  The past few decades have seen great leaps in technologies to analyze cells
and tissues. Omics methods in particular now allow us unprecedented access to
their the molecular composition where the base-level resolution of transcripts
and their numbers can be determined at a single cell level. Existing methods to
analyze the resulting data make use of the count data while discarding the
information present in the sequences themselves. In this paper we used a
maximum entropy approach to develop a method to analyze RNA-seq data using both
the sequence and count information. By mapping sequences to vectors of spins
and defining an energy function on them, we were able to identify specific
states in a biological process using mean energies and their associated
Boltzmann-probabilities. This approach opens up new avenues in the quantitative
analysis of -omics data and analysis of biological function.
