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Xinmeng Jasmine Mu

Researcher at Broad Institute

Publications -  32
Citations -  26561

Xinmeng Jasmine Mu is an academic researcher from Broad Institute. The author has contributed to research in topics: Genomics & Human genome. The author has an hindex of 26, co-authored 32 publications receiving 19845 citations. Previous affiliations of Xinmeng Jasmine Mu include W. M. Keck Foundation & Brigham and Women's Hospital.

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Journal ArticleDOI

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

An integrated map of structural variation in 2,504 human genomes

Peter H. Sudmant, +87 more
- 01 Oct 2015 - 
TL;DR: In this paper, the authors describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which are constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations.
Journal ArticleDOI

A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes

TL;DR: Functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies are described.