Java and Jive Discussion Tables
Tuesday, October 24
7:30 am

TABLE ONE

Integrating Data Across Platforms
Host: Lisa D. White, Baylor College of Medicine
It is more and more common for researchers to use multiple technology platforms to answer biological questions. Comparison and integration of data resulting from diverse platforms can be difficult. We will discuss difficulties and possible solutions to the integration of data from various platforms (e.g., sequencing, gene expression microarray, QPCR, ChIP on chip, etc.)

Topics for Discussion:

• Poll of various technolgies used around the table.
• What are the differences in these technologies that cause difficulty in integrating?
• How can we overcome these difficulties to generate a complete biological story?

TABLE TWO

Successful Design of Studies using PCR and Microarrays
Host: Herbert Auer, Columbus Children’s Research Institute
Studies using PCR and microarrays are frequently expensive and time-consuming, and at the end of the day the results can be unsatisfactory and/or uninterpretable. Careful upfront planning avoids headaches, wasted time and money. Assessment of biological variation and avoidance of technical bias are two major contributors to successful studies.

Topics for Discussion:

• Technical bias between study groups 
• Assessment of biological variation within a group 
• The concept of relative measurements 
• Early markers for successful studies 

TABLE THREE

Confirming Microarray Results with Alternative Technologies – Is it Necessary?
Host: Tucker Patterson, FDA
The debate on whether or not microarray-based studies need to be confirmed (“validated”) by alternative technologies has existed almost from the beginning of the development of microarray technology. Now that many of the variables initially present in microarray experiments are well-controlled, do the data generated from microarray studies need to be corroborated with alternative technologies such as real-time RT-PCR and Northern blots?

Topics for Discussion:

• Can microarray-based studies stand alone or do they need to be corroborated with an alternative technology?
• What constitutes an acceptable corroborative study?
• How many and which genes should be corroborated?
• If the corroborative study is in disagreement, which method is correct?
• Should journals develop or revisit policies on microarray data submission?

TABLE FOUR

The Trouble with Biochips
Host: Mike Lucero, Fluidigm
After many years of hype what have biochips contributed to life science? What has become of the microfluidic companies? What measurements are they best for? What could the companies have done different? How should they contribute in the future?

Topics for Discussion:

• Signature validation - should it be done with chips or move back to traditional measurements
• Unsolved issues with microfluidics 
• What applications are new on the horizon for microfluidic biochips

TABLE FIVE

Methods for Performing and Assessing Microarray Quality Control
Host: Richard Shippy, GE Healthcare

Topics for Discussion:

• The application of controls, such as External RNA Controls (ERCs), for assessing array quality.
• General array attributes which can be used to gauge performance.
• The use of baseline reference data sets for validating microarray experiments.

TABLE SIX

Use of Internal Standards in Quantitative RT-PCR
Host: James C. Willey, Medical College of Ohio and Gene Express, Inc.

Topics for Discussion:

• When are the helpful? 
• When are they essential? 
• What is best way to implement them?

TABLE SEVEN

Sample Collection and Processing, a Pivotal Stage of Transcript Profiling
Host: Eric Fedyk, Millennium Pharmaceuticals Inc.
The quality of microarray and qRT-PCR systems have improved to the extent that variability generated during these processes often compromise the fidelity of transcriptional profiling data less so than variability generated during sample collection and processing. All types of sample collection and storage are not equal and RNA isolation systems also exhibit biases. Choosing the best system for an application and characterizing the products via rigorous quality control analyses are critical for reproducible, high fidelity transcript profiling data, and thereby avoiding that haunting adage, “Putting **** in, results in getting **** out.” 

Topics for Discussion:

• When to collect and store samples frozen versus formalin-fixed and paraffin-embedded? 
• Isolate subpopulations of leukocytes or store whole blood? 
• Optimal method of RNA isolation for specific sample types, tissues, throughput (capacity), etc.? 
• What are the most predictive quality control metrics for mRNA, miRNAs, rRNAs, tRNAs, etc. 
• What are the most predictive quality control metrics for cDNA, cRNA and/or qPCR reactions?

TABLE EIGHT

Real-time PCR Quality Control and Statistical Analysis
Host: Joshua S. Yuan, The University of Tennessee, Knoxville 
We will discuss the most challenging aspect of real-time PCR technology, proper quality control and data analysis. The reproducibility and realiability of real-time PCR technology heavily depends on stringent quality control, which includes the RNA quality control, experimental design control, proper reference gene, and efficiency evaluation. The quality of real-time PCR data also depends on proper statistical methods leading to precise data. We hope the discussion will help the researchers to be aware of the issues and to work toward a standard for real-time PCR quality control and data analysis. 

Topics for Discussion: 

• Real-time experimental design 
• Reference gene Selection 
• Efficiency Considerations 
• Quality Control/Standards for Statistical Analysis