Life Sciences Commons^™

Evolution Of Interactomes, Monica Micek

Master's Theses

Protein-protein interactions are part of all biological processes and are responsible for directing the development and maintenance of all systems in a species. Identifying such interactions provides insight into molecular processes in addition to their importance in understanding disease. Identifying protein-protein interactions experimentally is expensive, both in terms of cost and effort, and can generate erroneous results. Thus computational methods are key in reducing the scope of experimental assays, providing predictions for subsequent verification. Herein I present a new computational tool for the prediction of protein-protein interactions which, looking at sequence data alone, can identify putative interacting proteins as a …

Miip: The Monomer Identification And Isolation Program, Christopher Bun, William Ziccardi, Jeffrey Doering, Catherine Putonti

Bioinformatics Faculty Publications

Repetitive elements within genomic DNA are both functionally and evolutionarily informative. Discovering these sequences ab initio is computationally challenging, compounded by the fact that selection on these repeats is often relaxed; thus sequence identity between repetitive elements can vary significantly. Here we present a new application, the Monomer Identification and Isolation Program (MiIP), which provides functionality to both search for a particular repeat as well as discover repetitive elements within a larger genomic sequence. To compare MiIP’s performance with other repeat detection tools, analysis was conducted for synthetic sequences as well as several α21-II clones and HC21 BAC sequences. The …

Cbdb: The Codon Bias Database, Adam Hilterbrand, Joseph Saelens, Catherine Putonti

Bioinformatics Faculty Publications

Background

In many genomes, a clear preference in the usage of particular codons exists. The mechanisms that induce codon biases remain an open question; studies have attributed codon usage to translational selection, mutational bias and drift. Furthermore, correlations between codon usage within host genomes and their viral pathogens have been observed for a myriad of host-virus systems. As such, numerous studies have investigated codon usage and codon bias in an effort to better understand how species evolve. Numerous metrics have been developed to identify biases in codon usage. In addition, a few data repositories of codon bias data are available, …