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Liangjiang Wang

Researcher at Clemson University

Publications -  81
Citations -  7842

Liangjiang Wang is an academic researcher from Clemson University. The author has contributed to research in topics: Gene & Genomics. The author has an hindex of 30, co-authored 77 publications receiving 7065 citations. Previous affiliations of Liangjiang Wang include Kansas State University & University of Georgia.

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The genome of the model beetle and pest Tribolium castaneum.

Stephen Richards, +190 more
- 24 Apr 2008 - 
TL;DR: Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products.
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The phenylpropanoid pathway and plant defence—a genomics perspective

TL;DR: The availability of the complete genome sequence of Arabidopsis thaliana, and the extensive expressed sequence tag resources in other species allow, for the first time, a full appreciation of the comparative genetic complexity of the phenylpropanoid pathway across species.

The genome of the model beetle and pest Tribolium castaneum

TL;DR: Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products as discussed by the authors.
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The WRKY transcription factor superfamily: its origin in eukaryotes and expansion in plants

TL;DR: A model to explain the WRKY family's origin in eukaryotes and expansion in plants is proposed and it is suggested that the C-terminal domain of the two- WRKY-domain encoding gene appears to be the ancestor of the single-WRKY- domain encoding genes, and that theWRKY domains may be phylogenetically classified into five groups.
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BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences.

TL;DR: BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data and the SVM models appear to be more accurate and more efficient for online predictions.