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Guberman, Jonathan M.; Ai, Jing; Arnaiz, Olivier; Baran, Joachim; Blake, Judith A.; Baldock, Richard; Chelala, Claude; Croft, David; Cros, Anthony; Cutts, Rosalind J.; Génova, Alex Di; Forbes, Simon; Fujisawa, Takatomo; Gadaleta, Emanuela; Goodstein, David; Gundem, Gunes; Haggarty, Bernard; Haider, Syed; Hall, Matthew D.; Harris, Todd W.; Haw, Robin; Hu, Shen; Hubbard, Simon J.; Hsu, Jack Shih-Chieh; Iyer, Vivek; Jones, Philip; Katayama, Toshiaki; Kinsella, Rhoda; Kong, Lei; Lawson, Daniel; Liang, Yong; López‐Bigas, Núria; Luo, Jie; Lush, Michael; Mason, Jeremy; Moreews, François; Ndegwa, Nelson; Oakley, Darren; Perez-Llamas, Christian; Primig, Michael; Rivkin, Elena; Rosanoff, Steven; Shepherd, Rebecca; Simon, Reinhard; Skarnes, Bill; Smedley, Damian; Sperling, Linda; Spooner, William; Stevenson, Peter; Stone, Kevin R.; Teague, Jon W.; Wang, Jun; Wang, Jianxin; Whitty, Brett; Wong, David T.; Wong, Marie; Yao, Long; Youens‐Clark, Ken; Yung, Christina K.; Zhang, Junjun; Kasprzyk, Arek

doi:10.1093/database/bar041

Cited by 155 publications

(145 citation statements)

References 23 publications

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“…To retrieve the frequencies of AUUUA pentamers of 3′-UTR, CDS, and 5′-UTR for Ensembl protein-coding genes (Ensembl Genes 83), the respective regions were extracted using Ensembl Biomart (54,55). The frequencies of AUUUA pentamers in each sequence region (5′-UTR, CDS, 3′-UTR) for each transcript were counted using a perl script.…”

Section: Discussionmentioning

confidence: 99%

The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection

Ebner

Sedlyarov

Tasciyan

et al. 2017

Journal of Clinical Investigation

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Section: Discussionmentioning

confidence: 99%

The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection

Ebner

Sedlyarov

Tasciyan

et al. 2017

Journal of Clinical Investigation

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“…All treatments were compared with the control experiments except treatments including FLG, which were compared with control and Silwet samples. Transcript annotation was acquired from The Arabidopsis Information Resource 10 (Lamesch et al, 2012) using the biomaRt data-mining tool (Guberman et al, 2011). The microarray data are available at the Gene Expression Omnibus with the record GSE41935.…”

Section: Microarray Data Preprocessingmentioning

confidence: 99%

Transcriptome Responses to Combinations of Stresses in Arabidopsis

et al. 2013

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Biotic and abiotic stresses limit agricultural yields, and plants are often simultaneously exposed to multiple stresses. Combinations of stresses such as heat and drought or cold and high light intensity have profound effects on crop performance and yields. Thus, delineation of the regulatory networks and metabolic pathways responding to single and multiple concurrent stresses is required for breeding and engineering crop stress tolerance. Many studies have described transcriptome changes in response to single stresses. However, exposure of plants to a combination of stress factors may require agonistic or antagonistic responses or responses potentially unrelated to responses to the corresponding single stresses. To analyze such responses, we initially compared transcriptome changes in 10 Arabidopsis (Arabidopsis thaliana) ecotypes using cold, heat, high-light, salt, and flagellin treatments as single stress factors as well as their double combinations. This revealed that some 61% of the transcriptome changes in response to double stresses were not predictable from the responses to single stress treatments. It also showed that plants prioritized between potentially antagonistic responses for only 5% to 10% of the responding transcripts. This indicates that plants have evolved to cope with combinations of stresses and, therefore, may be bred to endure them. In addition, using a subset of this data from the Columbia and Landsberg erecta ecotypes, we have delineated coexpression network modules responding to single and combined stresses.

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“…Known genes within each 1MB pair window in BTAU 4.0 were then obtained using BIOMART. 25 All previously reported candidate genes for prion disease susceptibility, 21,22 were included as well as newly identified candidate genes based on potential functional role in BSE. Candidate genes were identified using NCBI Genbank, 26 Gene Ontology 27 and KEGG the autonomous nervous system.…”

Section: Resultsmentioning

confidence: 99%