Discriminative DNA IUPAC motif discovery tool
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README.md

DiNAMO: An exact and efficient method for IUPAC motif discovery in DNA sequences

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The DiNAMO software implements an exhaustive algorithm to detect over-represented IUPAC motifs in a set of DNA sequences. It has two modes: scanning mode (default), where all windows are parsed, or fixed-position mode (see optional parameter -p ), where only motifs occurring at a specific position in the sequences are taken into account.

DiNAMO can be used in a variety of applications, such as ChIP-seq peak analysis for transcription factor binding sites identification, or finding motifs that induce systematic sequencing errors.

Installation

Binaries

Binaries for Windows, OS X and Linux are available here.

Build from source code

Dependencies

Boost library (if not in the standard includes, please use -I /path/to/boost in the Makefile)

Compilation

make

The executable file is located in the bin/ directory

Usage

DiNAMO takes as input two fasta files, which contain respectively the positive dataset (signal.fa) and the negative dataset (control.fa). You should specify also the motif length '-l'. Optionally, you can provide a maximum number of degenerate letters '-d'. The output file contains the over-represented motifs found in the positive dataset compared to the negative dataset, in the MEME format (see http://meme-suite.org/doc/meme-format.html).

    dinamo -pf signal.fa  -nf control.fa -l 6

Optional Parameters

  • maximum number i of degenerate letters

      -d <i>
    
  • Only process motifs at a offset i (0..N) related to the end of each sequence

     -p <i>
    
  • output file

      -o <file>
    
  • change Fisher's exact test p-value threshold f (default: 0.05)

      -t <f>
    
  • do not display logs

      --no-log
    

References

how to cite this tool:

C. Saad, L. Noé, H. Richard, J. Leclerc, M.-P. Buisine, H. Touzet, and M. Figeac. Dinamo: highly sensitive dna motif discovery in high-throughput sequencing data. BMC Bioinformatics, 19(1):223, Jun 2018. https://doi.org/10.1186/s12859-018-2215-1