PIIKA 2.5: Enhanced quality control of peptide microarrays for kinome analysis.

Peptide microarrays consisting of defined phosphorylation target sites are an effective approach for high throughput analysis of cellular kinase (kinome) activity. Kinome peptide arrays are highly customizable and do not require species-specific reagents to measure kinase activity, making them amenable for kinome analysis in any species. Our group developed software, Platform for Integrated, Intelligent Kinome Analysis (PIIKA), to enable more effective extraction of meaningful biological information from kinome peptide array data. A subsequent version, PIIKA2, unveiled new statistical tools and data visualization options. Here we introduce PIIKA 2.5 to provide two essential quality control metrics and a new background correction technique to increase the accuracy and consistency of kinome results. The first metric alerts users to improper spot size and informs them of the need to perform manual resizing to enhance the quality of the raw intensity data. The second metric uses inter-array comparisons to identify outlier arrays that sometimes emerge as a consequence of technical issues. In addition, a new background correction method, background scaling, can sharply reduce spatial biases within a single array in comparison to background subtraction alone. Collectively, the modifications of PIIKA 2.5 enable identification and correction of technical issues inherent to the technology and better facilitate the extraction of meaningful biological information. We show that these metrics demonstrably enhance kinome analysis by identifying low quality data and reducing batch effects, and ultimately improve clustering of treatment groups and enhance reproducibility. The web-based and stand-alone versions of PIIKA 2.5 are freely accessible at via http://saphire.usask.ca.

From Beef to Bees: High-Throughput Kinome Analysis to Understand Host Responses of Livestock Species to Infectious Diseases and Industry-Associated Stress.

Within human health research, the remarkable utility of kinase inhibitors as therapeutics has motivated efforts to understand biology at the level of global cellular kinase activity (the kinome). In contrast, the diminished potential for using kinase inhibitors in food animals has dampened efforts to translate this research approach to livestock species. This, in our opinion, was a lost opportunity for livestock researchers given the unique potential of kinome analysis to offer insight into complex biology. To remedy this situation, our lab developed user-friendly, cost-effective approaches for kinome analysis that can be readily incorporated into most research programs but with a specific priority to enable the technology to livestock researchers. These contributions include the development of custom software programs for the creation of species-specific kinome arrays as well as comprehensive deconvolution and analysis of kinome array data. Presented in this review are examples of the application of kinome analysis to highlight the utility of the technology to further our understanding of two key complex biological events of priority to the livestock industry: host immune responses to infectious diseases and animal stress responses. These advances and examples of application aim to provide both mechanisms and motivation for researchers, particularly livestock researchers, to incorporate kinome analysis into their research programs.

Banding Together: A Systematic Comparison of The Cancer Genome Atlas and the Mitelman Databases.

Cytogenetic aberrations at the single-cell level represent an important characteristic of cancer cells relevant to tumor evolution and prognosis. However, with the advent of The Cancer Genome Atlas (TCGA), there has been a major shift in cancer research to the use of data from aggregate cell populations. Given that tumor cells harbor hundreds to thousands of biologically relevant genetic alterations that manifest as intratumor heterogeneity, these aggregate analyses may miss alterations readily observable at single-cell resolution. Using the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, we developed an algorithm to parse International System for Cytogenetic Nomenclature notation for quantitative abnormalities. Comparison of the Mitelman database and TCGA demonstrated that the Mitelman database is a powerful resource, and that cytogenetic aberrations captured by traditional approaches used in Mitelman database are on par with population-based genomic analyses used in TCGA. This algorithm will help nonspecialists to overcome the challenges associated with the format and syntax of the Mitelman database. SIGNIFICANCE: A novel in silico approach compares cytogenetic data between the Mitelman database and TCGA, highlighting the advantages and limitations of both datasets.