Our data extend the gene expression network for neural differentiation and reveal novel aspects of transcriptional control pathways underlying the multistep process of commitment and differentiation of hESCs into neural cells. All significantly expressed transcripts were clustered using a hierarchical clustering method. The determination of the correct number of clusters was based on measuring the similarity of each gene to its own cluster compared to the similarity of the gene to genes in other clusters, which was measured using the average of the intracluster and intercluster distances. MATLAB software was used for clustering and correlation. Expander software was used for the hierarchical clustering of transcripts overexpressed in each stage separately and cell cycle associated transcripts. Briefly, the fold changes of the expression values compared to the ESC stage were imported into the software and standardized with a mean of 0 and a variance of 1. Then, using the average linkage Nilotinib method, transcripts were clustered, and the expression matrix was visualized with a dendrogram. The STRING database was used to construct a regulatory network of differentially expressed transcripts. Then, a regulatory sub-graph was extracted from this network by selecting edges with inhibitory or activatory regulatory interactions. The visualization of networks was performed using Cytoscape. We used BiNGO to find statistically over- or underrepresented Gene Onthology categories in the biological data as a tool to enrich the analysis of the transcriptome dataset. Enrichment was determined in reference to all human Entrez GeneIDs that were annotated in the Biological Process branch. P-values were derived from a hypergeometric test followed by the Benjamini and Hochberg false discovery rate. A P-value cutoff of 0.01 was used to identify significantly enriched categories. Pathway analyses were assigned with the ClueGO plugin to all of the genes using the KEGG database. A two sided hypergeometric test was used as statistical test for the probability of each gene falling into a pathway. An analysis of transcriptome dynamics during differentiation revealed that 5955 transcripts were SB431542 side effects modulated during differentiation in at least one stage compared with hESCs. As expected, the numbers of modulated genes increased during the differentiation of hESCs to MNs. While 505 and 1785 transcripts showed differential expression patterns in NIs and NEs, respectively, compared with hESCs, 5134 transcripts were modulated in MNs compared with hESCs. While most of the modulated genes in NIs were up-regulated, only 48% of regulated genes in the MNs were up-regulated. The minimum correlation of the expression patterns between stages was between the hESC and NI stages and between the NI and MN stages.