Skip to content

Latest commit

 

History

History
59 lines (34 loc) · 2.54 KB

README.md

File metadata and controls

59 lines (34 loc) · 2.54 KB

Metamotif

Metamotif is a tool for extracting informative subsequences from a set of sequences with corresponding postion-wise importance scores. Metamotif strives to be simple, easy to use and robust towards hyperparameters.

💻 Installation

To install the latest stabel version, run the following command:

pip install git+https://github.com/mhorlacher/metamotif.git

To install a specific version (e.g. 0.4.0), run this command:

pip install git+https://github.com/mhorlacher/[email protected]

   

Searching for subsequences

The search for informative subsequences is invoked via metamotif search, which takes as input

  1. a list of sequences in FASTA format
  2. a numpy array of shape (n_sequences, len_sequences). (Note that variable-length sequences are currently not supported.)

An example is given below.

metamotif search examples/example.QKI/QKI_HepG2.fasta examples/example.QKI/QKI_HepG2.scores.npy -o QKI.kmers.tsv

Identified subsequences are written to a TSV line-by-line. See QKI.kmers.tsv for an example.

Customizing metamotif search

metamotif search exposes a number of hyperparameters that can be specified to tune the search processes for a specific task. Hyperparameters may be specified by a config.gin file, which is provided via metamotif search --config config.gin. See the default-search.config.gin for an example.

The following hyperparameters may be specified:

  • search.seed_size: Size of the initial seed sequence (default = 2).
  • search.max_size: Maximum size of the extracted subsequences (default = 20).
  • search.sig_p: Significance threshold for subsequences (default = 0.01).
  • search.extend_flanks: Size by which significant subsequences are extended up-and downstream (default = 0).

   

🧪 How it works

Put simply, for a given sequence with corresponding importance scores, metamotif search identifies subsequences which are significantly more informative that one would expect by chance. To this end, the algorithms starts with a short subsequence (default search.seed_size = 2) and iteratively extends it until its cumulative importance becomes non-significant. The process continues until all significant informative subsequences of maximum length are identified. This way, metamotif search can identify a variable number of subsequences with variable length.

RBPNet

⚠️ Metamotif is under active developement and its API might change at any time.