  Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next
Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of
long and noisy reads demanding cutting edge research to get most out of it. To
deal with the high error probability of SMRT data, a novel contextual Locality
Sensitive Hashing (conLSH) based algorithm is proposed in this article, which
can effectively align the noisy SMRT reads to the reference genome. Here,
sequences are hashed together based not only on their closeness, but also on
similarity of context. The algorithm has $\mathcal{O}(n^{\rho+1})$ space
requirement, where $n$ is the number of sequences in the corpus and $\rho$ is a
constant. The indexing time and querying time are bounded by $\mathcal{O}(
\frac{n^{\rho+1} \cdot \ln n}{\ln \frac{1}{P_2}})$ and $\mathcal{O}(n^\rho)$
respectively, where $P_2 > 0$, is a probability value. This algorithm is
particularly useful for retrieving similar sequences, a widely used task in
biology. The proposed conLSH based aligner is compared with rHAT, popularly
used for aligning SMRT reads, and is found to comprehensively beat it in speed
as well as in memory requirements. In particular, it takes approximately
$24.2\%$ less processing time, while saving about $70.3\%$ in peak memory
requirement for H.sapiens PacBio dataset.
