  In this paper we describe a new technique for the comparison of populations
of DNA strands. Comparison is vital to the study of ecological systems, at both
the micro and macro scales. Existing methods make use of DNA sequencing and
cloning, which can prove costly and time consuming, even with current
sequencing techniques. Our overall objective is to address questions such as:
(i) (Genome detection) Is a known genome sequence present, at least in part, in
an environmental sample? (ii) (Sequence query) Is a specific fragment sequence
present in a sample? (iii) (Similarity discovery) How similar in terms of
sequence content are two unsequenced samples? We propose a method involving
multiple filtering criteria that result in "pools" of DNA of high or very high
purity. Because our method is similar in spirit to hashing in computer science,
we call it DNA hash pooling. To illustrate this method, we describe protocols
using pairs of restriction enzymes. The in silico empirical results we present
reflect a sensitivity to experimental error. Our method will normally be
performed as a filtering step prior to sequencing in order to reduce the amount
of sequencing required (generally by a factor of 10 or more). Even as
sequencing becomes cheaper, an order of magnitude remains important.
