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Jess Shen

dblp:122/6848 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics
genome-wide association study
0.322012
A high-performance computing toolset for relatedness and principal component analysis of SNP data · Bioinform. 2012
GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies · Bioinform. 2012
Bioinformatics and computational biology › statistical genetics
genotype-phenotype association
0.112012
GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies · Bioinform. 2012
Bioinformatics and computational biology › population genetics
identity by descent
0.112012
A high-performance computing toolset for relatedness and principal component analysis of SNP data · Bioinform. 2012
Bioinformatics and computational biology › biostatistics › statistical bioinformatics
statistical genomics
0.112012
GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies · Bioinform. 2012
Parallel and multicore computing
parallel programming models and runtimes
0.012012
A high-performance computing toolset for relatedness and principal component analysis of SNP data · Bioinform. 2012

Methods — techniques the papers use, named apart from their topics

multithreading · 0.3c/c++ optimization · 0.3r/bioconductor · 0.1netCDF · 0.1
YearPublicationVenuePosition
2012 GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies
abstract
Abstract Summary: GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R’s memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation. Availability and implementation: GWASTools is implemented in R and is available from Bioconductor (http://www.bioconductor.org). An extensive vignette detailing a recommended work flow is included. Contact: [email protected]
Stephanie M. Gogarten, Tushar Bhangale, Matthew P. Conomos, Cecelia A. Laurie, Caitlin P. McHugh, Ian S. Painter, Xiuwen Zheng 0001, David R. Crosslin, Thomas Lumley, Sarah C. Nelson, Ken Rice, Jess Shen, Rohit Swarnkar, Bruce S. Weir, Cathy C. Laurie
Bioinform.13
2012 A high-performance computing toolset for relatedness and principal component analysis of SNP data
abstract
Abstract Summary: Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized. Benchmarks show the uniprocessor implementations of PCA and identity-by-descent are ∼8–50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs, respectively, and can be sped up to 30–300-fold by using eight cores. SNPRelate can analyse tens of thousands of samples with millions of SNPs. For example, our package was used to perform PCA on 55 324 subjects from the ‘Gene-Environment Association Studies’ consortium studies. Availability and implementation: gdsfmt and SNPRelate are available from R CRAN (http://cran.r-project.org), including a vignette. A tutorial can be found at https://www.genevastudy.org/Accomplishments/software. Contact: [email protected]
Xiuwen Zheng 0001, Jess Shen, Stephanie M. Gogarten, Cathy C. Laurie, Bruce S. Weir
Bioinform.3