  Analyses of targeted genomic sequencing data from next-generation-sequencing
(NGS) technologies typically involves mapping reads to a reference sequence or
clustering reads. For a number of species a reference genome is not available
so the analyses of targeted sequencing data, for example polymorphic structural
variation caused by mobile elements is difficult; clustering methods are
preferred for such data analysis. Clustering of reads requires a clustering
threshold parameter, which is used to compare and group reads. However,
determining the optimal clustering threshold for a read dataset is challenging
because of different sequence composition, the number of sequences present, and
also the amount of sequencing errors in the dataset. High values of the
clustering threshold parameter can falsely inflate the number of recovered
genomic regions, while low values of clustering threshold can merge reads from
distinct regions into a single cluster. Thus, an algorithm that can empirically
determine clustering threshold is needed. We propose a pipeline for clustering
genomic sequences wherein the clustering threshold is empirically determined
from the NGS data. The optimal threshold is decided based on two internal
clustering measures which assess clusters for small intra-cluster diameters and
large inter-cluster distances. We evaluate the pipeline on two simulated
datasets derived from human genome sequence simulating different genomic
regions and sequencing depth. The total number of clusters obtained from our
pipeline is closer to the actual number of reference sequences when compared to
single round of clustering. Also, the number of clusters whose consensus
sequence matches a corresponding reference sequence is higher in our pipeline.
We observe that the presence of repeat regions affects clustering accuracy.
