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Discriminative Additive Model Optimization (DAMO)
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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)
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