Title |
Cell cycle, oncogenic and tumor suppressor pathways regulate numerous long and macro non-protein-coding RNAs
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Published in |
Genome Biology, March 2014
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DOI | 10.1186/gb-2014-15-3-r48 |
Pubmed ID | |
Authors |
Jörg Hackermüller, Kristin Reiche, Christian Otto, Nadine Hösler, Conny Blumert, Katja Brocke-Heidrich, Levin Böhlig, Anne Nitsche, Katharina Kasack, Peter Ahnert, Wolfgang Krupp, Kurt Engeland, Peter F Stadler, Friedemann Horn |
Abstract |
The genome is pervasively transcribed but most transcripts do not code for proteins, constituting non-protein coding RNAs. Despite increasing numbers of functional reports of individual long noncoding RNAs (lncRNAs), assessing the extent of functionality among the non-coding transcriptional output of mammalian cells remains intricate. In the protein coding world, transcripts differentially expressed in the context of processes essential for the survival of multicellular organisms have been instrumental for the discovery of functionally relevant proteins and their deregulation is frequently associated with diseases. We therefore systematically identify lncRNAs expressed differentially in response to oncologically relevant processes, cell-cycle, p53-, and STAT3 pathway, using tiling arrays. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 33% |
United States | 3 | 25% |
Switzerland | 2 | 17% |
France | 2 | 17% |
Unknown | 1 | 8% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 50% |
Members of the public | 5 | 42% |
Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 1% |
United Kingdom | 1 | 1% |
Mexico | 1 | 1% |
Denmark | 1 | 1% |
Namibia | 1 | 1% |
Unknown | 75 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 27 | 34% |
Student > Ph. D. Student | 14 | 18% |
Student > Master | 12 | 15% |
Professor | 7 | 9% |
Student > Bachelor | 4 | 5% |
Other | 10 | 13% |
Unknown | 6 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 35 | 44% |
Biochemistry, Genetics and Molecular Biology | 18 | 23% |
Computer Science | 6 | 8% |
Medicine and Dentistry | 6 | 8% |
Engineering | 2 | 3% |
Other | 4 | 5% |
Unknown | 9 | 11% |