Project titles for:
Project titles for: 2007-09
- Comparison of software for Analysis of Molecular Marker Data
- In silico Prediction of miRNA in Rice Genome
- Web Tools for Bioinformatics
- In silico Structure Prediction of enzyme Arsenite methyltransferase
- Comparison of various software for QTL Analysis
- QSAR studies of Tuberculosis Inhibitors
- Determination of common transcription factor binding site in promoter region of abiotic stress resistance gene in rice
- SNP Mining in Rice Genome
- Protein Modelling of Betaine Aldehyde Dehydrogenase-2 in Rice
- Data Mining of Chemical Substructures for Biological Efficacy
- Plant Disease Database mined from PubMed
- Comparison of Protein Structure Prediction Methods
- Virtual High Throughput Screening for Influenza Virus Inhibitors
- Combinatorial Libraries for Screening against Tuberculosis Inhibitors
Quantitative traits are defined as traits that have a continous phenotypic distribution. Variances of these traits are often controlled by the segregation of many loci, called quantitative trait loci (QTL). A QTL is the location of a gene that affects a trait that is measured on a quantitative scale. QTl are assigned to chromosome locations based on the positions of markers on a linkage map.In a recent issue of Science, Lander and Weinberg stated that “without doubt, the greatest achievement in biology over the past millennium has been the elucidation of the mechanism of heredity”. The genetic dissection of quantitative phenotypes into Mendelian – like components, or quantitative trait loci (QTL) analysis, has provided significant insight into how complex traits are regulated and controlled. QTL analysis is the phrase used currently to study the genetic variation, to locate the genes responsible and to explore their effects and interactions. QTL analysis has become an important tool to allow biologist to dissect the genetics of complex characters. The goal of QTL analysis is to identify the genes action, gene interaction, breeding value estimation etc. It plays an imp role in marker assisted selection breeding and human disease diagnosis. MAS can reduce breeding population sizes, continuous recurrent testing and the time required to develop a superior line. QTL information can also be used as a basis for germplasm characterization and conservation. During my course of investigation, three software’s were used to detect QTL.The QTLCartographer was based on ML-CIM and QTLmapper and QTL network were based on LS-CIM. Least square methods are much easier and faster to compute than Maximum likelihood method and allow more straightforward modeling of a large variety of effects, mating designs and generations with loss of estimation, accuracy and precision. By comparing the results of three software’s we ms conclude that, the comman QTLs were more stable, reliable and validate with Gramene database.