Sense-antisense transcripts (SATs) are messenger RNA (mRNA) transcripts that have regions that are complementary to regions of other mRNA transcripts. SATs may play an influential role in the regulation of gene expression. One evolutionary event that has had a dramatic impact on many genomes is the widespread dispersal of repetitive sequences which includes transposable elements (TEs) as well as simple and tandem repeats. Approximately 45% of the human and 37.5% of the mouse genomes are composed of repeats derived from transposable elements. A group of SATs was identified as resulting from transposable elements integrating into the coding strand of some genes and into the template strand of the coding region of other genes. These SATs may add to the complexity of an organism's regulatory network or they may be the result of rather recent TE activities yet to succumb to sequence divergence. The human, mouse and bovine genomes were analyzed for SATs using publicly available datasets and bioinformatics analysis tools. Each sense-antisense binding region (SABR) was aligned to transposable elements from the RepBase repeat database revealing many SABRs containing TE sequence in a large portion of the sequence. A Gene Ontology analysis on subsets of the data showed enrichments for the functional category of "DNA repair" and the component category "cytoplasm". An analysis of the substitution rates in human and mouse across the 3' UTRs of transcripts containing SABRs at the 5' end of their 3' UTRs showed that the substitution rate in the region of the SABR was lower than compared to the beginning of the 3' UTR. The lower percent GC composition found at the 3' end of the 3' UTRs could be attributed to conserved poly-A signals in this region.

Prediction of functions of genes in a genome is a key step in all genome sequencing projects. Sequences that carry out important functions are likely to be conserved between evolutionarily distant species and can be identified using cross-species comparisons. In the absence of completed genomes and the accompanying high-quality annotations, expressed sequence tags (ESTs) from random cDNA clones are the primary tools for functional genomics. EST datasets are fragmented and redundant, necessitating clustering of ESTs into groups that are likely to have been derived from the same genes. EST clustering helps reduce the search space for sequence homology searching and improves the accuracy of function predictions using EST datasets. This dissertation is a case study that describes clustering of Bos taurus and Sus scrofa EST datasets, and utilizes the EST clusters to make computational function predictions using a comparative genomics approach. We used a novel EST clustering method, TAMUClust, to cluster bovine ESTs and compare its performance to the bovine EST clusters from TIGR Gene Indices (TGI) by using bovine ESTs aligned to the bovine genome assembly as a gold standard. This comparison study reveals that TAMUClust and TGI are similar in performance. Comparisons of TAMUClust and TGI with predicted bovine gene models reveal that both datasets are similar in transcript coverage. We describe here the design and implementation of an annotation pipeline for predicting functions of the Bos taurus (cattle) and Sus scrofa (pig) transcriptomes. EST datasets were clustered into gene families using Ensembl protein family clusters as a framework. Following clustering, the EST consensus sequences were assigned predicted function by transferring annotations of the Ensembl vertebrate protein(s) they are grouped to after sequence homology searches and phylogenetic analysis. The annotations benefit the livestock community by helping narrow down the gamut of direct experiments needed to verify function.

The process of bioinformatics computing consumes huge computing resources, in situation of difficulty in improvement of algorithm and high cost of mainframe, many scholars choice distributed computing as an approach for reducing computing time. When using distributed computing for bioinformatics, how to find a properly tasks allocation strategy among different computing nodes to keep load-balancing is an important issue. By adopting multi-agent system as a tool, system developer can design tasks allocation strategies through intuitional view and keep load-balancing among computing nodes. The purpose of our research work is using multi-agent system as an underlying tool to develop a distributed computing environment and assist scholars in solving bioinformatics computing problem, In comparison with public computing projects such as BOINC, our research work focuses on utilizing computing nodes deployed inside organization and connected by local area network.

Thesis (Ph. D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 105 pages. Includes vita. Includes bibliographical references.

Thesis (Ph.D.)--University of Texas at Arlington, 2008.

Thesis (Ph. D.)--University of California, San Francisco, 2009. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3484. Adviser: Patricia C. Babbitt.

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 34-36).

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 2701. Adviser: Eric Jakobsson. Includes bibliographical references (leaves 120-147) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph. D.)--University of Hong Kong, 2010. / Includes bibliographical references (p. 169-186). Also available in print.

Thesis (M.S.)--California Polytechnic State University, 2010. / Title from PDF title page; viewed on February 22, 2010. Major professor: Xiao-Hua (Helen) Yu, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "February, 2010." Includes bibliographical references (p. 90-91).