Does Haste Make Waste in Regulatory Analysis?

Author(s):  
Patrick A. McLaughlin ◽  
Jerry Ellig
Keyword(s):  
BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Jermaine Ross ◽  
Alexander Kuzin ◽  
Thomas Brody ◽  
Ward F. Odenwald
Keyword(s):  

2018 ◽  
Vol 47 (D1) ◽  
pp. D729-D735 ◽  
Author(s):  
Rongbin Zheng ◽  
Changxin Wan ◽  
Shenglin Mei ◽  
Qian Qin ◽  
Qiu Wu ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Junyi Li ◽  
Yi-Xue Li ◽  
Yuan-Yuan Li

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.


Development ◽  
1994 ◽  
Vol 120 (9) ◽  
pp. 2673-2686 ◽  
Author(s):  
K.M. George ◽  
M.W. Leonard ◽  
M.E. Roth ◽  
K.H. Lieuw ◽  
D. Kioussis ◽  
...  

We describe the embryonic expression pattern as well as the cloning and initial transcriptional regulatory analysis of the murine (m) GATA-3 gene. In situ hybridization shows that mGATA-3 mRNA accumulation is temporally and spatially regulated during early development: although found most abundantly in the placenta prior to 10 days of embryogenesis, mGATA-3 expression becomes restricted to specific cells within the embryonic central nervous system (in the mesencephalon, diencephalon, pons and inner ear) later in gestation. GATA-3 also shows a restricted expression pattern in the peripheral nervous system, including terminally differentiating cells in the cranial and sympathetic ganglia. In addition to this distinct pattern in the nervous system, mGATA-3 is also expressed in the embryonic kidney and the thymic rudiment, and further analysis showed that it is expressed throughout T lymphocyte differentiation. To begin to investigate how this complex gene expression pattern is elicited, cloning and transcriptional regulatory analyses of the mGATA-3 gene were initiated. At least two regulatory elements (one positive and one negative) appear to be required for appropriate tissue-restricted regulation after transfection of mGATA-3-directed reporter genes into cells that naturally express GATA-3 (T lymphocytes and neuroblastoma cells). Furthermore, this same region of the locus confers developmentally appropriate expression in transgenic mice, but only in a subset of the tissues that naturally express the gene.


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