Developing, conducting, and evaluating tabletop exercises requires considerable planning and the perspectives of a variety of stakeholders. While these tabletop exercises identified both strengths and vulnerabilities in emergency preparedness, additional work is needed to develop reliable metrics to gauge exercise performance, inform followup action steps, and to develop re-evaluation exercise designs that assess the impact of post-exercise interventions. Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data BACKGROUND: The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level. RESULTS: Through performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins. CONCLUSION: By refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism. Microarray technology was invented to rapidly profile the quantities of mRNA transcripts in a particular cellular context [1] [2] [3] . Its application has become universal in biomedical researches. Although it is mRNA that is actually detected by microarray experiments, and it is mRNA that has the direct relationship with protein, the methodology and algorithms for data analysis are commonly gene based. As evident in the probe annotation file provided by Affymetrix, gene-level annotation is widely accepted even though it fails to discriminate multiple mRNAs transcribed from the same gene. As a result, the analysis results are usually summarized at the gene level, such as differentially expressed genes [4] [5] [6] or gene-sets [7, 8] . Even in the recent works that integrate protein-protein interaction data and microarray data [9] [10] [11] , the analysis unit is reduced to the gene instead of mRNA. This practice could be attributed to the fact that most of the functional knowledge is at gene level instead of transcript level, and the functional differences between mRNA variants transcribed from the same gene are seldom clear. Although the gene-level analysis ignores the difference among mRNAs variants, this strategy is still biologically meaningful considering that the diversity of genes is much higher than that of transcripts encoded by the same gene.