Automated data integration for developmental biological research.
In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research.
Zhong
Weiwei
W
HHMI and Division of Biology, Caltech, 1200 E California Blvd, Pasadena, CA 91125, USA.
Sternberg
Paul W
PW
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015509:Developmental Biology / Q000379:methods*; D016678:Genome / Q000235:genetics*; D048788:Growth and Development / Q000235:genetics*; D051379:Mice; D012106:Research; D018511:Systems Integration