Abstract
Proteomic mass spectrometry holds great promise in biological research, with its capability to identify thousands of proteins and their modifications using an automated high-throughput workflow. However, data analysis remains the Achilles heel that hampers proteomic technology, as product complexity and conflicting marketing messages continue to fuel confusion among proteomics researchers.
 
Whether you are thinking about setting up a new proteomics mass spec lab or have been running one for years, you may appreciate a fresh new look at the proteomics workflow – not with Bayesian statistics and ROC curves, but with intuitive, plain-English analogies that you can relate to. Find out how to avoid the most common misconceptions that many new labs continue to have, including: (1) the difference between throughput and productivity, (2) the great search engine debate, (3) when a computer cluster is your lifesaver rather than your anchor, and (4) what’s the difference between a probability score and a probability anyway?
 
The top 10 list of must-knows are from personal experiences working closely with many high-throughput labs (particularly collaborations with the Aebersold, Gygi, and Yates Labs) and interviews with more than a hundred proteomics scientists.
 
Please join us for an insightful and sometimes entertaining look at proteomics workflow from a fresh perspective.

Background
David Chiang is the Founder and Chief Executive of Sage-N Research Inc., which develops the Sorcerer™ line of Integrated Data Appliances (IDAs) – a standard for low maintenance, high throughput proteomics analysis. He is a popular speaker who has given talks at proteomics seminars and conferences, and was a panel speaker at HUPO 2006. Prior to founding Sage-N Research, David served in executive positions in marketing and development at leading Silicon Valley companies Xilinx and Altera. He is the lead inventor or co-inventor of 28 U.S. patents and numerous international patents in hi-tech, and has several patents pending in the area of proteomics analysis. He is also a business advisor to startup companies and is a member of the Sand Hill Angels private investment group. David holds BS and MS engineering degrees from MIT.