  In this work we propose a method to compute continuous embeddings for kmers
from raw RNA-seq data, without the need for alignment to a reference genome.
The approach uses an RNN to transform kmers of the RNA-seq reads into a 2
dimensional representation that is used to predict abundance of each kmer. We
report that our model captures information of both DNA sequence similarity as
well as DNA sequence abundance in the embedding latent space, that we call the
Latent Transcriptome. We confirm the quality of these vectors by comparing them
to known gene sub-structures and report that the latent space recovers exon
information from raw RNA-Seq data from acute myeloid leukemia patients.
Furthermore we show that this latent space allows the detection of genomic
abnormalities such as translocations as well as patient-specific mutations,
making this representation space both useful for visualization as well as
analysis.
