Browsing by Subject "Bioinformatics."
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Item Automated sequence homology : using empirical correlations to create graph-based networks for the elucidation of protein relationships.(2008-10-02T18:47:42Z) Bush, Stephen J.; Baker, Erich J.; Biomedical Studies.; Baylor University. Institute of Biomedical Studies.Identification of sequence homology has presented a formidable obstacle despite significant increases in both technological capability and detailed knowledge of genomes and proteomes. While PSI-BLAST remains the popular tool for the job, it often returns inaccurate results with unacceptable levels of false positives. In order to increase the sensitivity and accuracy of homology finding, we have developed a software application called Automated Sequence Homology that bypasses these shortcomings and provides reliable and precise results. The system presented here is based upon the creation of a graph-based network highlighting the relational connections between proteins using empirical correlations. It takes a step back from PSI-BLAST to the acclaimed BLAST algorithm to create a sampling of the protein relational network.Item Identification of phenotypes in Caenorabhditis elegans on the basis of sequence similarity.(2009-06-02T17:59:06Z) Batra, Sushil.; Baker, Erich J.; Lee, Myeongwoo.; Biomedical Studies.; Baylor University. Institute of Biomedical Studies.In biomedical research, Caenorabhditis elegans is an ideal choice as experimental organism due to striking similarity with human genome and its distinct features such as short life span, small reproductive cycle, simple body plan, easily observable mutant phenotypes and ease of cultivation in laboratory. The 97 megabase genomic sequence of C. elegans comprises approximately 19,920 genes, of which about 2807 genes (14% of total genome) are uniquely associated with one or more RNAi phenotypes. The challenge to assign phenotypes to remaining 86% genes has incited development of new rapid techniques and computational tools. Objective of this project was to identify phenotypes in C. elegans on the basis of sequence similarity using bioinformatics techniques. To find similarity in genes, we used BLAST as computational tool and predicted the phenotypes. Bi-directional pair wise BLAST was performed on 2,807 unique genes (associated with known phenotypes) against 19,920 genes. As a result, 141 new genes (with unknown phenotype) were obtained which share high sequence similarity with known RNAi phenotype genes of 16 categories. In the present work, putative genes associated with two phenotypes, Ste (37 genes) and Unc (29 genes), were studied by RNA interference (RNAi) in laboratory. The outcome of these experiments assigned sterility phenotype to 8 new genes and uncoordinated phenotype to 12 new genes which were not linked with any phenotype in previous studies. These observations were further verified by silencing the response using reverse transcriptase polymerase chain reaction (RT-PCR) for Ste genes. Thus, bioinformatics techniques were successfully utilized in identification of phenotypes on the basis of sequence similarity with a relatively high success rate of 22% and 41% for sterility and uncoordinated phenotypes respectively. High success rate of this bioinformatics technique will allow researchers to focus their efforts on identifying particular phenotypes of interest and understanding various biological processes and elucidating the pathogenesis of diseases.