The Triangle Area Mass Spectrometry Discussion Group (TAMS) serves a diverse group of scientists, from both academia and industry. TAMS is based in the Research Triangle Park, conveniently located between Chapel Hill, Durham, and Raleigh. Attendance at the meetings is typically 35-45 scientists and students, with some meetings drawing in excess of 100 people.

Introduction

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TAMS was created to meet the interests of such a variety of scientists with diverse interests, a balanced program between theory and applications is attempted, as well as covering a variety of applications. Due to the large number of scientists in the biotechnology and pharmaceutical industries, the application seminars are weighted towards these areas.

Upcoming Seminar

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  • February 4th starting at 5:30 (pizza and social hour) and a lecture starting at 6:15 pm. The speaker will be Ruth Angelleti from the Albert Einstein School of Medicine. It will be held at Duke University at the BioSciences Building, Room 111. (Directions)

    "Target Discovery in Apicomplexan Waterborne Parasites Toxoplasma gondii is an obligate intracellular protozoan that infects 20 to 90% of the population. It can cause both acute and chronic infections, many of which are asymptomatic, and, in immunocompromized hosts, can cause fatal infection due to reactivation from an asymptomatic chronic infection. An essential step towards understanding molecular mechanisms controlling transitions between the various life stages and identifying candidate drug targets is to accurately characterize the T. gondii proteome. Our laboratory focused on identifying membrane and cytoskeletal associated proteins, which are common chemotherapeutic targets. We assembled a database comprising all computationally and experimentally derived sequences in an effort to capture the complete hypothetical proteome of T. gondii. Commonly used gene prediction algorithms produced very disparate sets of protein sequences, with pairwise overlaps ranging from 1.4% to 12%. Using combined mass spectrometry and computational approaches, we benchmarked gene prediction methods and observed false negative rates of 31 to 43%. This study demonstrated that genome analysis coupled with experimental proteomics data is essential to benchmark and improve the accuracy of gene prediction methods and facilitates insights into gene structure and splice variability within genomes "