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https://github.com/alexjgriffith/mt1

Motifs in Tandem with One Another
https://github.com/alexjgriffith/mt1

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Motifs in Tandem with One Another

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# mT1

## Motifs in Tandem With One Another

### Alexander Griffith

### Aug 24 2016



**mT1** identifies the distance between DNA motifs in subsets of the genome. First it builds the empirical PDF from individual motifs, then it generates the expectations for the distances between the two motifs. **mT1** was designed to be applied to ChIP-Seq peaks.

___

### Installation

Currently only the source is available, compiled versions for Windows and Mac will be made available once mT1 is submitted to Bioconductor. There are two easy ways to install the package, via command line, and within R itself. The `devtools` package is required to install from within R.



From the command line:



```sh

## Clone the repository

git clone https://github.com/alexjgriffith/mT1.git

cd mT1



## Install 

R CMD INSTALL .

```

From within R:



```R

## Check if devtools is installed

if(!require(devtools)){

	install.packages("devtools")

	library(devtools)

}

## Install mT1

devtools::install_github("alexjgriffith/mT1")



## Install mT1 from the develop branch

## devtools::install_github("alexjgriffith/mT1",ref="develop")

```



The main branch is guaranteed to pass `R CMD check .` with no warnings once mT1 is build using `R CMD build --resave-data .`. The merges to the develop branch should always pass, however it is not a guarantee.



Note that mT1 also requires the Bioconductor package `Biostrings`.



To install `Biostrings`:



```R

source("http://bioconductor.org/biocLite.R")

biocLite("Biostrings")

```



___

### Examples

Compare the distances between two motifs in a set of genome subsets.



```R

## libraries

library(mT1)

## load the fasta file for analysis

fasta<-mT1_fasta



## Find the distances between CANNTG and GATAA for on each string

## they share. The distances are relative to the start of each

## motif

distances<-motifDistance(fasta,"CANNTG","GATAA")



## Plot the results between -20 and 20 bp

x<-seq(-20,20)

y<-combHeights(x,distances[,2])[[1]]

plot(x,y,main="CANNTG-GATAA",xlab="distance(bp)",

	 ylab="Frequency",type="l")



## Generate the emperical PDF

width<-300 # the width the fasta lines (must be uniform)

m1PDF<-motifPDF(fasta,"CANNTG")[,2]

m2PDF<-motifPDF(fasta,"GATAA")[,2]



## Find the expected probability for each distance

## Requires all peaks to be the same width

mp<-eMP(m1PDF,m2PDF,width)



## r is the range of the resulting mp, note that its length should 

## be one less than twice the width

r<-seq(-width+1,width-1)

hs<-combHeights(r,distances[,2])[[1]]

n<-length(distances[,2]) # total number of occurrences



## Apply the bionomial test to the expected probability and

## number of peaks seen

pvalue<-mapply(function(a,b)btest(a,n,b),hs,mp)



par(mfrow=c(2,1))

plot(r,pvalue)

plot(r,hs)



```



Analysis of multiple motifs under peaks that have been previously identified. This example requires `BSgenome.Hsapiens.UCSC.hg19`, which can be installed using `biocLite`.

```R

## libraries

library(mT1)

library(Biostrings) # needed to get fasta data from genomic co-ords

library(BSgenome.Hsapiens.UCSC.hg19) # genome



## load a set of Jaspar motifs as strings

jaspar<-mT1_jaspar



## Example set of peaks

peaks<-mT1_peaks



## Transform genomic co-ords into neucleotides

genome<-BSgenome.Hsapiens.UCSC.hg19

fasta<-getSeq(genome,peaks$chr,start=peaks$start,end=peaks$end)



## Motifs to compare

motifs<-c("CANNTG","GATAA",jaspar$jsublM[1:8])



## Find the preferred distances between `motifs` under the peaks

objMT1<-mT1(fasta,motifs)



## plot based on string

plot(objMT1,"CANNTG","GATAA")



## Plot based on printed order

print(objMT)

plot(objMT1,i=1)



```