Life Sciences Commons^™

Leveraging Global Gene Expression Patterns To Predict Expression Of Unmeasured Genes, James Rudd, René A. Zelaya, Eugene Demidenko, Ellen L. Goode, Casey S. Greene S. Greene, Jennifer A. Doherty

Dartmouth Scholarship

BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Application Of Subspace Clustering In Dna Sequence Analysis, Tim Wallace, Ali Sekmen, Xiaofei Wang

Computer Science Faculty Research

Identification and clustering of orthologous genes plays an important role in developing evolutionary models such as validating convergent and divergent phylogeny and predicting functional proteins in newly sequenced species of unverified nucleotide protein mappings. Here, we introduce an application of subspace clustering as applied to orthologous gene sequences and discuss the initial results. The working hypothesis is based upon the concept that genetic changes between nucleotide sequences coding for proteins among selected species and groups may lie within a union of subspaces for clusters of the orthologous groups. Estimates for the subspace dimensions were computed for a small population sample. …

A Conserved Three-Nucleotide Core Motif Defines Musashi Rna Binding Specificity, Nancy Zearfoss, Laura Deveau, Carina Clingman, Eric Schmidt, Emily Johnson, Francesca Massi, Sean Ryder

Sean P. Ryder

Musashi (MSI) family proteins control cell proliferation and differentiation in many biological systems. They are overexpressed in tumors of several origins, and their expression level correlates with poor prognosis. MSI proteins control gene expression by binding RNA and regulating its translation. They contain two RNA recognition motif (RRM) domains, which recognize a defined sequence element. The relative contribution of each nucleotide to the binding affinity and specificity is unknown. We analyzed the binding specificity of three MSI family RRM domains using a quantitative fluorescence anisotropy assay. We found that the core element driving recognition is the sequence UAG. Nucleotides outside …

The Role Of Visualization And 3-D Printing In Biological Data Mining, Talia L. Weiss, Amanda Zieselman, Douglas P. Hill, Solomon G. Diamond, Li Shen, Andrew J. Saykin, Jason H. Moore

Dartmouth Scholarship

Background:

Biological data mining is a powerful tool that can provide a wealth of information about patterns of genetic and genomic biomarkers of health and disease. A potential disadvantage of data mining is volume and complexity of the results that can often be overwhelming. It is our working hypothesis that visualization methods can greatly enhance our ability to make sense of data mining results. More specifically, we propose that 3-D printing has an important role to play as a visualization technology in biological data mining. We provide here a brief review of 3-D printing along with a case study to …

Loregic: A Method To Characterize The Cooperative Logic Of Regulatory Factors, Daifeng Wang, Koon-Kiu Yan, Cristina Sisu, Chao Cheng, Joel Rozowsky, William Meyerson, Mark B. Gerstein

Dartmouth Scholarship

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. …

An Approach For Determining And Measuring Network Hierarchy Applied To Comparing The Phosphorylome And The Regulome, Chao Cheng, Erik Andrews, Koon-Kiu Yan, Matthew Ung, Daifeng Wang, Mark Gerstein

Dartmouth Scholarship

Many biological networks naturally form a hierarchy with a preponderance of downward information flow. In this study, we define a score to quantify the degree of hierarchy in a network and develop a simulated-annealing algorithm to maximize the hierarchical score globally over a network. We apply our algorithm to determine the hierarchical structure of the phosphorylome in detail and investigate the correlation between its hierarchy and kinase properties. We also compare it to the regulatory network, finding that the phosphorylome is more hierarchical than the regulome.

Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …

Mapping The Pareto Optimal Design Space For A Functionally Deimmunized Biotherapeutic Candidate, Regina S. Salvat, Andrew S. Parker, Yoonjoo Choi, Chris Bailey-Kellogg, Karl E. Griswold

Dartmouth Scholarship

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the …