Next generation sequencing methodologies have resulted in an exponential increase in the amount of genomic sequence data available to researchers. Valuable tools in the initial analysis of such data for novel features are de novo techniques - methods which employ a minimum of comparative sequence information from known genomes. In this thesis I describe two heuristic algorithms for the rapid de novo analysis of genomic sequence data. The ﬁrst algorithm employs a multiple Fast Fourier Transform, mapped to two dimensional spaces. The resulting bitmap clearly illustrates periodic features of a genome including coding density. The compact representation allows mega base scales of genomic data to be rendered in a single bitmap. The second algorithm RTASSS, (RNA Template Assisted Secondary Structure Search) predicts potential members of RNA gene families that are related by similar secondary structure, but not necessarily conserved sequence. RTASSS has the ability to ﬁnd candidate structures similar to a given template structure without the use of sequence homology. Both algorithms have a linear complexity.