makePromoterDBfromFimo: Count the Occurence of Sequence Motifs from Fimo File.

In jsalojar/TFbindR: Identification and Counting of the Occurence of Motifs in a Set of Promoter Sequences. Motif Enrichment Analysis

Description

The function reads the input sequences from a FASTA file and identifies the genes. Next, it splits the strands from the FIMO file into "+" and "-", and counts the occurence of gene and motif combination for each strand separately. The counts are reported in a matrix.

Usage

makePromoterDBfromFimo(fimo, promoter.fasta, promoterDB = "promoter.DB")

Arguments

fimo	A fimo file containing the motifs of interest.
promoter.fasta	Fasta file of the promoter sequences.
promoterDB	The path, and the name of the object where results will be saved.

Value

A list named Strands with 3 elements,

plus	A count matrix of the occurence of motifs in the "plus" strand.
minus	A count matrix of the occurence of motifs in the "minus" strand.
both	A count matrix of the occurence of motifs in the "minus" and "plus" strands together.

References

Grant, Charles E., Timothy L. Bailey, and William Stafford Noble. "FIMO: scanning for occurrences of a given motif." Bioinformatics 27.7 (2011): 1017-1018.

Examples

# Read a sample fimo file. The data is downloaded from the Arabidopsis Cistrome database.
fimo.dir = system.file("extdata", "cistrome.fimo.txt", package="TFbindR", mustWork = TRUE)

# Read a sample fasta file. 
promoter.dir = system.file("extdata","TAIR10_upstream_1000_translation_start_20101028.fa",package="TFbindR")

# Specify the output directory.
output.dir="."

# Run the function to create the DB and save it into R file type .rda which is automatically compressed. 
makePromoterDBfromFimo(fimo = fimo.dir, promoter.fasta=promoter.dir, promoterDB=file.path(output.dir,"TAIR10_500bp_upstream.fimo.db.rda"))

jsalojar/TFbindR documentation built on Dec. 9, 2019, 12:16 a.m.