Last updated: 2019-01-28

workflowr checks: (Click a bullet for more information)
  • R Markdown file: up-to-date

    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.

  • Environment: empty

    Great job! The global environment was empty. Objects defined in the global environment 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.

  • Seed: set.seed(12345)

    The command set.seed(12345) 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.

  • Session information: recorded

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

  • Repository version: e7ed8fc

    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:    output/.DS_Store
    
    Untracked files:
        Untracked:  KalistoAbundance18486.txt
        Untracked:  analysis/DirectionapaQTL.Rmd
        Untracked:  analysis/EvaleQTLs.Rmd
        Untracked:  analysis/YL_QTL_test.Rmd
        Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
        Untracked:  analysis/snake.config.notes.Rmd
        Untracked:  analysis/verifyBAM.Rmd
        Untracked:  code/PeaksToCoverPerReads.py
        Untracked:  code/strober_pc_pve_heatmap_func.R
        Untracked:  data/18486.genecov.txt
        Untracked:  data/APApeaksYL.total.inbrain.bed
        Untracked:  data/ChromHmmOverlap/
        Untracked:  data/GM12878.chromHMM.bed
        Untracked:  data/GM12878.chromHMM.txt
        Untracked:  data/LianoglouLCL/
        Untracked:  data/LocusZoom/
        Untracked:  data/NuclearApaQTLs.txt
        Untracked:  data/PeakCounts/
        Untracked:  data/PeakCounts_noMP_5perc/
        Untracked:  data/PeakUsage/
        Untracked:  data/PeakUsage_noMP/
        Untracked:  data/PeaksUsed/
        Untracked:  data/PeaksUsed_noMP_5percCov/
        Untracked:  data/RNAkalisto/
        Untracked:  data/RefSeq_annotations/
        Untracked:  data/TotalApaQTLs.txt
        Untracked:  data/Totalpeaks_filtered_clean.bed
        Untracked:  data/UnderstandPeaksQC/
        Untracked:  data/YL-SP-18486-T-combined-genecov.txt
        Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
        Untracked:  data/YL_QTL_test/
        Untracked:  data/apaExamp/
        Untracked:  data/apaQTL_examp_noMP/
        Untracked:  data/bedgraph_peaks/
        Untracked:  data/bin200.5.T.nuccov.bed
        Untracked:  data/bin200.Anuccov.bed
        Untracked:  data/bin200.nuccov.bed
        Untracked:  data/clean_peaks/
        Untracked:  data/comb_map_stats.csv
        Untracked:  data/comb_map_stats.xlsx
        Untracked:  data/comb_map_stats_39ind.csv
        Untracked:  data/combined_reads_mapped_three_prime_seq.csv
        Untracked:  data/diff_iso_proc/
        Untracked:  data/diff_iso_trans/
        Untracked:  data/ensemble_to_genename.txt
        Untracked:  data/example_gene_peakQuant/
        Untracked:  data/explainProtVar/
        Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
        Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed
        Untracked:  data/first50lines_closest.txt
        Untracked:  data/gencov.test.csv
        Untracked:  data/gencov.test.txt
        Untracked:  data/gencov_zero.test.csv
        Untracked:  data/gencov_zero.test.txt
        Untracked:  data/gene_cov/
        Untracked:  data/joined
        Untracked:  data/leafcutter/
        Untracked:  data/merged_combined_YL-SP-threeprimeseq.bg
        Untracked:  data/molPheno_noMP/
        Untracked:  data/mol_overlap/
        Untracked:  data/mol_pheno/
        Untracked:  data/nom_QTL/
        Untracked:  data/nom_QTL_opp/
        Untracked:  data/nom_QTL_trans/
        Untracked:  data/nuc6up/
        Untracked:  data/nuc_10up/
        Untracked:  data/other_qtls/
        Untracked:  data/pQTL_otherphen/
        Untracked:  data/peakPerRefSeqGene/
        Untracked:  data/perm_QTL/
        Untracked:  data/perm_QTL_opp/
        Untracked:  data/perm_QTL_trans/
        Untracked:  data/perm_QTL_trans_filt/
        Untracked:  data/perm_QTL_trans_noMP_5percov/
        Untracked:  data/protAndAPAAndExplmRes.Rda
        Untracked:  data/protAndAPAlmRes.Rda
        Untracked:  data/protAndExpressionlmRes.Rda
        Untracked:  data/reads_mapped_three_prime_seq.csv
        Untracked:  data/smash.cov.results.bed
        Untracked:  data/smash.cov.results.csv
        Untracked:  data/smash.cov.results.txt
        Untracked:  data/smash_testregion/
        Untracked:  data/ssFC200.cov.bed
        Untracked:  data/temp.file1
        Untracked:  data/temp.file2
        Untracked:  data/temp.gencov.test.txt
        Untracked:  data/temp.gencov_zero.test.txt
        Untracked:  data/threePrimeSeqMetaData.csv
        Untracked:  output/picard/
        Untracked:  output/plots/
        Untracked:  output/qual.fig2.pdf
    
    Unstaged changes:
        Modified:   analysis/28ind.peak.explore.Rmd
        Modified:   analysis/CompareLianoglouData.Rmd
        Modified:   analysis/apaQTLoverlapGWAS.Rmd
        Modified:   analysis/cleanupdtseq.internalpriming.Rmd
        Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
        Modified:   analysis/dif.iso.usage.leafcutter.Rmd
        Modified:   analysis/diff_iso_pipeline.Rmd
        Modified:   analysis/explainpQTLs.Rmd
        Modified:   analysis/explore.filters.Rmd
        Modified:   analysis/flash2mash.Rmd
        Modified:   analysis/mispriming_approach.Rmd
        Modified:   analysis/overlapMolQTL.Rmd
        Modified:   analysis/overlapMolQTL.opposite.Rmd
        Modified:   analysis/overlap_qtls.Rmd
        Modified:   analysis/peakOverlap_oppstrand.Rmd
        Modified:   analysis/peakQCPPlots.Rmd
        Modified:   analysis/pheno.leaf.comb.Rmd
        Modified:   analysis/swarmPlots_QTLs.Rmd
        Modified:   analysis/test.max2.Rmd
        Modified:   analysis/understandPeaks.Rmd
        Modified:   code/Snakefile
    
    
    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.
Expand here to see past versions:
    File Version Author Date Message
    Rmd e7ed8fc Briana Mittleman 2019-01-28 fix map func
    html a97adbc Briana Mittleman 2019-01-28 Build site.
    Rmd a2911ff Briana Mittleman 2019-01-28 start analysis for new gene peak assignemnt


Lin et al: An in-depth map of polyadenylation sites in cancer 2012: -Mapped locations to annotated locations in UCUS browser: “The mapped locations were annotated using the UCSC genome browser tables ( 26 ). When a locus could be attributed to multiple possible annotations, the locus was assigned with a single annotation in the following priority order: 3′ UTRs (sense), coding sequences (CDS, sense), 5′ UTRs (sense), intron (sense), non-coding RNAs (ncRNAs, sense), 5′ UTR antisense, CDS antisense, 3′ UTR antisense, intron antisense, promoter antisense, ncRNA antisense and intergenic”

I want to download this annotation and try this. I am using the ncbi_refseq annotations. I will download regions of the genome seperatly and then merge the files.

  • 5’ UTR

  • Coding Exon

  • Intron

  • 3’ UTR

  • (downstream 5000)-downstream proximal region

I also want a dictionary with the transcripts and the gene names for the annotation. This information will come from the Transcript2GeneName file. In this file the transcript ID is in column1 and the gene name column 13.

I have downloaded all of the these to data/RefSeq_annotations. I will concatinate all of these for a full annotation dataset, I will then sort this file. The file is ncbiRefSeq_allAnnotation.sort.bed

Using this I can create an annotation in a bed file I can use for the overlap with my peaks. This will include getting the transcript to gene annotations. I will transfer the files to midway in my genome annotation directory and work with them there.

Format full refseq annotation:

TXN2Gene_file="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms"

gene_dic={}

for ln in open(TXN2Gene_file,"r"):
   txn=ln.split()[1]
   gene=ln.split()[12]
   gene_dic[txn]=gene

outF=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed","w")

inFile="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_allAnnotation.sort.bed"  


for ln in open(inFile, "r"):
   chrom, start, end, name, score, strand = ln.split()
   chrom_fix=chrom[3:]
   txn=name.split("_")[:2]
   txnF="_".join(txn)
   gene=gene_dic[txnF]
   type=name.split("_")[2]
   id=type + ":" + gene
   outF.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom_fix, start, end, id, score, strand))

outF.close()

I want to create a file with my peaks mapped to these regions. I will include a structure for when there is a tie and put intergenic if it is not found. I need to do an intersect that gives me all of the IDs. After this I can use python to parse the hiarchy.

I can use bedtools map for this. I want all of the data to come back.

-c 4 -o distinct
-S opposite strand

I will do this on the peaks before I looked at usage.

mapnoMPPeaks2GenomeLoc.sh

#!/bin/bash

#SBATCH --job-name=mapnoMPPeaks2GenomeLoc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapnoMPPeaks2GenomeLoc.out
#SBATCH --error=mapnoMPPeaks2GenomeLoc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#annotation: /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed

#peaks:  /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed


bedtools map -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed -c 4 -S -o distinct > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.19      digest_0.6.17    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20       



This reproducible R Markdown analysis was created with workflowr 1.1.1