 Millennium Development Goals (MDGs) declared the resolve to "ensure freedom of the media to perform their essential role and the right of the public to have access to information."
So, the prominent references to transparency and right to information in the recently released report of the High Level Panel (HLP) of Eminent Experts on the post-2015 Development Agenda is not remarkable or surprising in itself. But both the language – the report calls for a "transparency revolution" and a "data revolution," and the framing – the report proposes that good governance and transparency be included as core targets, suggest that there is an impetus for accelerated efforts in this area.
Any inclusion of transparency in the post-2015 MDGs will only be a logical complement to other global dynamics and the deepening of the information age. On the one hand, whistleblowers, wikileaks, and global media have harnessed the inexorably unshackling power of technology to bring issues of secrecy and transparency to the center of popular consciousness. On the other hand, and perhaps as a reaction to these forces, governments are launching various initiatives to demonstrate their commitment to the principle of transparency.
Recent controversies make it amply clear that there continue to be palpable tensions between disclosure and the right to know on the one hand, and concerns about privacy, national security, and international diplomacy on the other. Nevertheless, more than 60 countries have signed up to the Open Government Partnership, a global multi-stakeholder initiative that commits signatory countries to a range of transparency measures. The recently concluded G-8 summit elevated transparency as a critical global governance issue on par with tax and trade, and issued an Open Data Charter as one of the key documents to emerge from the summit.
But, while the principle itself might have near universal appeal, translating it into concrete, actionable targets that are equally widely accepted is fraught with challenges. Not least because transparency itself is interpreted differently in different contexts – ranging from simply disclosure of information to aspects covering quality, access, and usefulness. Whatever measure is computed will clearly address only one part or another of the puzzle.
The HLP report suggests a guarantee of the public's right to information (RTI) as a basic minimum standard. An RTI law is undoubtedly a good indicator of intent and in principle adherence to the idea of openness, but hardly adequate. More than 90 countries already have RTI laws, but in the face of implementation and context challenges in many countries, such laws have made little difference. There are several global indices that measure the extent of actual disclosure of information in specific areas – budgets,extractive industry revenues, but as has been suggested, a composite indicator of the extent of transparency in a society might be neither feasible, nor useful.
Moreover, a focus on the disclosure of information (and even taking into account issues of quality, accessibility, and usability of information), in abstraction from the broader institutional environment that conditions whether transparency policies can succeed and whether they have an impact will mean little. A broad assumption is embedded across transparency policies – that as information is pumped out into the public space, citizens will be "empowered ……to hold governments and companies to account," as the Declaration issued by the G8 Summit contends.
But in effect, transparency in many areas – including in most of the areas outlined in the G8 Open Data Charter – is useful because the disclosure of information enables specialized oversight agencies to work more effectively by removing a critical constraint, and when complementary institutions provide a credible threat of sanctions when corruption, non-performance, or other wrong-doing is exposed. And although the customary "citizen" is at the center of the rhetoric around transparency, the presence of strong, vocal, and independent civil society and media groups and a political system that provides enough credible alternatives to make the threat of electoral sanctions real, are all indispensable to making transparency policies effective. Moreover, an exclusive focus on transparency might ignore more deeply seated problems, as some civil society groups have argued in the case of the land transparency.
Surprisingly for an idea that uses up so much bandwidth, and embodies so much promise, there is still relatively little research about the kind broader institutional systems and political dynamics necessary for transparency policies to work have an impact – what in jargony terms might be called context or enabling environment. Someefforts to assess the impact of transparency policies are being launched, and can be useful inputs to the design of both transparency policies and transparency indices. Emerging standards and indices on transparency are also starting to take a more comprehensive approach. For instance, International Budget Partnership's latest survey identifies some of the mechanisms for participation by citizens, and assessing the extent to which their feedback is integrated into the policy process. The Open Contracting Principles, developed by the Open Contracting Initiative, include areas related to enabling environment, capacity of stakeholders, and quality of contracting outcomes. Following how these complexities are integrated into global targets will, no doubt, be fascinating.
Author: Anupama Dokeniya
Original source: World Bank Blog
Date: 3 July 2013