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- gga-miRNOME, a microRNA-sequencing dataset from chick embryonic tissuesPublication . Duarte, Isabel; Carraco, Gil; de Azevedo, Nayara T. D.; Benes, Vladimir; Andrade, Raquel P.MicroRNAs (miRNAs) are small non-coding RNA molecules, with sizes ranging from 18 to 25 nucleotides, which are key players in gene expression regulation. These molecules play an important role in fine-tuning early vertebrate embryo development. However, there are scarce publicly available miRNA datasets from non-mammal embryos, such as the chicken (Gallus gallus), which is a classical model system to study vertebrate embryogenesis. Here, we performed microRNA-sequencing to characterize the early stages of trunk and limb development in the chick embryo. For this, we profiled three chick embryonic tissues, namely, Undetermined Presomitic Mesoderm (PSM_U), Determined Presomitic Mesoderm (PSM_D) and Forelimb Distal Cyclic Domain (DCD). We identified 926 known miRNAs, and 1,141 novel candidate miRNAs, which nearly duplicates the number of Gallus gallus entries in the miRBase database. These data will greatly benefit the avian research community, particularly by highlighting new miRNAs potentially involved in the regulation of early vertebrate embryo development, that can be prioritized for further experimental testing.
- StemMapper: a curated gene expression database for stem cell lineage analysisPublication . Pinto, Jose P.; Machado, Rui S. R.; Magno, Ramiro; Oliveira, Daniel V.; Machado, Susana; Andrade, Raquel P.; Braganca, Jose; Duarte, Isabel; Futschik, Matthias E.Transcriptomic data have become a fundamental resource for stem cell (SC) biologists as well as for a wider research audience studying SC-related processes such as aging, embryonic development and prevalent diseases including cancer, diabetes and neurodegenerative diseases. Access and analysis of the growing amount of freely available transcriptomics datasets for SCs, however, are not trivial tasks. Here, we present StemMapper, a manually curated gene expression database and comprehensive resource for SC research, built on integrated data for different lineages of human and mouse SCs. It is based on careful selection, standardized processing and stringent quality control of relevant transcriptomics datasets to minimize artefacts, and includes currently over 960 transcriptomes covering a broad range of SC types. Each of the integrated datasets was individually inspected andmanually curated. StemMapper's user-friendly interface enables fast querying, comparison, and interactive visualization of quality-controlled SC gene expression data in a comprehensive manner. A proof-of-principle analysis discovering novel putative astrocyte/neural SC lineage markers exemplifies the utility of the integrated data resource. We believe that StemMapper can open the way for new insights and advances in SC research by greatly simplifying the access and analysis of SC transcriptomic data.
- rdml: A Mathematica package for parsing and importing Real-Time qPCR dataPublication . Magno, Ramiro; Duarte, Isabel; Andrade, Raquel P.; Palmeirim, IsabelObjective The purpose and objective of the research presented is to provide a package for easy importing of Real-Time PCR data markup language (RDML) data to Mathematica. Results Real-Time qPCR is the most widely used experimental method for the accurate quantification of gene expression. To enable the straightforward archiving and sharing of qPCR data and its associated experimental information, an XML-based data standard was developed—the Real-Time PCR data markup language (RDML)—devised by the RDML consortium. Here, we present rdml, a package to parse and import RDML data into Mathematica, allowing the quick loading and extraction of relevant data, thus promoting the re-analysis, meta-analysis or experimental re-validation of gene expression data deposited in RDML format.