  Metagenomics is the study of environments through genetic sampling of their
microbiota. Metagenomic studies produce large datasets that are estimated to
grow at a faster rate than the available computational capacity. A key step in
the study of metagenome data is sequence similarity searching which is
computationally intensive over large datasets. Tools such as BLAST require
large dedicated computing infrastructure to perform such analysis and may not
be available to every researcher.
  In this paper, we propose a novel approach called ScalLoPS that performs
searching on protein sequence datasets using LSH (Locality-Sensitive Hashing)
that is implemented using the MapReduce distributed framework. ScalLoPS is
designed to scale across computing resources sourced from cloud computing
providers. We present the design and implementation of ScalLoPS followed by
evaluation with datasets derived from both traditional as well as metagenomic
studies. Our experiments show that with this method approximates the quality of
BLAST results while improving the scalability of protein sequence search.
