@article { ISI:000285027100017, title = {Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence}, journal = {Journal of Biosciences}, volume = {35}, number = {4}, year = {2010}, month = {DEC}, pages = {617-627}, publisher = {INDIAN ACAD SCIENCES}, address = {C V RAMAN AVENUE, SADASHIVANAGAR, P B \#8005, BANGALORE 560 080, INDIA}, abstract = {

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.

}, keywords = {Curvilinear component analysis, Principal component analysis, self-organizing maps}, issn = {0250-5991}, doi = {10.1007/s12038-010-0070-y}, author = {Raje, D. V. and Purohit, H. J. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000288661500011, title = {Association between chemical and genetic variation in calophyllum inophyllum, a medicinally important tree of the Western Ghats of India}, journal = {Plant Systematics and Evolution}, volume = {292}, number = {3-4}, year = {2011}, month = {APR}, pages = {257-265}, publisher = {SPRINGER WIEN}, address = {SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA}, abstract = {

The objective of the present work is to study the chemical variation in Calophyllum inophyllum growing along the Western Ghats of India. Contents of dipyranocoumarins (inophyllums) in C. inophyllum were determined to assess whether they could be used as a taxonomic marker for C. inophyllum. This study also aims to establish inter simple sequence repeat (ISSR) markers that can be used to study genetic variation within the species and explore correlation between ISSR and chemical markers. The contents of dipyranocoumarins were estimated in seeds collected from 20 locations. Leaves from plants at the same 20 locations were assayed for ISSR variation. A dendrogram based on Nei{\textquoteright}s genetic distance as well as principal component analysis based on dipyranocoumarins and ISSR variation clustered plants from these 20 locations into three groups that indicated close relationship among ISSR, dipyranocoumarins contents, and geographical position (variation) of locations. Based on this study, two locations of elite plants were identified.

}, keywords = {Calophyllum inophyllum, Dendrogram, dipyranocoumarins, HPLC, ISSR, Principal component analysis, Western Ghats of India}, issn = {0378-2697}, doi = {10.1007/s00606-010-0409-8}, author = {Pawar, Kiran D. and Joshi, Swati P. and Thengane, Shubhada Ratnakar} } @article { ISI:000304621400045, title = {Dimensionality reduction in computational demarcation of protein tertiary structures}, journal = {Journal of Molecular Modeling}, volume = {18}, number = {6}, year = {2012}, month = {JUN}, pages = {2741-2754}, publisher = {SPRINGER}, address = {233 SPRING ST, NEW YORK, NY 10013 USA}, abstract = {

Predictive classification of major structural families and fold types of proteins is investigated deploying logistic regression. Only five to seven dimensional quantitative feature vector representations of tertiary structures are found adequate. Results for benchmark sample of non-homologous proteins from SCOP database are presented. Importance of this work as compared to homology modeling and best-known quantitative approaches is highlighted.

}, keywords = {Logistic regression, Principal component analysis, Protein structural classes, Quantitative features of tertiary folds, SCOP database}, issn = {1610-2940}, doi = {10.1007/s00894-011-1223-0}, author = {Joshi, Rajani R. and Panigrahi, Priyabrata R. and Patil, Reshma N.} }