Computational Biology Commons^™

Prediction Of Kinase-Substrate Associations Using The Functional Landscape Of Kinases And Phosphorylation Sites, Serhan Yilmaz, Filipa Blasco Tavares Pereira Lopes, Mark R. Chance, Mehmet Koyutürk

Faculty Scholarship

Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships …

Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica D. Wiredja, Daniela M. Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance

Faculty Scholarship

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, …

An Integrative -Omics Approach To Identify Functional Sub-Networks In Human Colorectal Cancer, Rod K. Nibbe, Mehmet Koyutürk, Mark R. Chance

Faculty Scholarship

Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that …

A Brief History Of Bioperl, Colin Crossman, Arti K. Rai

Faculty Scholarship

Large-scale open-source projects face a litany of pitfalls and difficulties. Problems of contribution quality, credit for contributions, project coordination, funding, and mission-creep are ever-present. Of these, long-term funding and project coordination can interact to form a particularly difficult problem for open-source projects in an academic environment.

BioPerl was chosen as an example of a successful academic open-source project. Several of the roadblocks and hurdles encountered and overcome in the development of BioPerl are examined through the telling of the history of the project. Along the way, key points of open-source law are explained, such as license choice and copyright.

The …