Zam works both on developing efficient computational and statistical methods, and their applications to pathogens. His approach is to develop algorithms and data structures for representing and detecting simple and complex genetic variation without using a reference genome - in essence a sample is viewed in the context of all other genomes in that species, rather than comparing with one exemplar (the "reference"). One strength of this unbiased approach is that highly diverse areas of a genome can be made accessible.
Several collaborations have now been formed to apply these methods to pathogens. Zam and the CGGH are working together on Plasmodium falciparum. The parasite’s genome has an extremely high repeat content, as well as its own non-allelic recombination and mutation processes which occur in the subtelomeric regions which contain extremely important antigen/virulence genes. An additional complexity is that in some parts of the world it is very common to have mixed infections. Assembly approaches are proving tremendously powerful, especially as for many surface antigen genes different strains can have astonishingly diverged genomes. These methods are for the first time allowing us to understand variation at a number of critical genes.