Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/66771
Title: Digital Gene Expression approach over multiple RNA-Seq data sets to detect neoblast transcriptional changes in Schmidtea mediterranea
Author: Rodríguez Esteban, Gustavo
González-Sastre, Alejandr
Rojo-Laguna, José Ignacio
Saló i Boix, Emili
Abril, Josep F.
Keywords: Expressió gènica
Cèl·lules mare
Gene expression
Stem cells
Issue Date: 15-Apr-2015
Publisher: BioMed Central
Abstract: The freshwater planarian Schmidtea mediterranea is recognised as a valuable model for research into adult stem cells and regeneration. With the advent of the high-throughput sequencing technologies, it has become feasible to undertake detailed transcriptional analysis of its unique stem cell population, the neoblasts. Nonetheless, a reliable reference for this type of studies is still lacking. Taking advantage of digital gene expression (DGE) sequencing technology we compare all the available transcriptomes for S. mediterranea and improve their annotation. These results are accessible via web for the community of researchers. Using the quantitative nature of DGE, we describe the transcriptional profile of neoblasts and present 42 new neoblast genes, including several cancer-related genes and transcription factors. Furthermore, we describe in detail the Smed-meis-like gene and the three Nuclear Factor Y subunits Smed-nf-YA, Smed-nf-YB-2 and Smed-nf-YC. DGE is a valuable tool for gene discovery, quantification and annotation. The application of DGE in S. mediterranea confirms the planarian stem cells or neoblasts as a complex population of pluripotent and multipotent cells regulated by a mixture of transcription factors and cancer-related genes.
Note: Reproducció del document publicat a: http://dx.doi.org/10.1186/s12864-015-1533-1
It is part of: Bmc Genomics, 2015, vol. 16, num. 361
Related resource: http://dx.doi.org/10.1186/s12864-015-1533-1
URI: http://hdl.handle.net/2445/66771
ISSN: 1471-2164
Appears in Collections:Articles publicats en revistes (Genètica, Microbiologia i Estadística)

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