Interpolative multidimensional scaling techniques for the identification of clusters in very large sequence sets Page: 3
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Hughes et al. BMC Bioinformatics 2012, 13(Suppl 2):S9
enables us to target large, Linux-based compute clusters
. This scaled-up pipeline is shown in Figure 3.
Results and discussion
Full calculation on entire data set
Figure 4 shows the results of running full Needleman-
Wunsch (NW) and Multidimensional Scaling (MDS)
calculations on a set of 100,000 raw 16S rRNA sequence
reads. The results of this calculation fit well with the
expected groupings for this genome [7,8]. The initial
clustering calculation colors the predicted sequences in
a given grouping, while the MDS calculation produces
Cartesian coordinates for each sequence. As Figure 4
shows, the spatial and colored results correspond to the
same sequences, indicating that the combination of NW
and MDS produce reasonable sequence clusters.
Interpolation: 50000 in-sample sequences, 50000 out-of-
Figure 5 shows the results of running interpolative MDS
and NW on the same 100,000 sequences, with 50,000 in-
sample and 50,000 out-of-sample data points. The basic
structure observed in this case is similar to that seen in
the full calculation discussed above. Some slight differ-
ences within individual clusters are noted, but the major
sequence groupings are intact.
Interpolation: 10000 in-sample sequences, 90000 out-of-
Figure 6 shows the results of running interpolative MDS
and NW on the same 100,000 sequences, with 10,000
in-sample and 90,000 out-of-sample data points. Once
again, the same basic clustering structure is observed,
512 2861164 67
/88 62 40 343
34 608 486 389
887 661 539 22
x, y, z
x, y, z
Figure 3 Scaled-up computational pipeline for sequence clustering. As with the basic pipeline, the scaled-up workflow begins with a raw
sequence file. Before calculating genetic distances, the file is divided into in-sample and out-of-sample sets for use in Interpolative MDS. Full
MDS and NW distance calculations on the in-sample data yield trained distances, which are used to interpolate the remaining distances. The
interpolation step includes on-the-fly pairwise NW distance calculation. The overall complexity of the pipeline is reduced from O(N2) for the basic
pipeline to O(M2 + (N-M)*M) for the pipeline with interpolation, where N is the size of the original sequence set and M is the size of the in-
sample data. To enhance computational job management and resource availability, all computational portions of the depicted pipeline were
implemented using the Twister Iterative Map Reduce runtime.
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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/m1/3/: accessed September 25, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.