Enhort - Genomic Position Profiling

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Current File: HIV

Positions395

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Data

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Annotation group ( 7 / 23 )

Max effect size

Value distribution

Genetic (6)

1.78

Significant Tracks

P Value

Effect Size

Relative Integration Data vs. Background

Housekeeping genes

0.0

1.78

	In	Out
HIV	73 (18.48%)	322 (81.52%)
Background	519 (5.19%)	9481 (94.81%)

Housekeeping genes
Cell line Unknown (hg19).
The original data source is UCSC http://www.tau.ac.il/~elieis/HKG/HK_genes.txt.

RefSeq genes

0.0

1.4

	In	Out
HIV	315 (79.75%)	80 (20.25%)
Background	4601 (46.01%)	5399 (53.99%)

RefSeq genes
Cell line Unknown (hg19).
The original data source is UCSC https://genome.ucsc.edu/cgi-bin/hgTables?clade=mammal&org=Human&db=hg19&hgta_group=allTables&hgta_table=refGene.

Coding Exons

0.00105

1.17

	In	Out
HIV	13 (3.29%)	382 (96.71%)
Background	132 (1.32%)	9868 (98.68%)

Coding Exons
Cell line Unknown (hg19).
The original data source is UCSC https://genome.ucsc.edu/cgi-bin/hgTables?clade=mammal&org=Human&db=hg19&hgta_group=genes&hgta_track=refSeqComposite&hgta_table=refGene.

3' UTR

9.65E-4

1.15

	In	Out
HIV	14 (3.54%)	381 (96.46%)
Background	146 (1.46%)	9854 (98.54%)

3' UTR
Cell line Unknown (hg19).
The original data source is UCSC https://genome.ucsc.edu/cgi-bin/hgTables?clade=mammal&org=Human&db=hg19&hgta_group=genes&hgta_track=refSeqComposite&hgta_table=refGene.

Introns

0.0

1.12

	In	Out
HIV	293 (74.18%)	102 (25.82%)
Background	4358 (43.58%)	5642 (56.42%)

Introns
Cell line Unknown (hg19).
The original data source is UCSC https://genome.ucsc.edu/cgi-bin/hgTables?clade=mammal&org=Human&db=hg19&hgta_group=genes&hgta_track=refSeqComposite&hgta_table=refGene.

Exons

4.55E-5

1.07

	In	Out
HIV	27 (6.84%)	368 (93.16%)
Background	312 (3.12%)	9688 (96.88%)

Exons
Cell line Unknown (hg19).
The original data source is UCSC https://genome.ucsc.edu/cgi-bin/hgTables?clade=mammal&org=Human&db=hg19&hgta_group=genes&hgta_track=refSeqComposite&hgta_table=refGene.

Restriction Sites (1)

0.88

Significant Tracks

P Value

Effect Size

Relative Integration Data vs. Background

BamHI

0.00102

0.88

	In	Out
HIV	24 (6.08%)	371 (93.92%)
Background	1139 (11.39%)	8861 (88.61%)

Restriction enzyme
Cell line Unknown (hg19).
The original data source is Other

Covariates (0)

chr1

chr2

chr3

chr4

chr5

chr6

chr7

chr8

chr9

chr10

chr11

chr12

chr13

chr14

chr15

chr16

chr17

chr18

chr19

chr20

chr21

chr22

chrX

chrY

Each blue bar represents the relative count of integration sites in this part of the genome. Lighter bars show few, and darker bars show an increased count of sites.

Positional intersect of the most significant tracks

Copy this code and run it on your machine using R to generate a barplot of the results of your analysis. You can select a subset of tracks by clicking 'change tracks' on the left.

dat = data.frame(
c(9.65E-4, 0.0475, 0.727, 0.00105, 1.0, 0.082, 0.0726, 0.117, 0.0132, 0.8, 4.55E-5, 0.551, 0.0, 0.0, 0.0, 0.255, 0.0139, 0.00102, 0.776, 0.989),
c(1.15, 0.95, 0.27, 1.17, 0.0, 2.0, 1.07, 0.65, 0.49, 0.04, 1.07, 0.43, 1.78, 1.12, 1.4, 0.33, 0.47, 0.88, 0.02, 0.0),
c(14, 6, 1, 13, 395, 0, 4, 10, 49, 64, 27, 0, 73, 293, 315, 22, 52, 24, 217, 213),
c(146, 67, 36, 132, 10000, 76, 241, 412, 878, 1573, 312, 9, 519, 4358, 4601, 437, 945, 1139, 5421, 5389),
c(3.54, 1.52, 0.25, 3.29, 100.0, 0.0, 1.01, 2.53, 12.41, 16.2, 6.84, 0.0, 18.48, 74.18, 79.75, 5.57, 13.16, 6.08, 54.94, 53.92),
c(1.46, 0.67, 0.36, 1.32, 100.0, 0.76, 2.41, 4.12, 8.78, 15.73, 3.12, 0.09, 5.19, 43.58, 46.01, 4.37, 9.45, 11.39, 54.21, 53.89)
)

row.names(dat) <- c("3' UTR", "5' UTR", "Blacklisted regions", "Coding Exons", "Contigs", "CpG islands", "CpG islands with 1kb window", "CpG islands with 2kb window", "CpG islands with 5kb window", "DNAse cluster regions", "Exons", "FANTOM5 CAGE Peaks", "Housekeeping genes", "Introns", "RefSeq genes", "TSS with 2kb window", "TSS with 5kb window", "BamHI", "EcoRII-1", "EcoRII-2")
colnames(dat) <- c('p value','effect size','count positions measured inside','count positions expected inside ','percent positions measured inside','percent positions expected inside')

#### Barplot ####

#bottom, left, top and right margins
par(mar=c(15,5,5,5))
fontsize=1

perc <- as.matrix(rbind(dat$`percent positions measured inside`,dat$`percent positions expected inside`))
colnames(perc) <- row.names(dat)

mp <- barplot(perc,las=2,
              ylim=c(0,115),
              col = c('blue', 'lightgray'),
              #log='y',
              beside=TRUE,
              cex=fontsize,
              cex.axis=fontsize,
              cex.lab=fontsize,
              ylab = '% inside')

#### pvalues####
pval = 0.05 # significance level 5%
pvals <- dat$`p value` #pvalues

#Plot stars above each significant bar group:
stars <- lapply(pvals,function(x)(ifelse(x < pval, '*', '')))
text(mp[1,] + 0.5, apply(perc,2,max)+2, stars,col='red', cex=2)

#Or plot the p value for each two bars:
#text(mp[1,] + 0.5, apply(perc,2,max)+4, pvals,col='red')

#### Legend ####-
legend('topright', legend=c('% inside HIV', '% inside control', 'p value < 0.05'), col=c('blue','lightgray','red'), bty='n', pch = c(15, 15, 8))

#### Vertical percent values, delete or comment out both lines to remove from plot ####
percVals <- lapply(perc, function(x) paste(toString(x[1]),'%'))
text(mp, apply(perc, 1:2, function(x) max(x-5,20)), percVals,col='coral',srt=90)

### column number warning ####
if (length(colnames(perc)) > 20) {
    title(main = 'You selected too many tracks in Enhort, 
 try to run the analysis with fewer tracks to get a better plot.')
}

Download background positions as .bed-file Download results as .csv-file