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Wenwei Zhang

Researcher at Beijing Institute of Genomics

Publications -  40
Citations -  21872

Wenwei Zhang is an academic researcher from Beijing Institute of Genomics. The author has contributed to research in topics: Genome & Genomics. The author has an hindex of 15, co-authored 40 publications receiving 15558 citations.

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A global reference for human genetic variation.

Adam Auton, +517 more
- 01 Oct 2015 - 
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.

A global reference for human genetic variation

Adam Auton, +479 more
TL;DR: The 1000 Genomes Project as mentioned in this paper provided a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and reported the completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole genome sequencing, deep exome sequencing and dense microarray genotyping.
Journal ArticleDOI

Phylogenomics resolves the timing and pattern of insect evolution

Bernhard Misof, +105 more
- 07 Nov 2014 - 
TL;DR: The phylogeny of all major insect lineages reveals how and when insects diversified and provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.
Journal ArticleDOI

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Zhenqiang Su, +164 more
- 01 Sep 2014 - 
TL;DR: The complete SEQC data sets, comprising >100 billion reads, provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings, and measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling.