Discriminative Additive Model Optimization (DAMO)

DAMO is a Python implementation of the Discriminative Motif Optimizer (DiMO) program and extends it to include adjacent di-nucleotide interactions. It requires Python 2.7 and the following Python packages:
numpy, scipy, matplotlib, sklearn, requests.

To execute the DAMO program, please follow the instructions below.

usage: DAMO.py [-h] -p POSITIVE -n NEGATIVE -s SEED [-f FLAG] [-g GENERATION] [-i] [-o OUTPUT] [-v]

optional arguments:
  -h, --help            show this help message and exit
  -p POSITIVE, --positive POSITIVE
                        path of positive sequences (FASTA format)
  -n NEGATIVE, --negative NEGATIVE
                        path of negative sequences (FASTA format)
  -s SEED, --seed SEED  path of the initial position frequency matrix
  -f FLAG, --flag FLAG  prefix of the output filename (optional, default: "DAMO")
  -g GENERATION, --generation GENERATION
                        number of optimization iterations (optional, default: 500)
  -i, --interaction     consider adjacent di-nucleotide interactions (optional, default: False)
  -o OUTPUT, --output OUTPUT
                        output directory (optional, default: current working directory)
  -v, --version         show program's version number and exit


Note:
The input format of the initial position frequency matrix: The first line starts with '>' and gives a description of the motif. The next four lines specify the position frequency matrix (in the order of ACGT).

Example:
> motif name
A | 0.001 0.001 0.3 0.001 0.001
C | 0.001 0.001 0.3 0.001 0.001
G | 0.997 0.997 0.1 0.001 0.001
T | 0.001 0.001 0.3 0.997 0.997

Author:
Shuxiang Ruan (sruan@wustl.edu), Washington University in St. Louis

Reference:
Ruan S, Stormo GD. Comparison of discriminative motif optimization using matrix and DNA shape-based models. BMC Bioinformatics (2018)
Patel RY, Stormo GD. Discriminative motif optimization based on perceptron training. Bioinformatics (2014)