MotiveSearch

Introduction

[under development]

Workflow

1. Identify differentially expressed genes. (e.g. RNAseq)
2. Aquire genomic sequence of host organism and sequences of promoters.
3. Specify parameters in config.txt. An example is given in the "Input files" section.
4. Determine most frequent kmers in the promoters of the differentially expressed genes.
5. Determine baseline frequency of those kmers in the entire genome.
6. Cluster kmers that are found significantly more frequent in the used promoters than in the genome.

Usage

The script offers two

python script.py -m PrepareGenome -i genome.fasta

python script.py -m AnalysePromoters -i counttable.txt -t counttable

Input files

config.txt

path_to_genome = ... // Full path to the genome of the relevant host organism
path_to_promoters = ... // Full path to the file with the genes promoter sequences
list_of_differentially_expressed_genes = ... // file containing a list of gene identifiers
kmer_length = ... // Length of the kmers that will be searched
top_n_kmers = ... // Amount of kmers that should be analysed