  We describe an algorithm to find maximal exact matches (MEMs) among HiFi
reads with homopolymer errors. The main novelty in our work is that we resort
to run-length compression to help deal with errors. Our method receives as
input a run-length-encoded string collection containing the HiFi reads along
with their reverse complements. Subsequently, it splits the encoding into two
arrays, one storing the sequence of symbols for equal-symbol runs and another
storing the run lengths. The purpose of the split is to get the BWT of the run
symbols and reorder their lengths accordingly. We show that this special BWT,
as it encodes the HiFi reads and their reverse complements, supports
bi-directional queries for the HiFi reads. Then, we propose a variation of the
MEM algorithm of Belazzougui et al. (2013) that exploits the run-length
encoding and the implicit bi-directional property of our BWT to compute
approximate MEMs. Concretely, if the algorithm finds that two substrings, $a_1
\ldots a_p$ and $b_1 \ldots b_p$, have a MEM, then it reports the MEM only if
their corresponding length sequences, $\ell^{a}_1 \ldots \ell^{a}_p$ and
$\ell^{b}_1 \ldots \ell^{b}_p$, do not differ beyond an input threshold. We use
a simple metric to calculate the similarity of the length sequences that we
call the {\em run-length excess}. Our technique facilitates the detection of
MEMs with homopolymer errors as it does not require dynamic programming to find
approximate matches where the only edits are the lengths of the equal-symbol
runs. Finally, we present a method that relies on a geometric data structure to
report the text occurrences of the MEMs detected by our algorithm.
