SeREGen 0.1.2
Automated, cross-platform Representative Embedding generation.
Requires Python: >=3.9

# SeqRep

**This tool is still in active development.** Install with `pip install SeREGen`.

SeREGen is a biological sequence representation tool that will be available as a Python package. This is a computationally intensive analysis methodology, and best results are achieved when running on a modern computer with multiple CPU cores and a powerful GPU. The central idea is to train a machine learning model to convert individual DNA sequences into n-dimensional points, such that the distances between any two points in space is correlated with the true dissimilarity of those points' parent DNA sequences.

Currently, a preliminary library implementation of this methodology is offered, with goals of being easy-to-use, efficient, and highly extensible. Some knowledge of machine learning and TensorFlow is helpful, but not required to use this library. A copy of SILVA's 16S database is included, along with several Jupyter notebooks which show this tool's applications.

## Tutorial

## FAQ

**Q: Distance vs Embedding Distance?**

A: Generally, Distance refers to the distance between model inputs (Euclidean between k-mer count arrays, Levenshtein between string sequences, etc). Embedding distance is the distance metric used in the embedding space, trained to match with the true Distance. This is 'euclidean' or 'hyperbolic'.

**Q: I'm getting this error when calling fit: `ValueError: tried to create variables on non-first call`.**

A: If you attempted to run a text_input model in the past, you need to reinitialize the model before running again.

---

**Below is the legacy documentation. This is very out of date.**

Inside the SeqRep library are the following modules:

- dataset_builder.py
    * DatasetBuilder
        + Imports FASTA data into a custom Dataset object and parses out taxonomic information from FASTA headers automatically. Header parsing is designed to be extensible to additional formats.
    * Dataset
        + Builds on top of the pandas DataFrame to allow easy importing of FASTA data, parsing out of taxonomic information, and dataset filtering.
        + Taxonomic information can be added after object creation if the source is something other than the FASTA headers.
        + Integrates with visualization module to simplify plot generation.
- comparative_encoder.py
    * ComparativeEncoder
        + Converts a TensorFlow encoder model into a comparative encoder model. Takes a Distance object as an argument.
        + Designed to be as generic and extensible as possible, and can function on any input shape and with any output size. Encoder model can either be built using included utilities or programmed from scratch and passed as an argument.
- encoders.py
    * ModelBuilder
        + Helpful class that can reduce the difficulty of designing an encoder model.
- distance.py
    * EuclideanDistance
        + Currently the only available distance metric, implements a simple euclidean distance measure between the inputs and normalizes that distance as a z-score.
        + Works best when the distribution of distances between randomly sampled points in the dataset is approximately normal (as in the SILVA dataset).
- visualize.py
    * repr_scatterplot
        + The most basic scatterplot function that wraps matplotlib and plots a scatterplot of sequence representations.
    * reprs_by_taxa
        + An incredibly useful function that can filter down input arrays with a boolean mask and plot all points in each value of a given taxonomic level, colored by taxonomic classification. Takes arguments as: (sequence representations, Dataset object, string taxonomic level to target, plot title, alpha: optional alpha value for points, filter: optional minimum number of sequences in a taxa for that taxa to be plotted, savepath: optional save path for the generated figure, mask: optional boolean mask to apply before plotting).

Many of these files have reasonable internal documentation, so it's worth looking at that for assistance as well.