  Recently several deep learning models have been used for DNA sequence based
classification tasks. Often such tasks require long and variable length DNA
sequences in the input. In this work, we use a sequence-to-sequence autoencoder
model to learn a latent representation of a fixed dimension for long and
variable length DNA sequences in an unsupervised manner. We evaluate both
quantitatively and qualitatively the learned latent representation for a
supervised task of splice site classification. The quantitative evaluation is
done under two different settings. Our experiments show that these
representations can be used as features or priors in closely related tasks such
as splice site classification. Further, in our qualitative analysis, we use a
model attribution technique Integrated Gradients to infer significant sequence
signatures influencing the classification accuracy. We show the identified
splice signatures resemble well with the existing knowledge.
