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We analyzed mRNAs regulated during differentiation of rat neural stem cells using the ABI1700 microarray platform. This microarray, while technically advanced, suffers from the difficulty of integrating hybridization results into public databases for systems-level analysis. This is particularly true for the rat array, since many of the probes were designed for transcripts based on predicted human and mouse homologs. Using several strategies, we increased the public annotation of the 27,531 probes from 43% to over 65%. To increase the dynamic range of annotation, probes were mapped to numerous public keys from several data sources. Consensus annotation from multiple sources was determined for well-scoring alignments, and a confidence-based ranking system established for probes with less agreement across multiple data sources. Previous attempts at genomic interpretation using the Celera annotation model resulted in poor overlap with expected genomic sequences. Since the public keys are more precisely mapped to the genome, we could now analyze the relationships between predicted transcription factor binding sites and expression clusters. Results collected from a differentiation time course of two neural stem cell clones were clustered using a model-based algorithm. Transcription factor binding sites were predicted from upstream regions of mapped transcripts using position-weight matrices from either JAS-PAR or TRANSFAC, and the resulting scores were used to discriminate between observed expression clusters. A classification and regression tree analysis was conducted using cluster numbers as gene identifiers and TFBS scores as predictors, pruning back to obtain a tree with the lowest gene class prediction error rate. Results identify several transcription factors, the presence or absence of which are sufficient to differentiate clusters of mRNAs changing over time from those that are static, as well as clusters describing cell-line differences. Public annotation of the ABI1700 rat genome array will be valuable for integrating results into future systems-level analyses.