![](bg6.png)
ms use an FM-index instead of suffix trees to improve
the memory requirements. In future versions, our al-
gorithm could implement a more memory-friendly in-
dex structure. A possibility to increase accuracy is
changing the anchors or seeds from MEMs to max-
imal unique matches as in
MUMmer
(Kurtz et al.,
2004) or using spaced or inexact matches.
5 CONCLUSIONS
We presented an algorithm for mapping large reads
to a reference genome which is fast and accurate in
finding an optimal local alignment. The algorithm is
easy to understand and has few parameters. Our first
results show that the algorithm is able to map reads
with insertions, deletions and mutations and with a
length of several hundreds of base pairs successfully
to a reference sequence.
The algorithm is more accurate given longer
queries with lower error rates. The main algorithm
takes several parameters which can be tuned. These
are: the expected edit distance and the cost parameters
for the Needleman-Wunsch algorithm. The expected
edit distance can be approximated and allows some
deviation from the real edit distance. Better results
are obtained with an overestimate than with an un-
derestimate. The algorithm is devised in such a way
that an alignment is always found even if it is subop-
timal. Even if reads contain insertions, deletions or
mutations in the first or last parts of the query, our
algorithm was able to find an optimal alignment.
ACKNOWLEDGEMENTS
The research of MV is funded by a grant from the
N2N-MRP at Ghent University.
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