Engineering Commons^™

Discovering Gene Functional Relationships Using Faun (Feature Annotation Using Nonnegative Matrix Factorization), Elina Tjioe, Michael W. Berry, Ramin Homayouni

Faculty Publications and Other Works -- EECS

Background

Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments.

Results

In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity …

Graph Algorithms For Machine Learning: A Case-Control Study Based On Prostate Cancer Populations And High Throughput Transcriptomic Data, Gary L. Rogers, Pablo Moscato, Michael A. Langston

Faculty Publications and Other Works -- EECS

Background

The continuing proliferation of high-throughput biological data promises to revolutionize personalized medicine. Confirming the presence or absence of disease is an important goal. In this study, we seek to identify genes, gene products and biological pathways that are crucial to human health, with prostate cancer chosen as the target disease.

Materials and methods

Using case-control transcriptomic data, we devise a graph theoretical toolkit for this task. It employs both innovative algorithms and novel two-way correlations to pinpoint putative biomarkers that classify unknown samples as cancerous or normal.

Results and conclusion

Observed accuracy on real data suggests that we are …

Inferring Gene Coexpression Networks For Low Dose Ionizing Radiation Using Graph Theoretical Algorithms And Systems Genetics, Sudhir Naswa, Gary L. Rogers, Rachel M. Lynch, Stephen A. Kania, Suchita Das, Elissa J. Chesler, Arnold M. Saxton, Brynn H. Voy, Michael A. Langston

Faculty Publications and Other Works -- EECS

Background

Biological data generated through large scale -omics technologies have resulted in a new paradigm in the study of biological systems. Instead of focusing on individual genes or proteins these technologies enable us to extract biological networks using powerful computing and statistical algorithms that are scalable to very large datasets.

Materials and methods

We have developed a tool chain using novel graph algorithms to extract gene coexpression networks from microarray data. We highlight implementation of our tool chain to investigate the effects of in vivo low dose ionizing radiation treatments on mice. We are using systems genetics approach to investigate …

Development Of Tools For The Automated Analysis Of Spectra Generated By Tandem Mass Spectrometry, Sally R. Ellingson, Joe Hughes, Dylan Storey, Rick Weber, Nathan Verberkmoes

Faculty Publications and Other Works -- EECS

Background

While multiple tools exist for the analysis and identification of spectra generated in shotgun proteomics experiments, few easily implemented tools exist that allow for the automated analysis of the quality of spectra. A researcher’s knowledge of the quality of a spectra from an experiment can be helpful in determining possible reasons for misidentification or lack of identification of spectra in a sample.

Materials and methods

We are developing a automated high throughput method that analyses spectra from 2d-LC-MS/MS datasets to determine their quality and overall determines the quality of the run. We will then compare our programs to existing …

Developing Measures For Microbial Genome Assembly Quality Control, Rachel M. Adams, Jason B. Harris, Jeremy J. Jay, Beth G. Johnson, Miriam L. Land, Loren J. Hauser

Faculty Publications and Other Works -- EECS

Background

Advances in sequencing technologies are outpacing the rate at which genomes can be thoroughly finished and analyzed. Over the next year, genome sequencing will increase many-fold, but high quality and high-throughput annotation methods have yet to be developed to handle the need. As more microbial genomes are sequenced, whole-genome annotation methods identify many putative genes which need further verification. By analyzing a broad range of annotated genomes we can identify patterns and statistics useful in determining the annotation quality and spurious gene outliers. Our work is attempting to identify quality control measures based on a full inter-genomic comparison instead …