Interpolative multidimensional scaling techniques for the identification of clusters in very large sequence sets Page: 4
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Hughes et al. BMC Bioinformatics 2012, 13(Suppl 2):S9
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Figure 4 100K Metagenomics sequences - Full MDS. Visualization of MDS and clustering results for 100,000 gene sequences from an
environmental sample of 16S rRNA. The many different genes are classified by a clustering algorithm and visualized by MDS dimension
although more significant changes in intra-cluster
arrangement can be seen.
Figure 7 shows the wall-clock time required to run each
complete pipeline discussed above. The full, non-inter-
polative calculation required about seven hours, while
the interpolative pipeline consisting of 50,000 in-sample
and 50,000 out-of-sample points required about three-
and-a-half hours. Finally, the interpolative calculation
with 10,000 in-sample and 90,000 out-of-sample
sequences completed in a little under an hour.
This study demonstrates the effectiveness of combining
the Needleman-Wunsch genetic distance algorithm with
Multidimensional Scaling (MDS) to enable visual identi-
fication of sequence clusters in a large sample of raw
reads from the 16S rRNA genome. In addition, the use
of interpolative MDS and the Twister Iterative MapRe-
duce runtime provides significant improvement in over-
all computational throughput while maintaining the
basic structure of the resultant sequence space. Further
investigation is needed to determine the optimal ratio of
in-sample to out-of-sample data set sizes in order to
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Hughes, Adam; Ruan, Yang; Ekanayake, Saliya; Bae, Seung-Hee; Dong, Qunfeng; Rho, Mina et al. Interpolative multidimensional scaling techniques for the identification of clusters in very large sequence sets, article, March 13, 2012; [London, United Kingdom]. (digital.library.unt.edu/ark:/67531/metadc78283/m1/4/: accessed January 21, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.