Biomedical subjects

R Sasikumar

Publications and source records attributed to R Sasikumar.

2 recordsLinked to original sources

PubMed2005

Genetic diversity of UPASI tea clones (Camellia sinensis (L.) O. Kuntze) on the basis of total catechins and their fractions.

Tea leaf catechins and the ratio of dihydroxylated to trihydroxylated catechin fractions were analysed to identify the genetic diversity of 26 UPASI released tea clones. Principal component analysis (PCA) based on regression factor separated tea clones into five groups according to their jats (Jats are region based rays for e.g., Assam, China and Cambod origin) as well as their quality constituents (such as total polyphenols, total catechins, amino acids in the green leaves and liquor characteristics of black tea), particularly the catechins. Group 1 represented medium quality (quality of the final produce) clones, such as UPASI-10, UPASI-12 and UPASI-15 and drought tolerant clones like UPASI-1, UPASI-2, UPASI-9 and UPASI-10. Group 2 contained purely "China" cultivars while group 3 possessed high quality tea cultivars. "Assam" (group 5) teas had the lowest ratio of dihydroxylated to trihydroxylated catechin fractions (1:4) than the "Chinery" (group 2) teas (1:5). This biochemical differentiation indicated that there is a vast genetic diversity in UPASI released tea clones in terms of catechin fractions, even though the majority of them were selected from one tea estate located in the Nilgiris.

Camellia sinensis↗

PubMed2004

A fuzzy guided genetic algorithm for operon prediction.

MOTIVATION: The operon structure of the prokaryotic genome is a critical input for the reconstruction of regulatory networks at the whole genome level. As experimental methods for the detection of operons are difficult and time-consuming, efforts are being put into developing computational methods that can use available biological information to predict operons. METHOD: A genetic algorithm is developed to evolve a starting population of putative operon maps of the genome into progressively better predictions. Fuzzy scoring functions based on multiple criteria are used for assessing the 'fitness' of the newly evolved operon maps and guiding their evolution. RESULTS: The algorithm organizes the whole genome into operons. The fuzzy guided genetic algorithm-based approach makes it possible to use diverse biological information like genome sequence data, functional annotations and conservation across multiple genomes, to guide the organization process. This approach does not require any prior training with experimental operons. The predictions from this algorithm for Escherchia coli K12 and Bacillus subtilis are evaluated against experimentally discovered operons for these organisms. The accuracy of the method is evaluated using an ROC (receiver operating characteristic) analysis. The area under the ROC curve is around 0.9, which indicates excellent accuracy. CONTACT: roschen_csir@rediffmail.com.

Algorithms↗