02425nas a2200229   4500008004100000022001400041245011700055210006900172260010200241300001200343490000700355520151100362653003501873653003301908653002501941100001701966700002001983700002202003700001802025700002102043856013102064       2010                        eng d  a0250-599100aSelf-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence0 aSelforganizing maps a tool to ascertain taxonomic relatedness ba  aC V RAMAN AVENUE, SADASHIVANAGAR, P B \#8005, BANGALORE 560 080, INDIAbINDIAN ACAD SCIENCEScDEC  a617-6270 v353 a<p>Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.</p>10aCurvilinear component analysis10aPrincipal component analysis10aself-organizing maps1 aRaje, D., V.1 aPurohit, H., J.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/self-organizing-maps-tool-ascertain-taxonomic-relatedness-based-features-derived-16s-rdna-0