Microarrays and Mass Spectrometry - The Future of Proteomics

Bioinformatics and Proteomics Approaches for Aging Research

Aging is a natural phenomenon that affects the entire physiology of an organism. Elucidating the molecular mechanisms underlying this complex process remains a major challenge today. Humans make poor models for research into aging because of their long life span. Thus, most of the current knowledge is through studies conducted in lower organisms. Large differences in life spans make it difficult to extrapolate the results of experiments carried out in model organisms to humans. Recent advances in genomic and proteomic technologies now permit generation of data pertaining to aging on a large-scale. In addition, several web-based community resources and databases are available that provide easy access to the available data. Use of bioinformatics and systems biology type of approaches provide a framework to start dissecting this complex biological phenomenon. Here, we discuss various genomic, transcriptomic and proteomic approaches that have the potential to provide a comprehensive mechanistic insight into the aging process.

Enabling proteomics discovery through visual analysis. The peptide permutation and protein prediction tool

Proteins play a key role in cellular processes, making proteomics central to understanding systems biology. MS techniques provide a means to observe entire proteomes at a global level. Yet, high-throughput MS proteomics techniques generate data faster than it can currently be analyzed. The success of proteomics depends on high-throughput experimental techniques coupled with sophisticated visual analysis and data-mining methods. Visual analysis has been applied successfully in a number of fields plagued with huge, complex data sets and will likely be an important tool in proteomics discovery. PQuad, a novel visualization of MS proteomics data, provides powerful analysis capabilities that support a number of proteomic data applications. In particular, PQuad supports differential proteomics by simplifying the comparison of peptide sets from different experimental conditions as well as different protein identification or confidence scoring techniques. Finally, PQuad supports data validation and quality control by providing a variety of resolutions for huge amounts of data to reveal errors undetected by other methods.