PHASTMOTIF(1)

✨ PHASTMOTIF 💅

okay bestie, welcome to phastMotif — the tool that predicts motifs from a set of multiple alignments 💖

🌸 NAME 🌸

phastMotif — Predicts motifs from a set of multiple alignments.

🦋 DESCRIPTION 🦋

Predicts motifs from a set of multiple alignments. Uses an EM algorithm similar to that of MEME, but a motif is defined by phylogenetic models rather than multinomial distributions. The specified multiple alignments may actually be single sequences (see -m). Various parameters control the strategy for initialization (see below). Currently, the F81 substitution model is assumed.

🎀 USAGE 🎀

phastMotif [-t <treefile>] [OPTIONS] <msa_list>

👑 OPTIONS 👑

• -t <file> — (Required unless -m or -p) Use specified tree topology for all phylogenetic models (Newick format).
• -i <fmt> — Input format for alignment. May be FASTA, PHYLIP, MPM, SS, or MAF (default FASTA).
• -b <file> — Read background model from specified file (.mod format).

By default, the background model is estimated in a preprocessing step, by pooling all data.

• -s — Estimate a separate background model for each multiple alignment. (Not yet implemented. 🙈)
• -k <size> — Learn motifs of the specified size (default is 10).
• -B <n> — Report best <n> motifs (default 3).
• -m — MEME mode. Use multinomial rather than phylogenetic models. Causes multiple alignments to be ignored — any gaps are discarded and all sequences are assumed independent.
• -d <+lst> — Use the discriminative training method of Segal et al. (RECOMB'02), rather than EM. The specified list

should contain the filenames from msa_list that are to be considered positive examples (containing the desired motif); all others will be considered negative examples. Can be used with or without -m.

• -p — Use "profile" models rather than phylogenetic models (characters in each alignment column assumed independent). The resulting model is a hybrid of the full model and MEME's model. Essentially, it uses the multiple alignments but not the phylogeny. NOT YET IMPLEMENTED.
• -n <n> — Perform <n> random restarts and report the motif with highest likelihood. Default number is 10. Ignored with -I, -P, and -R unless -S is specified (see below).
• -I <mlst> — Run the algorithm after a "soft" initialization with

each of the consensus sequences in the specified list. At each position, <pc> pseudocounts (see -c) are given to the consensus base and 1 pseudocount to all other bases. Each string must have length at most equal to the size of the motif. If shorter, it is used as a "seed" for a motif, with flanking positions treated as wildcards.

• -P <x,y> — Initialize with the x most prevalent y-tuples. A soft initialization is performed, as above. If y is less than the motif size, y-tuples are used as a "seed" for a motif, as above.
• -R <x,y> — Initialize with a random sample of x y-tuples. A soft initialization is performed, as above. If y is less than the motif size, y-tuples are used as a "seed" for a motif, as above.
• -w <n> — (for use with -I, -P, -R) Winnow initialization sequences to the top <n> based on the unmaximized likelihood.
• -c <pc> — (for use with -I, -P, -R) Number of pseudocounts for consensus bases (default 5).
• -S — (for use with -I, -P, -R) Instead of doing a deterministic initialization based on a consensus sequence, sample parameters from a Dirichlet distribution defined by the pseudocounts (see -c). In this case, random restarts are performed, as specified by -n.
• -o <pref> — Use the specified prefix for all output files (dflt. "phastm").
• -H — Produce HTML formatted output, in addition to ordinary output. One file is produced per predicted motif, as well as a single HTML-formatted summary file.
• -D — Produce a BED file with predicted motifs, for use in the UCSC browser. Currently, sequence names must be formatted such as "chr10:102553847-102554897+", with the final '+' or '-' indicating strand.
• -x — (For use with -H or -D) Suppress ordinary output to stdout.
• -h — Print this help message.

that's the whole vibe, bestie 💖 this is phastMotif version 1.4, dated May 2016 — go forth and find those motifs, you absolute icon 👑✨🦋🎀