MotifMaker is a tool for identify motifs associated with DNA modifications in prokaryotic genomes. Modified DNA in prokaryotes commonly arises from restriction-modification systems that methylate a specific base in a specific sequence motif. The canonical example the m6A methylation of adenine in GATC contexts in E.coli. Prokaryotes may have a very large number of active restriction-modification systems present, leading to a complicated mixture of sequence motifs.

PacBio SMRT sequencing is sensitive to the presence of methylated DNA at single base resolution, via shifts in the polymerase kinetics observed in the real-time sequencing traces. See our publication for more background on modification detection: http://nar.oxfordjournals.org/content/early/2011/12/07/nar.gkr1146.full

Existing motif finding algorithms such as MEME-chip and YMF are sub-optimal for this case for the following reasons:

1. They search for a single motif, rather than attempting to identify a complicated mixture of motifs

2. They generally don't accept the notion of aligned motifs - the input to the tools is a window into the reference sequence which can contain the motif at any offset, rather a single center position that is available with kinetic modification detection.

3. Implementations generally either use a Markov model of the reference (MEME-chip), or do exact counting on the reference, but place restrictions on the size and complexity of the motifs that can be discovered.

Here we give a rough overview of the algorthim used by MotifMaker. Define a motif as a set of (position relative to methylation, required base) tuples. Positions not listed in the motif are implicitly degenerate. Given a list of modification detections and a genome sequence, we define the following objective function on motifs:

Motif score(motif) = (# of detections matching motif) / (# of genome sites matching motif) * (Sum of log-pvalue of detections matching motif) = (fraction methylated) * (sum of log-pvalues of matches)

We search (close to exhaustively) through the space of all possible motifs, progressively testing longer motifs using a branch-and-bound search. The 'fraction methylated' term must be less than 1, so the maximum achievable score of a child node is the sum of scores of modification hits in the current node, permitting us to prune all search paths whose maximum achievable score is less than the best score discovered so far.

The jar supplied in target/motif-maker-0.2.one-jar.jar bundles all dependencies and should be runnable on most systems. mvn package will rebuild the one-jar jar if you make any code change.

For command-line motif finding, run the 'find' sub-command, and pass the reference fasta and the modifications.gff(.gz) file emitted by the PacBio modification detection workflow. The reprocess command annotates the the gff with motif information for better genome browsing.

$ java -jar target/motif-maker-0.2.one-jar.jar

Usage: MotifMaker [options] [command] [command options]
  Options:
    -h, --help
                 Default: false
  Commands:
    find      Run motif finding
      Usage: find [options]
        Options:
        * -f, --fasta      Reference fasta file
        * -g, --gff        modifications.gff or .gff.gz file
          -m, --minScore   Minimum Qmod score to use in motif finding
                           Default: 40.0
        * -o, --output     Output motifs csv file
          -x, --xml        Output motifs xml file

    reprocess      Reprocess gff file with motif information
      Usage: reprocess [options]
        Options:
          -c, --csv           Raw modifications.csv file
        * -f, --fasta         Reference fasta file
        * -g, --gff           original modifications.gff or .gff.gz file
              --minFraction   Only use motifs above this methylated fraction
                              Default: 0.75
        * -m, --motifs        motifs csv
        * -o, --output        Reprocessed modifications.gff file

Using the find command:

• Output csv file: This file follows the same format as the standard "Fields included in motif_summary.csv" described in the Methylome Analysis White Paper (https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Methylome-Analysis-Technical-Note).

• Output xml file: This is an output used by SMRT Portal and is not necessary using the command line. Simply do not include the -x command option. The information contained in this file is used to fill in the standard motif report table in SMRT Portal and is redundant with the CSV output file.

Using the reprocess command: (Reprocessing will update a modifications.gff file with information based on new Modification QV thresholds)

• Output gff file: The format of the output file is the same as the input file, and is described in the Methylome Analysis White Paper (https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Methylome-Analysis-White-Paper) under "Fields included in the modifications.gff file".

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