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We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources. RationaleGene-expression profiling and other forms of high-throughput genomic and proteomic studies are revolutionizing biology. That much is universally agreed. But the new technologies pose new challenges. The first is the experiment itself, the second is statistical analysis of results, the third is biological interpretation. That third challenge is often the most vexing and time-consuming. In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks: 'What does all of this mean biologically?' The work of the Gene Ontology (GO) Consortium [1] provides a way to address that question. GO organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. In the past, this GO information was queried one gene at a time. Recently, batch processing has been introduced [2], but with a flat-format output that does not communicate the richness of GO's hierarchical structure.We have developed, and present here, the program package GoMiner as a freely available computer resource that fully incorporates the hierarchical structure of the Gene Ontology to automate the functional categorization of gene lists of any length. GoMiner is downloadable free of charge from [3] or [4]. GoMiner was developed particularly for biological interpretation of microarray data; one can input a list of underand overexpressed genes and a list of all genes on the array, and then calculate enrichment or depletion of categories with genes that have changed expression. GoMiner thus facilitates analysis and organization of the results for rapid interpretation of 'omic' [5,6] data. For concreteness, the descriptions in

The molecular mechanism underlying brain regeneration in vertebrates remains elusive. We performed spatial enhanced resolution omics sequencing (Stereo-seq) to capture spatially resolved single-cell transcriptomes of axolotl telencephalon sections during development and regeneration. Annotated cell types exhibited distinct spatial distribution, molecular features, and functions. We identified an injury-induced ependymoglial cell cluster at the wound site as a progenitor cell population for the potential replenishment of lost neurons, through a cell state transition process resembling neurogenesis during development. Transcriptome comparisons indicated that these induced cells may originate from local resident ependymoglial cells. We further uncovered spatially defined neurons at the lesion site that may regress to an immature neuron–like state. Our work establishes spatial transcriptome profiles of an anamniote tetrapod brain and decodes potential neurogenesis from ependymoglial cells for development and regeneration, thus providing mechanistic insights into vertebrate brain regeneration.