@article{WagnerFischerThomaetal.2011,
  author    = {Wagner, Toni U. and Fischer, Andreas and Thoma, Eva C. and Schartl, Manfred},
  title     = {CrossQuery : A Web Tool for Easy Associative Querying of Transcriptome Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-76088},
  year      = {2011},
  abstract  = {Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deepsequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.},
  subject      = {CrossQuery},
  language  = {en}
}
@article{WagnerFischerThomaetal.2011,
  author    = {Wagner, Toni U. and Fischer, Andreas and Thoma, Eva C. and Schartl, Manfred},
  title     = {CrossQuery: A Web Tool for Easy Associative Querying of Transcriptome Data},
  series = {PLoS ONE},
  volume    = {6},
  journal   = {PLoS ONE},
  number    = {12},
  doi       = {10.1371/journal.pone.0028990},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134787},
  pages     = {e28990},
  year      = {2011},
  abstract  = {Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deep-sequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.},
  language  = {en}
}
@article{ThomaWischmeyerOffenetal.2012,
  author    = {Thoma, Eva C. and Wischmeyer, Erhard and Offen, Nils and Maurus, Katja and Sir{\´e}n, Anna-Leena and Schartl, Manfred and Wagner, Toni U.},
  title     = {Ectopic expression of Neurogenin 2 alone is sufficient to induce differentiation of embryonic stem cells into mature neurons},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-75862},
  year      = {2012},
  abstract  = {Recent studies show that combinations of defined key developmental transcription factors (TFs) can reprogram somatic cells to pluripotency or induce cell conversion of one somatic cell type to another. However, it is not clear if single genes can define a cells identity and if the cell fate defining potential of TFs is also operative in pluripotent stem cells in vitro. Here, we show that ectopic expression of the neural TF Neurogenin2 (Ngn2) is sufficient to induce rapid and efficient differentiation of embryonic stem cells (ESCs) into mature glutamatergic neurons. Ngn2-induced neuronal differentiation did not require any additional external or internal factors and occurred even under pluripotency-promoting conditions. Differentiated cells displayed neuron-specific morphology, protein expression, and functional features, most importantly the generation of action potentials and contacts with hippocampal neurons. Gene expression analyses revealed that Ngn2-induced in vitro differentiation partially resembled neurogenesis in vivo, as it included specific activation of Ngn2 target genes and interaction partners. These findings demonstrate that a single gene is sufficient to determine cell fate decisions of uncommitted stem cells thus giving insights into the role of key developmental genes during lineage commitment. Furthermore, we present a promising tool to improve directed differentiation strategies for applications in both stem cell research and regenerative medicine.},
  subject      = {Physiologie},
  language  = {en}
}