Last updated: 2019-09-06

Checks: 6 1

Knit directory: apaQTL/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

The global environment had objects present when the code in the R Markdown file was run. These objects can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment. Use wflow_publish or wflow_build to ensure that the code is always run in an empty environment.

The following objects were defined in the global environment when these results were created:

Name Class Size
data environment 56 bytes
env environment 56 bytes

The command set.seed(20190411) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store
    Ignored:    docs/.DS_Store
    Ignored:    docs/figure/.DS_Store
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  .Rprofile
    Untracked:  ._.DS_Store
    Untracked:  .gitignore
    Untracked:  @
    Untracked:  _workflowr.yml
    Untracked:  analysis/._PASdescriptiveplots.Rmd
    Untracked:  analysis/._cuttoffPercUsage.Rmd
    Untracked:  analysis/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm.allChrom
    Untracked:  analysis/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm.allChrom
    Untracked:  analysis/QTLexampleplots.Rmd
    Untracked:  analysis/cuttoffPercUsage.Rmd
    Untracked:  analysis/eQTLoverlap.Rmd
    Untracked:  analysis/interpret verify bam.Rmd
    Untracked:  analysis/interpret_verifybam.Rmd
    Untracked:  analysis/mergeRNA.Rmd
    Untracked:  analysis/oldstuffNotNeeded.Rmd
    Untracked:  analysis/remove_badlines.Rmd
    Untracked:  analysis/totalspec.Rmd
    Untracked:  apaQTL.Rproj
    Untracked:  code/.NascentRNAdtPlotFirstintronicPAS.sh.swp
    Untracked:  code/._ApaQTL_nominalNonnorm.sh
    Untracked:  code/._BothFracDTPlotGeneRegions.sh
    Untracked:  code/._BothFracDTPlotGeneRegions_normalized.sh
    Untracked:  code/._EandPqtl_perm.sh
    Untracked:  code/._EandPqtls.sh
    Untracked:  code/._FC_NucintornUpandDown.sh
    Untracked:  code/._FC_UTR.sh
    Untracked:  code/._FC_intornUpandDownsteamPAS.sh
    Untracked:  code/._FC_nascentseq.sh
    Untracked:  code/._FC_newPeaks_olddata.sh
    Untracked:  code/._HMMpermuteTotal.py
    Untracked:  code/._HmmPermute.py
    Untracked:  code/._IntronicPASDT.sh
    Untracked:  code/._LC_samplegroups.py
    Untracked:  code/._LD_qtl.sh
    Untracked:  code/._LD_snpsproxy.sh
    Untracked:  code/._NascentRNAdtPlot.sh
    Untracked:  code/._NascentRNAdtPlot3UTRPAS.sh
    Untracked:  code/._NascentRNAdtPlotExcludeFirstintronicPAS.sh
    Untracked:  code/._NascentRNAdtPlotNucPAS.sh
    Untracked:  code/._NascentRNAdtPlotTotPAS.sh
    Untracked:  code/._NascentRNAdtPlotintronicPAS.sh
    Untracked:  code/._NascnetRNAdtPlotPAS.sh
    Untracked:  code/._NetSeq_fourthintronDT.sh
    Untracked:  code/._NomResfromPASSNP.py
    Untracked:  code/._NuclearPAS_5per.bed.py
    Untracked:  code/._PTTfacetboxplots.R
    Untracked:  code/._PrematureQTLNominal.sh
    Untracked:  code/._PrematureQTLPermuted.sh
    Untracked:  code/._QTL2bed.py
    Untracked:  code/._QTL2bed_withstrand.py
    Untracked:  code/._RNAbam2bw.sh
    Untracked:  code/._RNAseqDTplot.sh
    Untracked:  code/._RunRes2PAS.sh
    Untracked:  code/._SAF215upbed.py
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilefiltPAS
    Untracked:  code/._TESplots100bp.sh
    Untracked:  code/._TESplots150bp.sh
    Untracked:  code/._TESplots200bp.sh
    Untracked:  code/._TotalPAS_5perc.bed.py
    Untracked:  code/._Untitled
    Untracked:  code/._ZipandTabPheno.sh
    Untracked:  code/._aAPAqtl_nominal39ind.sh
    Untracked:  code/._annotatePacBioPASregion.sh
    Untracked:  code/._annotatedPAS2bed.py
    Untracked:  code/._apaInPandE.py
    Untracked:  code/._apaQTLCorrectPvalMakeQQ.R
    Untracked:  code/._apaQTLCorrectpval_6or7a.R
    Untracked:  code/._apaQTL_Nominal.sh
    Untracked:  code/._apaQTL_nominalv67.sh
    Untracked:  code/._apaQTL_permuted.sh
    Untracked:  code/._apaQTL_permuted_test6A7A.sh
    Untracked:  code/._apainRibo.py
    Untracked:  code/._assignNucIntonpeak2intronlocs.sh
    Untracked:  code/._assignTotIntronpeak2intronlocs.sh
    Untracked:  code/._bam2BW_5primemost.sh
    Untracked:  code/._bed2saf.py
    Untracked:  code/._bothFracDTplot1stintron.sh
    Untracked:  code/._bothFracDTplot4thintron.sh
    Untracked:  code/._bothFrac_FC.sh
    Untracked:  code/._callPeaksYL.py
    Untracked:  code/._changeRibonomQTLres2genename.py
    Untracked:  code/._changenomQTLres2geneName.py
    Untracked:  code/._chooseAnno2PAS_pacbio.py
    Untracked:  code/._chooseAnno2SAF.py
    Untracked:  code/._chooseSignalSite
    Untracked:  code/._chooseSignalSite.py
    Untracked:  code/._closestannotated.sh
    Untracked:  code/._closestannotated_byfrac.sh
    Untracked:  code/._cluster.json
    Untracked:  code/._clusterPAS.json
    Untracked:  code/._clusterfiltPAS.json
    Untracked:  code/._codingdms2bed.py
    Untracked:  code/._config.yaml
    Untracked:  code/._config2.yaml
    Untracked:  code/._configOLD.yaml
    Untracked:  code/._convertNominal2SNPLOC.py
    Untracked:  code/._convertNominal2SNPloc2Versions.py
    Untracked:  code/._convertNumeric.py
    Untracked:  code/._correctNomeqtl.R
    Untracked:  code/._createPlinkSampfile.py
    Untracked:  code/._dag.pdf
    Untracked:  code/._eQTL_switch2snploc.py
    Untracked:  code/._eQTLgenestestedapa.py
    Untracked:  code/._encodeRNADTplots.sh
    Untracked:  code/._extractGenotypes.py
    Untracked:  code/._extractseqfromqtlfastq.py
    Untracked:  code/._fc2leafphen.py
    Untracked:  code/._fc_filteredPAS6and7As.sh
    Untracked:  code/._fifteenBPupstreamPAS.py
    Untracked:  code/._fiftyBPupstreamPAS.py
    Untracked:  code/._filter5perc.R
    Untracked:  code/._filter5percPheno.py
    Untracked:  code/._filterLDsnps.py
    Untracked:  code/._filterMPPAS.py
    Untracked:  code/._filterMPPAS_15.py
    Untracked:  code/._filterMPPAS_15_7As.py
    Untracked:  code/._filterMPPAS_50.py
    Untracked:  code/._filterSAFforMP.py
    Untracked:  code/._filterpeaks.py
    Untracked:  code/._finalPASbed2SAF.py
    Untracked:  code/._fix4su304corr.py
    Untracked:  code/._fix4su604corr.py
    Untracked:  code/._fix4sukalisto.py
    Untracked:  code/._fixExandUnexeQTL
    Untracked:  code/._fixExandUnexeQTL.py
    Untracked:  code/._fixFChead.py
    Untracked:  code/._fixFChead_bothfrac.py
    Untracked:  code/._fixFChead_short.py
    Untracked:  code/._fixH3k12ac.py
    Untracked:  code/._fixPASregionSNPs.py
    Untracked:  code/._fixRNAhead4corr.py
    Untracked:  code/._fixRNAkalisto.py
    Untracked:  code/._fixgroupedtranscript.py
    Untracked:  code/._fixhead_netseqfc.py
    Untracked:  code/._getAPAfromanyeQTL.py
    Untracked:  code/._getApapval4eqtl.py
    Untracked:  code/._getApapval4eqtl_unexp.py
    Untracked:  code/._getApapval4eqtl_version67.py
    Untracked:  code/._getDownstreamIntronNuclear.py
    Untracked:  code/._getIntronDownstreamPAS.py
    Untracked:  code/._getIntronUpstreamPAS.py
    Untracked:  code/._getQTLalleles.py
    Untracked:  code/._getQTLfastq.sh
    Untracked:  code/._getUpstreamIntronNuclear.py
    Untracked:  code/._grouptranscripts.py
    Untracked:  code/._intersectVCFandupPAS.sh
    Untracked:  code/._keep5perMAF.py
    Untracked:  code/._keepSNP_vcf.sh
    Untracked:  code/._make5percPeakbed.py
    Untracked:  code/._makeFileID.py
    Untracked:  code/._makePheno.py
    Untracked:  code/._makeSAFbothfrac5perc.py
    Untracked:  code/._makeSNP2rsidfile.py
    Untracked:  code/._makeeQTLempirical_unexp.py
    Untracked:  code/._makeeQTLempiricaldist.py
    Untracked:  code/._makegencondeTSSfile.py
    Untracked:  code/._mapSSsnps2PAS.sh
    Untracked:  code/._mergRNABam.sh
    Untracked:  code/._mergeAllBam.sh
    Untracked:  code/._mergeBW_norm.sh
    Untracked:  code/._mergeBamNascent.sh
    Untracked:  code/._mergeByFracBam.sh
    Untracked:  code/._mergePeaks.sh
    Untracked:  code/._mnase1stintron.sh
    Untracked:  code/._mnaseDT_fourthintron.sh
    Untracked:  code/._namePeaks.py
    Untracked:  code/._netseqDTplot1stIntron.sh
    Untracked:  code/._netseqFC.sh
    Untracked:  code/._nucQTLGWAS.py
    Untracked:  code/._nucSpecQTLineData.py
    Untracked:  code/._nucSpeceffectsize.py
    Untracked:  code/._pQTLsotherdata.py
    Untracked:  code/._pacbioDT.sh
    Untracked:  code/._pacbioIntronicDT.sh
    Untracked:  code/._parseBestbamid.py
    Untracked:  code/._peak2PAS.py
    Untracked:  code/._peakFC.sh
    Untracked:  code/._pheno2countonly.R
    Untracked:  code/._phenoQTLfromlist.py
    Untracked:  code/._processYRIgen.py
    Untracked:  code/._pttQTLsinapaQTL.py
    Untracked:  code/._qtlRegionseq.sh
    Untracked:  code/._qtlsPvalOppFrac.py
    Untracked:  code/._quantassign2parsedpeak.py
    Untracked:  code/._removeXfromHmm.py
    Untracked:  code/._removeloc_pheno.py
    Untracked:  code/._riboQTL.sh
    Untracked:  code/._runCorrectNomEqtl.sh
    Untracked:  code/._runHMMpermuteAPAqtls.sh
    Untracked:  code/._runHMMpermuteeQTLS.sh
    Untracked:  code/._runMakeEmpiricaleQTL_unexp.sh
    Untracked:  code/._runMakeeQTLempirical.sh
    Untracked:  code/._run_bam2bw_all3prime.sh
    Untracked:  code/._run_bam2bw_extra3.sh
    Untracked:  code/._run_bestbamid.sj
    Untracked:  code/._run_filtersnpLD.sh
    Untracked:  code/._run_getAPAfromeQTL_version6.7.sh
    Untracked:  code/._run_getApaPval4eqtl.sh
    Untracked:  code/._run_getapafromeQTL.py
    Untracked:  code/._run_getapafromeQTL.sh
    Untracked:  code/._run_getapapval4eqtl_unexp.sh
    Untracked:  code/._run_leafcutterDiffIso.sh
    Untracked:  code/._run_prxySNP.sh
    Untracked:  code/._run_pttfacetboxplot.sh
    Untracked:  code/._run_sepUsagephen.sh
    Untracked:  code/._run_sepgenobychrom.sh
    Untracked:  code/._run_verifybam.sh
    Untracked:  code/._selectNominalPvalues.py
    Untracked:  code/._sepUsagePhen.py
    Untracked:  code/._sepgenobychrom.py
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._sortindexRNAbam.sh
    Untracked:  code/._submit-snakemakePAS.sh
    Untracked:  code/._submit-snakemakefiltPAS.sh
    Untracked:  code/._subsetAPAnotEorPgene.py
    Untracked:  code/._subsetAPAnotEorPgene_2versions.py
    Untracked:  code/._subsetApanoteGene.py
    Untracked:  code/._subsetApanoteGene_2versions.py
    Untracked:  code/._subsetUnexplainedeQTLs.py
    Untracked:  code/._subsetVCF_SS.sh
    Untracked:  code/._subsetVCF_noSSregions.sh
    Untracked:  code/._subsetVCF_upstreamPAS.sh
    Untracked:  code/._subset_diffisopheno.py
    Untracked:  code/._subsetpermAPAwithGenelist.py
    Untracked:  code/._subsetpermAPAwithGenelist_2versions.py
    Untracked:  code/._subsetvcf_otherreg.sh
    Untracked:  code/._subsetvcf_permSS.sh
    Untracked:  code/._subtrachfiveprimeUTR.sh
    Untracked:  code/._subtractExons.sh
    Untracked:  code/._subtractfiveprimeUTR.sh
    Untracked:  code/._tabixSNPS.sh
    Untracked:  code/._tenBPupstreamPAS.py
    Untracked:  code/._testVerifyBam.sh
    Untracked:  code/._totSeceffectsize.py
    Untracked:  code/._twentyBPupstreamPAS.py
    Untracked:  code/._utrdms2saf.py
    Untracked:  code/._vcf2bed.py
    Untracked:  code/._verifyBam18517N.sh
    Untracked:  code/._verifyBam18517T.sh
    Untracked:  code/._verifyBam19128N.sh
    Untracked:  code/._verifyBam19128T.sh
    Untracked:  code/._wrap_verifybam.sh
    Untracked:  code/._writePTTexamplecode.py
    Untracked:  code/._writePTTexamplecode.sh
    Untracked:  code/.pversion
    Untracked:  code/.snakemake/
    Untracked:  code/1
    Untracked:  code/APAqtl_nominal.err
    Untracked:  code/APAqtl_nominal.out
    Untracked:  code/APAqtl_nominal_39.err
    Untracked:  code/APAqtl_nominal_39.out
    Untracked:  code/APAqtl_nominal_nonNorm.err
    Untracked:  code/APAqtl_nominal_nonNorm.out
    Untracked:  code/APAqtl_nominal_versions67.err
    Untracked:  code/APAqtl_nominal_versions67.out
    Untracked:  code/APAqtl_permuted.err
    Untracked:  code/APAqtl_permuted.out
    Untracked:  code/APAqtl_permuted_versions67.err
    Untracked:  code/APAqtl_permuted_versions67.out
    Untracked:  code/ApaQTL_nominalNonnorm.sh
    Untracked:  code/BothFracDTPlot1stintron.err
    Untracked:  code/BothFracDTPlot1stintron.out
    Untracked:  code/BothFracDTPlot4stintron.err
    Untracked:  code/BothFracDTPlot4stintron.out
    Untracked:  code/BothFracDTPlotGeneRegions.err
    Untracked:  code/BothFracDTPlotGeneRegions.out
    Untracked:  code/BothFracDTPlotGeneRegions_norm.err
    Untracked:  code/BothFracDTPlotGeneRegions_norm.out
    Untracked:  code/BothFracDTPlotGeneRegions_normalized.sh
    Untracked:  code/DistPAS2Sig.py
    Untracked:  code/EandPqtl.err
    Untracked:  code/EandPqtl.out
    Untracked:  code/EandPqtl_perm.sh
    Untracked:  code/EandPqtls.sh
    Untracked:  code/EncodeRNADTPlotGeneRegions.err
    Untracked:  code/EncodeRNADTPlotGeneRegions.out
    Untracked:  code/FC_NucintornUpandDown.sh
    Untracked:  code/FC_NucintronPASupandDown.err
    Untracked:  code/FC_NucintronPASupandDown.out
    Untracked:  code/FC_UTR.err
    Untracked:  code/FC_UTR.out
    Untracked:  code/FC_UTR.sh
    Untracked:  code/FC_intornUpandDownsteamPAS.sh
    Untracked:  code/FC_intronPASupandDown.err
    Untracked:  code/FC_intronPASupandDown.out
    Untracked:  code/FC_nascent.err
    Untracked:  code/FC_nascentout
    Untracked:  code/FC_nascentseq.sh
    Untracked:  code/FC_newPAS_olddata.err
    Untracked:  code/FC_newPAS_olddata.out
    Untracked:  code/FC_newPeaks_olddata.sh
    Untracked:  code/HMMpermuteTotal.py
    Untracked:  code/HmmPermute.p
    Untracked:  code/HmmPermute.py
    Untracked:  code/IntronicPASDT.err
    Untracked:  code/IntronicPASDT.out
    Untracked:  code/IntronicPASDT.sh
    Untracked:  code/LC_samplegroups.py
    Untracked:  code/LD_qtl.sh
    Untracked:  code/LD_snpsproxy.sh
    Untracked:  code/LD_vcftools.hap.out
    Untracked:  code/NascentDTPlotGeneRegions.err
    Untracked:  code/NascentDTPlotGeneRegions.out
    Untracked:  code/NascentDTPlotPAS.err
    Untracked:  code/NascentDTPlotPAS.out
    Untracked:  code/NascentDTPlotPAS_3utr.err
    Untracked:  code/NascentDTPlotPAS_3utr.out
    Untracked:  code/NascentDTPlotPAS_firstintron.err
    Untracked:  code/NascentDTPlotPAS_firstintron.out
    Untracked:  code/NascentDTPlotPAS_intron.err
    Untracked:  code/NascentDTPlotPAS_intron.out
    Untracked:  code/NascentDTPlotPAS_nuc.err
    Untracked:  code/NascentDTPlotPAS_nuc.out
    Untracked:  code/NascentDTPlotPAS_tot.err
    Untracked:  code/NascentDTPlotPAS_tot.out
    Untracked:  code/NascentRNAdtPlot.sh
    Untracked:  code/NascentRNAdtPlot3UTRPAS.sh
    Untracked:  code/NascentRNAdtPlotExcludeFirstintronicPAS.sh
    Untracked:  code/NascentRNAdtPlotFirstintronicPAS.sh
    Untracked:  code/NascentRNAdtPlotNucPAS.sh
    Untracked:  code/NascentRNAdtPlotTotPAS.sh
    Untracked:  code/NascentRNAdtPlotintronicPAS.sh
    Untracked:  code/NascnetRNAdtPlotPAS.sh
    Untracked:  code/NetSeq_fourthintronDT.sh
    Untracked:  code/NomResfromPASSNP.py
    Untracked:  code/NuclearPAS_5per.bed.py
    Untracked:  code/Nuclear_example.err
    Untracked:  code/Nuclear_example.out
    Untracked:  code/PACbioDT.err
    Untracked:  code/PACbioDT.out
    Untracked:  code/PACbioDTitronic.err
    Untracked:  code/PACbioDTitronic.out
    Untracked:  code/PTTfacetboxplots.R
    Untracked:  code/PrematureQTLNominal.sh
    Untracked:  code/PrematureQTLPermuted.sh
    Untracked:  code/Prematureqtl_nominal.err
    Untracked:  code/Prematureqtl_nominal.out
    Untracked:  code/Prematureqtl_permuted.err
    Untracked:  code/Prematureqtl_permuted.out
    Untracked:  code/QTL2bed.py
    Untracked:  code/QTL2bed_withstrand.py
    Untracked:  code/README.md
    Untracked:  code/RNABam2BW.err
    Untracked:  code/RNABam2BW.out
    Untracked:  code/RNAbam2bw.sh
    Untracked:  code/RNAseqDTPlotGeneRegions.err
    Untracked:  code/RNAseqDTPlotGeneRegions.out
    Untracked:  code/RNAseqDTplot.sh
    Untracked:  code/Rplots.pdf
    Untracked:  code/RunRes2PAS.sh
    Untracked:  code/SAF215upbed.py
    Untracked:  code/SAF215upbed_gen.py
    Untracked:  code/Script4NuclearPTTqtlexamples.sh
    Untracked:  code/Script4NuclearQTLexamples.sh
    Untracked:  code/Script4TotalPTTqtlexamples.sh
    Untracked:  code/Script4TotalQTLexamples.sh
    Untracked:  code/TESplots100bp.err
    Untracked:  code/TESplots100bp.out
    Untracked:  code/TESplots100bp.sh
    Untracked:  code/TESplots150bp.err
    Untracked:  code/TESplots150bp.out
    Untracked:  code/TESplots150bp.sh
    Untracked:  code/TESplots200bp.err
    Untracked:  code/TESplots200bp.out
    Untracked:  code/TESplots200bp.sh
    Untracked:  code/TotalPAS_5perc.bed.py
    Untracked:  code/Total_example.err
    Untracked:  code/Total_example.out
    Untracked:  code/Untitled
    Untracked:  code/Upstream100Bases_general.py
    Untracked:  code/YRI_LCL.vcf.gz
    Untracked:  code/YRI_LCL_chr1.vcf.gz.log
    Untracked:  code/YRI_LCL_chr1.vcf.gz.recode.vcf
    Untracked:  code/ZipandTabPheno.sh
    Untracked:  code/aAPAqtl_nominal39ind.sh
    Untracked:  code/annotatePacBioPASregion.sh
    Untracked:  code/annotatedPAS2bed.py
    Untracked:  code/annotatedPASregion.err
    Untracked:  code/annotatedPASregion.out
    Untracked:  code/apaInPandE.py
    Untracked:  code/apaQTLCorrectPvalMakeQQ_4pc.R
    Untracked:  code/apaQTLCorrectpval_6or7a.R
    Untracked:  code/apaQTL_Nominal_4pc.sh
    Untracked:  code/apaQTL_nominalv67.sh
    Untracked:  code/apaQTL_permuted.4pc.sh
    Untracked:  code/apaQTL_permuted_test6A7A.sh
    Untracked:  code/apafacetboxplots.R
    Untracked:  code/apainRibo.py
    Untracked:  code/apaqtlfacetboxplots.R
    Untracked:  code/assignNucIntonpeak2intronlocs.sh
    Untracked:  code/assignPeak2Intronicregion.err
    Untracked:  code/assignPeak2Intronicregion.out
    Untracked:  code/assignTotIntronpeak2intronlocs.sh
    Untracked:  code/assigntotPeak2Intronicregion.err
    Untracked:  code/assigntotPeak2Intronicregion.out
    Untracked:  code/bam2BW_5primemost.sh
    Untracked:  code/bam2bw.err
    Untracked:  code/bam2bw.out
    Untracked:  code/bam2bw_5primemost.err
    Untracked:  code/bam2bw_5primemost.out
    Untracked:  code/binary_fileset.log
    Untracked:  code/bothFracDTplot1stintron.sh
    Untracked:  code/bothFracDTplot4thintron.sh
    Untracked:  code/bothFrac_FC.err
    Untracked:  code/bothFrac_FC.out
    Untracked:  code/bothFrac_FC.sh
    Untracked:  code/callSHscripts.txt
    Untracked:  code/changePermQTLres2geneName.py
    Untracked:  code/changeRibonomQTLres2genename.py
    Untracked:  code/changenomQTLres2geneName.py
    Untracked:  code/chooseAnno2PAS_pacbio.py
    Untracked:  code/closestannotated.err
    Untracked:  code/closestannotated.out
    Untracked:  code/closestannotated.sh
    Untracked:  code/closestannotated_byfrac.sh
    Untracked:  code/closestannotatedbyfrac.err
    Untracked:  code/closestannotatedbyfrac.out
    Untracked:  code/codingdms2bed.py
    Untracked:  code/convertNominal2SNPLOC.py
    Untracked:  code/convertNominal2SNPloc2Versions.py
    Untracked:  code/correctNomeqtl.R
    Untracked:  code/createPlinkSampfile.py
    Untracked:  code/dag.pdf
    Untracked:  code/dagPAS.pdf
    Untracked:  code/dagfiltPAS.pdf
    Untracked:  code/eQTL_switch2snploc.py
    Untracked:  code/eQTLgenestestedapa.py
    Untracked:  code/encodeRNADTplots.sh
    Untracked:  code/environmentLeaf.yaml
    Untracked:  code/extractGenotypes.py
    Untracked:  code/extractseqfromqtlfastq.py
    Untracked:  code/fc2leafphen.py
    Untracked:  code/fc_filteredPAS6and7As.sh
    Untracked:  code/fifteenBPupstreamPAS.py
    Untracked:  code/fiftyBPupstreamPAS.py
    Untracked:  code/filterLDsnps.py
    Untracked:  code/filterMPPAS.py
    Untracked:  code/filterMPPAS_15.py
    Untracked:  code/filterMPPAS_15_7As.py
    Untracked:  code/filterMPPAS_50.py
    Untracked:  code/filterSAFforMP.py
    Untracked:  code/filterSAFforMP_gen.py
    Untracked:  code/finalPASbed2SAF.py
    Untracked:  code/findbuginpeaks.R
    Untracked:  code/fix4su304corr.py
    Untracked:  code/fix4su604corr.py
    Untracked:  code/fix4sukalisto.py
    Untracked:  code/fixExandUnexeQTL
    Untracked:  code/fixExandUnexeQTL.py
    Untracked:  code/fixFChead_bothfrac.py
    Untracked:  code/fixFChead_short.py
    Untracked:  code/fixFChead_summary.py
    Untracked:  code/fixH3k12ac.py
    Untracked:  code/fixPASregionSNPs.py
    Untracked:  code/fixRNAhead4corr.py
    Untracked:  code/fixRNAkalisto.py
    Untracked:  code/fixgroupedtranscript.py
    Untracked:  code/fixhead_netseqfc.py
    Untracked:  code/genotypesYRI.gen.proc.keep.vcf.log
    Untracked:  code/genotypesYRI.gen.proc.keep.vcf.recode.vcf
    Untracked:  code/get100upPAS.py
    Untracked:  code/getAPAfromanyeQTL.py
    Untracked:  code/getApapval4eqtl.py
    Untracked:  code/getApapval4eqtl_unexp.py
    Untracked:  code/getApapval4eqtl_version67.py
    Untracked:  code/getDownstreamIntronNuclear.py
    Untracked:  code/getIntronDownstreamPAS.py
    Untracked:  code/getIntronUpstreamPAS.py
    Untracked:  code/getQTLalleles.py
    Untracked:  code/getQTLfastq.sh
    Untracked:  code/getSeq100up.sh
    Untracked:  code/getUpstreamIntronNuclear.py
    Untracked:  code/getseq100up.err
    Untracked:  code/getseq100up.out
    Untracked:  code/grouptranscripts.err
    Untracked:  code/grouptranscripts.out
    Untracked:  code/grouptranscripts.py
    Untracked:  code/intersectPAS_ssSNPS.err
    Untracked:  code/intersectPAS_ssSNPS.out
    Untracked:  code/intersectVCFPAS.err
    Untracked:  code/intersectVCFPAS.out
    Untracked:  code/intersectVCFandupPAS.sh
    Untracked:  code/keep5perMAF.py
    Untracked:  code/keepSNP_vcf.sh
    Untracked:  code/log/
    Untracked:  code/makeSAFbothfrac5perc.py
    Untracked:  code/makeSNP2rsidfile.py
    Untracked:  code/makeeQTLempirical_unexp.py
    Untracked:  code/makeeQTLempiricaldist.py
    Untracked:  code/makegencondeTSSfile.py
    Untracked:  code/mapSSsnps2PAS.sh
    Untracked:  code/mergRNABam.sh
    Untracked:  code/mergeBW_norm.sh
    Untracked:  code/mergeBWnorm.err
    Untracked:  code/mergeBWnorm.out
    Untracked:  code/mergeBamNacent.err
    Untracked:  code/mergeBamNacent.out
    Untracked:  code/mergeBamNascent.sh
    Untracked:  code/mergeRNAbam.err
    Untracked:  code/mergeRNAbam.out
    Untracked:  code/mnase1stintron.sh
    Untracked:  code/mnaseDTPlot1stintron.err
    Untracked:  code/mnaseDTPlot1stintron.out
    Untracked:  code/mnaseDTPlot4thintron.err
    Untracked:  code/mnaseDTPlot4thintron.out
    Untracked:  code/mnaseDT_fourthintron.sh
    Untracked:  code/netDTPlot4thintron.out
    Untracked:  code/netseqDTplot1stIntron.sh
    Untracked:  code/netseqFC.err
    Untracked:  code/netseqFC.out
    Untracked:  code/netseqFC.sh
    Untracked:  code/neyDTPlot4thintron.err
    Untracked:  code/nucQTLGWAS.py
    Untracked:  code/nucQTLGWAS_withLD.py
    Untracked:  code/nucSpecQTLineData.py
    Untracked:  code/nucSpeceffectsize.py
    Untracked:  code/pQTLsotherdata.py
    Untracked:  code/pacbioDT.sh
    Untracked:  code/pacbioIntronicDT.sh
    Untracked:  code/parseBestbamid.py
    Untracked:  code/phenoQTLfromlist.py
    Untracked:  code/plink.log
    Untracked:  code/processYRIgen.py
    Untracked:  code/prxySNP.err
    Untracked:  code/prxySNP.out
    Untracked:  code/pttFacetBoxplots.err
    Untracked:  code/pttFacetBoxplots.out
    Untracked:  code/pttQTLsinapaQTL.py
    Untracked:  code/pullTwoMechData.py
    Untracked:  code/qtlFacetBoxplots.err
    Untracked:  code/qtlFacetBoxplots.out
    Untracked:  code/qtlRegionseq.sh
    Untracked:  code/qtlsPvalOppFrac.py
    Untracked:  code/rLD_vcftools.hap.err
    Untracked:  code/removeXfromHmm.py
    Untracked:  code/removeloc_pheno.py
    Untracked:  code/riboQTL.sh
    Untracked:  code/riboqtl.err
    Untracked:  code/riboqtl.out
    Untracked:  code/runBestBamID.err
    Untracked:  code/runCorrectNomEqtl.sh
    Untracked:  code/runCorrectNomeqtl.err
    Untracked:  code/runCorrectNomeqtl.out
    Untracked:  code/runFilterLD.err
    Untracked:  code/runFilterLD.out
    Untracked:  code/runHMMpermute.err
    Untracked:  code/runHMMpermute.out
    Untracked:  code/runHMMpermuteAPAqtls.sh
    Untracked:  code/runHMMpermuteeQTLS.sh
    Untracked:  code/runHMMpermuteeQTLs.err
    Untracked:  code/runHMMpermuteeQTLs.out
    Untracked:  code/runMakeEmpiricaleQTL_unexp.sh
    Untracked:  code/runMakeEmpiricaleQTLs.err
    Untracked:  code/runMakeEmpiricaleQTLs.out
    Untracked:  code/runMakeEmpiricaleQTLsunex.err
    Untracked:  code/runMakeEmpiricaleQTLsunex.out
    Untracked:  code/runMakeeQTLempirical.sh
    Untracked:  code/run_DistPAS2Sig.err
    Untracked:  code/run_DistPAS2Sig.out
    Untracked:  code/run_bam2bw.err
    Untracked:  code/run_bam2bw.out
    Untracked:  code/run_bam2bw_all3prime.sh
    Untracked:  code/run_bam2bw_extra3.sh
    Untracked:  code/run_bam2bwexta.err
    Untracked:  code/run_bam2bwexta.out
    Untracked:  code/run_bestbamid.sh
    Untracked:  code/run_distPAS2Sig.sh
    Untracked:  code/run_filtersnpLD.sh
    Untracked:  code/run_getAPAfromanyeQTL.err
    Untracked:  code/run_getAPAfromanyeQTL.out
    Untracked:  code/run_getAPAfromeQTL_version6.7.sh
    Untracked:  code/run_getApaPval4eQTLs.err
    Untracked:  code/run_getApaPval4eQTLs.out
    Untracked:  code/run_getApaPval4eQTLsunexplained.err
    Untracked:  code/run_getApaPval4eQTLsunexplained.out
    Untracked:  code/run_getApaPval4eqtl.sh
    Untracked:  code/run_getapafromeQTL.sh
    Untracked:  code/run_getapapval4eqtl_unexp.sh
    Untracked:  code/run_leafcutterDiffIso.sh
    Untracked:  code/run_leafcutter_ds.err
    Untracked:  code/run_leafcutter_ds.out
    Untracked:  code/run_prxySNP.sh
    Untracked:  code/run_pttfacetboxplot.sh
    Untracked:  code/run_qtlFacetBoxplots.sh
    Untracked:  code/run_sepUsagephen.sh
    Untracked:  code/run_sepgenobychrom.err
    Untracked:  code/run_sepgenobychrom.out
    Untracked:  code/run_sepgenobychrom.sh
    Untracked:  code/run_sepusage.err
    Untracked:  code/run_sepusage.out
    Untracked:  code/run_verifybam.err
    Untracked:  code/run_verifybam.out
    Untracked:  code/run_verifybam.sh
    Untracked:  code/run_verifybam128N.err
    Untracked:  code/run_verifybam128N.out
    Untracked:  code/run_verifybam128T.err
    Untracked:  code/run_verifybam128T.out
    Untracked:  code/run_verifybam517N.err
    Untracked:  code/run_verifybam517N.out
    Untracked:  code/run_verifybam517T.err
    Untracked:  code/run_verifybam517T.out
    Untracked:  code/run_verifybam_fullVCF.sh
    Untracked:  code/runprxySNP.err
    Untracked:  code/runprxySNP.out
    Untracked:  code/runres2pas.err
    Untracked:  code/runres2pas.out
    Untracked:  code/selectNominalPvalues.py
    Untracked:  code/sepUsagePhen.py
    Untracked:  code/sepgenobychrom.py
    Untracked:  code/seqQTLfastq.err
    Untracked:  code/seqQTLfastq.out
    Untracked:  code/seqQTLregion.err
    Untracked:  code/seqQTLregion.out
    Untracked:  code/snakePASlog.out
    Untracked:  code/snakefiltPASlog.out
    Untracked:  code/sortindexRNABam.err
    Untracked:  code/sortindexRNABam.out
    Untracked:  code/sortindexRNAbam.sh
    Untracked:  code/subsetAPAnotEorPgene.py
    Untracked:  code/subsetAPAnotEorPgene_2versions.py
    Untracked:  code/subsetApanoteGene.py
    Untracked:  code/subsetApanoteGene_2versions.py
    Untracked:  code/subsetUnexplainedeQTLs.py
    Untracked:  code/subsetVCF_SS.sh
    Untracked:  code/subsetVCF_noSSregions.sh
    Untracked:  code/subsetVCF_upstreamPAS.sh
    Untracked:  code/subset_diffisopheno.py
    Untracked:  code/subsetpermAPAwithGenelist.py
    Untracked:  code/subsetpermAPAwithGenelist_2versions.py
    Untracked:  code/subsetvcf_SS.err
    Untracked:  code/subsetvcf_SS.out
    Untracked:  code/subsetvcf_noSS.err
    Untracked:  code/subsetvcf_noSS.out
    Untracked:  code/subsetvcf_otherreg.sh
    Untracked:  code/subsetvcf_pas.err
    Untracked:  code/subsetvcf_pas.out
    Untracked:  code/subsetvcf_perm.err
    Untracked:  code/subsetvcf_perm.out
    Untracked:  code/subsetvcf_permSS.sh
    Untracked:  code/subsetvcf_rand.err
    Untracked:  code/subsetvcf_rand.out
    Untracked:  code/subtract5UTR.err
    Untracked:  code/subtract5UTR.out
    Untracked:  code/subtractExons.err
    Untracked:  code/subtractExons.out
    Untracked:  code/subtractExons.sh
    Untracked:  code/subtractfiveprimeUTR.sh
    Untracked:  code/tabixSNPS.sh
    Untracked:  code/tabixSNPs.err
    Untracked:  code/tabixSNPs.out
    Untracked:  code/tenBPupstreamPAS.py
    Untracked:  code/testVerifyBam.sh
    Untracked:  code/test_verifybam.err
    Untracked:  code/test_verifybam.out
    Untracked:  code/totSeceffectsize.py
    Untracked:  code/transcriptdm2bed.py
    Untracked:  code/twentyBPupstreamPAS.py
    Untracked:  code/utrdms2saf.py
    Untracked:  code/vcf2bed.py
    Untracked:  code/vcf_keepsnps.err
    Untracked:  code/vcf_keepsnps.out
    Untracked:  code/verifyBam18517N.sh
    Untracked:  code/verifyBam18517T.sh
    Untracked:  code/verifyBam19128N.sh
    Untracked:  code/verifyBam19128T.sh
    Untracked:  code/wrap_verifybam.err
    Untracked:  code/wrap_verifybam.out
    Untracked:  code/wrap_verifybam.sh
    Untracked:  code/wrap_verifybam_full.sh
    Untracked:  code/writeExampleQTLcode.py
    Untracked:  code/writePTTexamplecode.py
    Untracked:  code/zipandtabPhen.err
    Untracked:  code/zipandtabPhen.out
    Untracked:  data/._.DS_Store
    Untracked:  data/._MetaDataSequencing.txt
    Untracked:  data/AnnotatedPAS/
    Untracked:  data/ApaByEgene/
    Untracked:  data/ApaByPgene/
    Untracked:  data/BadLines/
    Untracked:  data/Battle_pQTL/
    Untracked:  data/CheckSums/
    Untracked:  data/CompareOldandNew/
    Untracked:  data/DTmatrix/
    Untracked:  data/DiffIso/
    Untracked:  data/EncodeRNA/
    Untracked:  data/ExampleQTLPlots/
    Untracked:  data/FlaggedPAS/
    Untracked:  data/GWAS_overlap/
    Untracked:  data/GeuvadisRNA/
    Untracked:  data/HMMqtls/
    Untracked:  data/Li_eQTLs/
    Untracked:  data/NascentRNA/
    Untracked:  data/NucSpeceQTLeffect/
    Untracked:  data/PAS/
    Untracked:  data/PAS_postFlag/
    Untracked:  data/PolyA_DB/
    Untracked:  data/PreTerm_pheno/
    Untracked:  data/PrematureQTLNominal/
    Untracked:  data/PrematureQTLPermuted/
    Untracked:  data/QTLGenotypes/
    Untracked:  data/QTLoverlap/
    Untracked:  data/QTLoverlap_nonNorm/
    Untracked:  data/README.md
    Untracked:  data/RNAseq/
    Untracked:  data/Reads2UTR/
    Untracked:  data/SNPinSS/
    Untracked:  data/SignalSiteFiles/
    Untracked:  data/TF_motifdisruption/
    Untracked:  data/ThirtyNineIndQtl_nominal/
    Untracked:  data/Version15bp6As/
    Untracked:  data/Version15bp7As/
    Untracked:  data/apaQTLNominal/
    Untracked:  data/apaQTLNominal_4pc/
    Untracked:  data/apaQTLPermuted/
    Untracked:  data/apaQTLPermuted_4pc/
    Untracked:  data/apaQTLs/
    Untracked:  data/assignedPeaks/
    Untracked:  data/assignedPeaks_15Up/
    Untracked:  data/bam/
    Untracked:  data/bam_clean/
    Untracked:  data/bam_waspfilt/
    Untracked:  data/bed_10up/
    Untracked:  data/bed_clean/
    Untracked:  data/bed_clean_sort/
    Untracked:  data/bed_waspfilter/
    Untracked:  data/bedsort_waspfilter/
    Untracked:  data/bothFrac_FC/
    Untracked:  data/bw/
    Untracked:  data/bw_norm/
    Untracked:  data/eQTLs/
    Untracked:  data/exampleQTLs/
    Untracked:  data/fastq/
    Untracked:  data/filterPeaks/
    Untracked:  data/fourSU/
    Untracked:  data/h3k27ac/
    Untracked:  data/highdiffsiggenes.txt
    Untracked:  data/inclusivePeaks/
    Untracked:  data/inclusivePeaks_FC/
    Untracked:  data/intronRNAratio/
    Untracked:  data/intron_analysis/
    Untracked:  data/locusZoom/
    Untracked:  data/mergedBG/
    Untracked:  data/mergedBW_byfrac/
    Untracked:  data/mergedBW_norm/
    Untracked:  data/mergedBam/
    Untracked:  data/mergedbyFracBam/
    Untracked:  data/molPhenos/
    Untracked:  data/molQTLs/
    Untracked:  data/motifdistrupt/
    Untracked:  data/netseq/
    Untracked:  data/nonNorm_pheno/
    Untracked:  data/nuc_10up/
    Untracked:  data/nuc_10upclean/
    Untracked:  data/oldPASfiles/
    Untracked:  data/overlapeQTL_try2/
    Untracked:  data/overlapeQTLs/
    Untracked:  data/pQTLoverlap/
    Untracked:  data/pacbio/
    Untracked:  data/peakCoverage/
    Untracked:  data/peaks_5perc/
    Untracked:  data/phenotype/
    Untracked:  data/phenotype_5perc/
    Untracked:  data/pttQTL/
    Untracked:  data/pttQTLplots/
    Untracked:  data/sigDiffGenes.txt
    Untracked:  data/sort/
    Untracked:  data/sort_clean/
    Untracked:  data/sort_waspfilter/
    Untracked:  data/twoMech/
    Untracked:  data/verifyBAM/
    Untracked:  data/verifyBAM_full/
    Untracked:  docs/._.DS_Store
    Untracked:  docs/figure/._.DS_Store
    Untracked:  nohup.out
    Untracked:  output/._.DS_Store
    Untracked:  output/._meanCorrelationPhenotypes.svg
    Untracked:  output/dtPlots/
    Untracked:  output/fastqc/
    Untracked:  output/meanCorrelationPhenotypes.svg
    Untracked:  run_verifybam517N.err
    Untracked:  run_verifybam517N.out

Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Deleted:    reads_graphs.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html 416f8e8 brimittleman 2019-06-27 Build site.
Rmd b8e6035 brimittleman 2019-06-27 add sig
html 77700ac brimittleman 2019-06-25 Build site.
Rmd 1aa270e brimittleman 2019-06-25 change order and names
html 11a3069 brimittleman 2019-06-25 Build site.
Rmd fffb14b brimittleman 2019-06-25 add pvalue and effect size
html 9011f4b brimittleman 2019-06-25 Build site.
Rmd 1683d57 brimittleman 2019-06-25 add snp write out
html e767391 brimittleman 2019-06-25 Build site.
Rmd 45839ab brimittleman 2019-06-25 results from snp in each loc
html 433092b brimittleman 2019-06-24 Build site.
Rmd 78dd5da brimittleman 2019-06-24 add snp in ss analysis

In this analysis I want to ask if snps in a signal site are more likely to be apaQTLs than other snps close to the PAS. In order to do this i need to subset to the pas that have signal site (identified here) I will then identyify the region 50 bp upstream of the PAS and ask if there are snps in this region using the vcf files for the snps i tested.

library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract
mkdir ../data/SNPinSS

I want a bed file with 50bp upstream of these PAS.

PASwSS=read.table("../data/PAS/PASwSignalSite.txt", header = T,stringsAsFactors = F)
PAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, header = F, col.names = c("chr", "start", "end", "PASid","score", "strand")) %>% separate(PASid, into=c("pasNum", "geneiD"), sep=":") %>% mutate(PAS=paste("peak", pasNum, sep=""),PASname=paste(PAS, geneiD, sep="_")) 

PASwSSregion=PASwSS %>% inner_join(PAS, by="PAS") %>% mutate(newEnd=ifelse(strand=="+", end+50, end),newStart=ifelse(strand=="+", start, start-50)) %>% select(chr, newStart,newEnd, PASname, score, strand)

write.table(PASwSSregion,"../data/SNPinSS/FiftyupstreamPASwSS.bed", col.names = F, row.names = F, quote = F, sep="\t")

sort -k1,1 -k2,2n ../data/SNPinSS/FiftyupstreamPASwSS.bed > ../data/SNPinSS/FiftyupstreamPASwSS.sortbed
sbatch subsetVCF_upstreamPAS.sh

cat  ../data/SNPinSS/SNPSinFiftyupstreamPAS_chr* >../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.recode.vcf

I want to further subset to those in a signal site.

SSregions=PASwSS %>% inner_join(PAS, by="PAS") %>% mutate(absdist=abs(UpstreamDist),newEnd= ifelse(strand=="+", end-absdist, end+absdist), newStart=ifelse(strand=="+", end- (absdist+6), end + (absdist-6)), length=newEnd-newStart) %>% select(chr, newStart,newEnd, PASname, score, strand) 


write.table(SSregions,"../data/SNPinSS/SignalSiteRegions.bed", col.names = F, row.names = F, quote = F, sep="\t")
../data/SNPinSS/SSregions_Allchr.sort.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SignalSiteRegions.bed > ../data/SNPinSS/SignalSiteRegions.sort.bed
sbatch subsetVCF_SS.sh

cat  ../data/SNPinSS/SSregions_chr* > ../data/SNPinSS/SSregions_Allchr.recode.vcf

I will also need a different region to comare to. I can just shift these regions upstream by 7

SS_diffregion=SSregions %>% mutate(randStart=ifelse(strand=="+", newStart-7, newEnd), randend=ifelse(strand=="+", newStart, newEnd+7), length=randend-randStart) %>% select(chr, randStart,randend, PASname, score, strand) 

write.table(SS_diffregion,"../data/SNPinSS/OtherSSRegions.bed", col.names = F, row.names = F, quote = F, sep="\t")

sort -k1,1 -k2,2n ../data/SNPinSS/OtherSSRegions.bed  > ../data/SNPinSS/OtherSSRegions.sort.bed 
sbatch subsetvcf_otherreg.sh 

cat  ../data/SNPinSS/Otherregions_chr* > ../data/SNPinSS/Otherregions_Allchr.recode.vcf

Alternative option, permute the distances:

permdist=sample(PASwSS$UpstreamDist, length(PASwSS$UpstreamDist), replace = F)

SSregions_perm=as.data.frame(cbind(PASwSS,  permdist))%>% inner_join(PAS, by="PAS")  %>% mutate(absdist=abs(permdist),newEnd= ifelse(strand=="+", end-absdist, end+absdist), newStart=ifelse(strand=="+", end- (absdist+6), end + (absdist-6)), length=newEnd-newStart)%>% select(chr, newStart,newEnd, PASname, score, strand)

write.table(SSregions_perm,"../data/SNPinSS/SSRegions_permuted.bed", col.names = F, row.names = F, quote = F, sep="\t")

sort -k1,1 -k2,2n ../data/SNPinSS/SSRegions_permuted.bed > ../data/SNPinSS/SSRegions_permuted.sort.bed
sbatch subsetvcf_permSS.sh 

cat  ../data/SNPinSS/SSRegionsPerm_chr* > ../data/SNPinSS/SSRegionsPerm_Allchr.recode.vcf
#remove # in first line

Pull in QTL snps:

totQTLs=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt",header = T, stringsAsFactors = F) %>% select(sid) %>% unique()
write.table(totQTLs,"../data/apaQTLs/TotalQTLSNPsRSID.txt", col.names = F, row.names = F, quote = F)

nucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt",header = T, stringsAsFactors = F) %>% select(sid) %>% unique()
write.table(nucQTLs,"../data/apaQTLs/NuclearQTLSNPsRSID.txt", col.names = F, row.names = F, quote = F)

Signal site results:

SS_snps=read.table("../data/SNPinSS/SSregions_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))


permutedSS_snps=read.table("../data/SNPinSS/SSRegionsPerm_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))

otherReg_snp=read.table("../data/SNPinSS/Otherregions_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))

fiftybp_snp=read.table("../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.recode.vcf",header = T, stringsAsFactors = F) %>% select(ID) %>% mutate(totQTL=ifelse(ID %in% totQTLs$sid, "Yes", "No"), nucQTL=ifelse(ID %in% nucQTLs$sid, "Yes", "No"))

There are only 2 qtl snps in these signal sites. This is not enough to draw anything from this.

Try with pvalues. Are the snps in pvals more likely to be significant than those not.

I need to figure out which peak is associated with each snp.

I can make a bedfile from the SS snps in python and overlap this with the Signal site regions.

python vcf2bed.py ../data/SNPinSS/SSregions_Allchr.recode.vcf  ../data/SNPinSS/SSregions_Allchr.bed

sort -k1,1 -k2,2n ../data/SNPinSS/SSregions_Allchr.bed > ../data/SNPinSS/SSregions_Allchr.sort.bed


python vcf2bed.py ../data/SNPinSS/SSRegionsPerm_Allchr.recode.vcf  ../data/SNPinSS/SSRegionsPerm_Allchr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SSRegionsPerm_Allchr.bed> ../data/SNPinSS/SSRegionsPerm_Allchr.sort.bed



python vcf2bed.py ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.recode.vcf  ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.bed > ../data/SNPinSS/SNPSinFiftyupstreamPAS_Allchr.sort.bed


python vcf2bed.py ../data/SNPinSS/Otherregions_Allchr.recode.vcf  ../data/SNPinSS/Otherregions_Allchr.bed
sort -k1,1 -k2,2n ../data/SNPinSS/Otherregions_Allchr.bed > ../data/SNPinSS/Otherregions_Allchr.sort.bed


sort -k1,1 -k2,2n ../data/SNPinSS/SignalSiteRegions.bed > ../data/SNPinSS/SignalSiteRegions.sort.bed


sort -k1,1 -k2,2n ../data/SNPinSS/SSRegions_permuted.bed >../data/SNPinSS/SSRegions_permuted.sort.bed

sort -k1,1 -k2,2n ../data/SNPinSS/FiftyupstreamPASwSS.bed > ../data/SNPinSS/FiftyupstreamPASwSS.sort.bed

sort -k1,1 -k2,2n ../data/SNPinSS/OtherSSRegions.bed > ../data/SNPinSS/OtherSSRegions.sort.bed

intersect with bedtools to map the snps to the regions. Then I will be able to select the snp PAS associations.

sbatch mapSSsnps2PAS.sh

Results to get the associations:

SSsnpswithPAS=read.table("../data/SNPinSS/SNPinSS2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(SSsnpswithPAS, "../data/SNPinSS/SS_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")


SSsnpswithPERMPAS=read.table("../data/SNPinSS/SNPinPermSS2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(SSsnpswithPERMPAS, "../data/SNPinSS/PermSS_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")

SNPregion=read.table("../data/SNPinSS/SNPSinFiftyupstream2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(SNPregion, "../data/SNPinSS/PASregion_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")


Otherregion=read.table("../data/SNPinSS/Otherregions2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(Otherregion, "../data/SNPinSS/Otherregions_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")

Process the snp region with python

python fixPASregionSNPs.py

Run this with total and nuclear


sbatch RunRes2PAS.sh

#all of this code is in the above batch script 
python NomResfromPASSNP.py ../data/SNPinSS/SS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/SS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/SS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/SS_Total_nomRes.txt 


python NomResfromPASSNP.py ../data/SNPinSS/PermSS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/PermSS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/PermSS_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/PermSS_Total_nomRes.txt 


python NomResfromPASSNP.py ../data/SNPinSS/PASregion_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/RegionSS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/PASregion_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/RegionSS_Total_nomRes.txt  

python NomResfromPASSNP.py ../data/SNPinSS/Otherregions_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/OtherSS_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/Otherregions_PASandSNPs.FIXED.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/OtherSS_Total_nomRes.txt  
Nuclear_SS=read.table('../data/apaQTLNominal_4pc/SS_Nuclear_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="SS")
Nuclear_Perm=read.table('../data/apaQTLNominal_4pc/PermSS_Nuclear_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="Permuted")

Total_SS=read.table('../data/apaQTLNominal_4pc/SS_Total_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="SS")
Total_Perm=read.table('../data/apaQTLNominal_4pc/PermSS_Total_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="Permuted")


Nuclear_Region=read.table('../data/apaQTLNominal_4pc/RegionSS_Nuclear_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="Region")

Total_Region=read.table('../data/apaQTLNominal_4pc/RegionSS_Total_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="Region")

Nuclear_other=read.table('../data/apaQTLNominal_4pc/OtherSS_Nuclear_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Nuclear", set="Upstream")

Total_other=read.table('../data/apaQTLNominal_4pc/OtherSS_Total_nomRes.txt',header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F) %>% select(pval,slope) %>% mutate(fraction="Total", set="Upstream")


all_SS_pval=bind_rows(Nuclear_SS,Nuclear_Perm,Total_SS,Total_Perm,Nuclear_Region,Total_Region,Nuclear_other,Total_other)

all_SS_pval$set <- factor(all_SS_pval$set, levels=c("SS", "Upstream", "Permuted", "Region"))
ggplot(all_SS_pval, aes(x=fraction, fill=set, y=pval)) + geom_boxplot() + labs(x="Fraction", title="p-values for SNPs in Signal Sites",y="nominal P-value" ) + scale_fill_discrete(name = 'Set', labels = c('Signal Sites', 'Region upstream of Signal Site', 'Permuted Distance to Signal Site',"50 bp upstream of PAS"))

Version Author Date
77700ac brimittleman 2019-06-25
11a3069 brimittleman 2019-06-25
ggplot(all_SS_pval, aes(x=fraction, fill=set, y=abs(slope))) + geom_boxplot()  + labs(x="Fraction", title="p-values for SNPs in Signal Sites",y="absolute value effect size" ) + scale_fill_discrete(name = 'Set', labels = c('Signal Sites', 'Region upstream of Signal Site', 'Permuted Distance to Signal Site',"50 bp upstream of PAS"))

Version Author Date
77700ac brimittleman 2019-06-25
11a3069 brimittleman 2019-06-25

I am plotting this the wrong way. I need to make qqplots with the snps.

#plot qqplot
qqplot(-log10(runif(nrow(Nuclear_SS))), -log10(Nuclear_SS$pval))
points(sort(-log10(runif(nrow(Total_SS)))), sort(-log10(Total_SS$pval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(Nuclear_Region)))), sort(-log10(Nuclear_Region$pval)),col= alpha("Orange"))
points(sort(-log10(runif(nrow(Total_Region)))), sort(-log10(Total_Region$pval)),col= alpha("Green"))

abline(0,1)

Version Author Date
416f8e8 brimittleman 2019-06-27

This is close but i need to make this a better comparison.I want just the UTR variants.

Nuclear_SS_all=read.table('../data/apaQTLNominal_4pc/SS_Nuclear_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)  %>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

Total_SS_all=read.table('../data/apaQTLNominal_4pc/SS_Total_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)%>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

The benchmark set is the UTRs without signal sites

PASwSS=read.table("../data/PAS/PASwSignalSite.txt", header = T,stringsAsFactors = F)
PAS_noSS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, header = F, col.names = c("chr", "start", "end", "PASid","score", "strand")) %>% separate(PASid, into=c("pasNum", "geneiD"), sep=":") %>% mutate(PAS=paste("peak", pasNum, sep=""),PASname=paste(PAS, geneiD, sep="_")) %>% anti_join(PASwSS, by="PAS") %>% separate(geneiD,into=c("gene", "loc"), sep="_") %>% filter(loc=="utr3") %>% mutate(newEnd=ifelse(strand=="+", end+50, end),newStart=ifelse(strand=="+", start, start-50))%>% select(chr, newStart,newEnd, PASname, score, strand)

write.table(PAS_noSS,"../data/SNPinSS/UTRregionsPASnoSS.bed", col.names = F, row.names = F, quote = F, sep="\t")

sort -k1,1 -k2,2n ../data/SNPinSS/UTRregionsPASnoSS.bed > ../data/SNPinSS/UTRregionsPASnoSS.sort.bed


sbatch subsetVCF_noSSregions.sh

cat  ../data/SNPinSS/UTRnoSS_chr* >../data/SNPinSS/UTRnoSS_Allchr.recode.vcf


python vcf2bed.py ../data/SNPinSS/UTRnoSS_Allchr.recode.vcf  ../data/SNPinSS/UTRnoSS_SNPsAllchr.bed

sort -k1,1 -k2,2n ../data/SNPinSS/UTRregionsPASnoSS.bed > ../data/SNPinSS/UTRregionsPASnoSS.sort.bed
sort -k1,1 -k2,2n ../data/SNPinSS/UTRnoSS_SNPsAllchr.bed > ../data/SNPinSS/UTRnoSS_SNPsAllchr.sort.bed

sbatch mapSSsnps2PAS.sh
NOSSsnpswithPAS=read.table("../data/SNPinSS/UTRnoSS_SNPsAllchr2PAS.txt",col.names = c("chr","start", "end", "PASname", "score", "strand", "SNP")) %>% filter(SNP!=".") %>% separate(PASname, into=c("PAS", "gene", "loc"),sep="_") %>% select(PAS, SNP)
write.table(NOSSsnpswithPAS, "../data/SNPinSS/NoSSUTR_PASandSNPs.txt", row.names = F, col.names = F, quote = F, sep="\t")

Get the pval:

python NomResfromPASSNP.py ../data/SNPinSS/NoSSUTR_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/NoSSUTR_Nuclear_nomRes.txt 

python NomResfromPASSNP.py ../data/SNPinSS/NoSSUTR_PASandSNPs.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/apaQTLNominal_4pc/NoSSUTR_Total_nomRes.txt 
Nuclear_NOSS=read.table('../data/apaQTLNominal_4pc/NoSSUTR_Nuclear_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)  %>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

Total_NOSS=read.table('../data/apaQTLNominal_4pc/NoSSUTR_Total_nomRes.txt', header = F, col.names=c("peakID", "snp", "dist", "pval", "slope"), stringsAsFactors = F)%>% separate(peakID, into=c("chr", "start", "end", "PASid"), sep=":")%>% separate(PASid, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3")

Total

#plot qqplot
qqplot(-log10(runif(nrow(Total_NOSS))), -log10(Total_NOSS$pval), xlab="-log10(Uniform)", ylab="-log10(pval)", main="Total Apa")
points(sort(-log10(runif(nrow(Total_SS)))), sort(-log10(Total_SS$pval)),col= alpha("Red"))
abline(0,1)
legend("topleft", legend=c("SNPs in UTR PAS Signal Sites", "SNPS not in Signal Sites"),col=c("red", "black"), pch=16,bty = 'n')

Version Author Date
416f8e8 brimittleman 2019-06-27

Nuclear:

qqplot(-log10(runif(nrow(Nuclear_NOSS))), -log10(Nuclear_NOSS$pval),xlab="-log10(Uniform)", ylab="-log10(pval)", main="Nuclear Apa")
points(sort(-log10(runif(nrow(Nuclear_SS)))), sort(-log10(Nuclear_SS$pval)),col= alpha("Red"))
abline(0,1)

legend("topleft", legend=c("SNPs in UTR PAS Signal Sites", "SNPS not in Signal Sites"),col=c("red", "black"), pch=16,bty = 'n')

Version Author Date
416f8e8 brimittleman 2019-06-27

Assess significance:

wilcox.test(Nuclear_SS$pval,Nuclear_NOSS$pval,alternative = 'less')

    Wilcoxon rank sum test with continuity correction

data:  Nuclear_SS$pval and Nuclear_NOSS$pval
W = 13543, p-value = 0.2143
alternative hypothesis: true location shift is less than 0
wilcox.test(Total_SS$pval,Total_NOSS$pval, alternative = "less")

    Wilcoxon rank sum test with continuity correction

data:  Total_SS$pval and Total_NOSS$pval
W = 14728, p-value = 0.5313
alternative hypothesis: true location shift is less than 0

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.4.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4