Phylogenetics analysis of TP53 gene in humans and its use in biosensors for breast cancer diagnosis

Biosensors are small devices that use biological reactions to detect target analytes. Such devices combine a biological component with a physical transducer, which converts bio-recognition processes into measurable signals. Its use brings a number of advantages, as they are highly sensitive and selective, relatively easy in terms of development, as well as accessible and ready to use. Biosensors can be of direct detection, using a non-catalytic ligand, such as cell receptors and antibodies, or indirect detection, in which there is the use of fluorescently marked antibodies or catalytic elements, such as enzymes. They also appear as bio-affinity devices, depending only on the selective binding of the target analyte to the ligative attached to the surface (e.g., oligonucleotide probe). The objectives were to evaluate the levels of genetic diversity existing in fragments of the TP53 gene deposited in molecular databases and to study its viability as a biosensor in the detection of breast cancer. The methodology used was to recover and analyze 301 sequences of a fragment of the TP53 gene of humans from GENBANK, which, after being aligned with the MEGA software version 6.06, were tested for the phylogenetic signal using TREE-PUZZLE 5.2. Trees of maximum likelihood were generated through PAUP version 4.0b10 and the consistency of the branches was verified with the bootstrap test with 1000 pseudo-replications. After aligning, 783 of the 791 sites remained conserved. The maximum likelihood had a slight manifestation since the gamma distribution used 05 categories + G for the evolutionary rates between sites with (0.90 0.96, 1.00, 1.04 and 1.10 substitutions per site). To estimate ML values, a tree topology was automatically computed with a maximum Log of −1058,195 for this calculation. All positions containing missing gaps or data were deleted, leaving a total of 755 sites in the final dataset. The evolutionary history was represented by consensus trees generated by 500 replications, which according to neighbor-join and BioNJ algorithms set up a matrix with minimal distances between haplotypes, corroborating the high degree of conservation for the TP53 gene. GENE TP53 seems to be a strong candidate in the construction of Biosensors for breast cancer diagnosis in human populations.

Optimized Cost Model for k-NN Queries in R*-Trees over Random Distribution

Efficient storage and retrieval of multidimensional data in large volumes has become one of the key issues in the design and implementation of commercial and application software. The kind of queries posted on such data is also multifarious. Nearest neighbor queries are one such category and have more significance in GIS type of application. R-tree and its sequel are data partitioned hierarchical multidimensional indexing structures that help in this purpose. Today’s research has turned towards the development of powerful analytical method to predict the performance of such indexing structures such as for varies categories of queries such as range, nearest neighbor, join, etc .This paper focuses on performance of R*-tree for k nearest neighbor (kNN) queries. While general approaches are available in literature that works better for larger k over uniform data, few have explored the impact of small values of k. This paper proposes improved performance analysis model for kNN query for small k over random data. The results are tabulated and compared with existing models, the proposed model out performs the existing models in a significant way for small k