EUGENe: A Python toolkit for predictive analyses of regulatory sequences

 Deep learning (DL) has become a popular tool to study cis-regulatory element function. Yet efforts
to design software for DL analyses in genomics that are Findable, Accessible, Interoperable and
Reusable (FAIR) have fallen short of fully meeting these criteria.

Here we present EUGENe
(Elucidating the Utility of Genomic Elements with Neural Nets), a FAIR toolkit for the analysis of
labeled sets of nucleotide sequences with DL. EUGENe consists of a set of modules that
empower users to execute the key functionality of a DL workflow:

1) extracting, transforming and
loading sequence data from many common file formats,

2) instantiating, initializing and training
diverse model architectures, and

3) evaluating and interpreting model behavior.

We designed EUGENe to be simple; users can develop and deploy workflows on new or existing datasets with
functions that act on two customizable Python objects, annotated sequence data (SeqData) and
PyTorch models (BaseModel).

The modularity and simplicity of EUGENe also make it highly
extensible and we illustrate these principles through application of the toolkit to three predictive
modeling tasks. First, we train and compare a set of built-in models along with a custom
architecture for the accurate prediction of activities of plant promoters from STARR-seq data.
Next, we apply EUGENe to an RNA binding prediction task and showcase how seminal model
architectures can be retrained in EUGENe or imported from Kipoi. Finally, we train models to
classify transcription factor binding by wrapping functionality from Janngu, which can efficiently
extract sequences in BED file format from the human genome. We emphasize that the code used
in each use case is simple, readable, and well documented (https://eugene-
tools.readthedocs.io/en/latest/index.html).

We believe that EUGENe represents a springboard
toward a collaborative ecosystem for DL applications in genomics research. EUGENe is available
for download on GitHub (https://github.com/cartercompbio/EUGENe) along with several
introductory tutorials and for installation on PyPi (https://pypi.org/proje