Due to the NGS data deluge, sequence mapping has become an intensive
task that, depending on the experiment, may demand high amounts of computing
power or memory capacity.
On the one hand, GPGPU architectures have become a cost-effective solution that
outperforms common processors in specific tasks. On the other hand, out-of-core
implementations allow to directly access data from secondary memory, which may
be useful when mapping against big indexes in systems with low memory
configurations. In this paper we discuss the implementation of backward search
methods for inexact mapping in these two different study cases.
A hybrid CPU-GPU implementation of a backward search algorithm capable of obtaining the pair-ends
and the one error mappings of a read has been developed. This implementation can be used to increase
the sensitivity and reduce the number of reads to be analysed with a dynamic programming approach.
Also, a CPU out-of-core index using MMAP has been studied (provided by csalib). Such index can be
used in memory limited scenarios, in which the time of loading many different big genomes into
memory is greater than the time needed to map the reads.