Abstract: Studying DNA (deoxyribonucleic acid) sequence is useful in biological processes and it is applied in the fields such as diagnostic and forensic research. DNA is the hereditary information in human and almost all other organisms. It is passed to their generations. Earlier stage detection of defective DNA sequence may lead to many developments in the field of Bioinformatics. Nowadays various tedious techniques are used to identify defective DNA. The proposed work is to analyze and identify the cancer-causing DNA motif in a given sequence. Initially the human DNA sequence is separated as k-mers using k-mer separation rule. The separated k-mers are clustered using Self Organizing Map (SOM). Using Levenshtein distance measure, cancer associated DNA motif is identified from the k-mer clusters. Experimental results of this work indicate the presence or absence of cancer causing DNA motif. If the cancer associated DNA motif is found in DNA, it is declared as the cancer disease causing DNA sequence. Otherwise the input human DNA is declared as normal sequence. Finally, elapsed time is calculated for finding the presence of cancer causing DNA motif using clustering formation. It is compared with normal process of finding cancer causing DNA motif. Locating cancer associated motif is easier in cluster formation process than the other one. The proposed work will be an initiative aid for finding genetic disease related research.
Abstract: This study presents a conformational model of the helical structures of globular protein particularly ferritin in the framework of white noise path integral formulation by using Associated Legendre functions, Bessel and convolution of Bessel and trigonometric functions as modulating functions. The model incorporates chirality features of proteins and their helix-turn-helix sequence structural motif.
Abstract: GRF, Growth regulating factor, genes encode a novel
class of plant-specific transcription factors. The GRF proteins play a
role in the regulation of cell numbers in young and growing tissues
and may act as transcription activations in growth and development
of plants. Identification of GRF genes and their expression are
important in plants to performance of the growth and development of
various organs. In this study, to better understanding the structural
and functional differences of GRFs family, 45 GRF proteins
sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H.
vulgare and S. bicolor, have been collected and analyzed through
bioinformatics data mining. As a result, in secondary structure of
GRFs, the number of alpha helices was more than beta sheets and in
all of them QLQ domains were completely in the biggest alpha helix.
In all GRFs, QLQ and WRC domains were completely protected
except in AtGRF9. These proteins have no trans-membrane domain
and due to have nuclear localization signals act in nuclear and they
are component of unstable proteins in the test tube.