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Zhao Xu

Researcher at Fudan University

Publications -  19
Citations -  3133

Zhao Xu is an academic researcher from Fudan University. The author has contributed to research in topics: Genome & Gene prediction. The author has an hindex of 14, co-authored 19 publications receiving 2548 citations. Previous affiliations of Zhao Xu include Chinese Academy of Sciences & Beijing Institute of Genomics.

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LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons

TL;DR: LTR_FINDER is a system capable of scanning large-scale sequences rapidly and the first web server for ab initio LTR retrotransposon finding and illustrated its usage and performance on the genome of Saccharomyces cerevisiae.
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The Genomes of Oryza sativa: a history of duplications.

Jun Yu, +134 more
- 01 Feb 2005 - 
TL;DR: A more inclusive new approach for analyzing duplication history is introduced here, which reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications.
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A fungal phylogeny based on 82 complete genomes using the composition vector method

TL;DR: Using different input data and methodology, the CVTree approach is a good complement to the standard methods and brings about more confidence to the current understanding of the fungal branch of TOL.
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CVTree update: a newly designed phylogenetic study platform using composition vectors and whole genomes

TL;DR: The CVTree web server presented here is a new implementation of the whole genome-based, alignment-free composition vector (CV) method for phylogenetic analysis that is more efficient and user-friendly than the previously published version in the 2004 web server issue of Nucleic Acids Research.
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Vertebrate gene predictions and the problem of large genes.

TL;DR: It is shown that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistently low false-negative rate, which means genes of the most extreme sizes, especially very large genes, are most likely to be incorrectly predicted.