V
Valerie Obenchain
Researcher at Fred Hutchinson Cancer Research Center
Publications - 9
Citations - 3886
Valerie Obenchain is an academic researcher from Fred Hutchinson Cancer Research Center. The author has contributed to research in topics: Bioconductor & Human Microbiome Project. The author has an hindex of 7, co-authored 9 publications receiving 3066 citations. Previous affiliations of Valerie Obenchain include Roswell Park Cancer Institute & City University of New York.
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Journal ArticleDOI
Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber,Vincent J. Carey,Robert Gentleman,Simon Anders,Marc R. J. Carlson,Benilton S. Carvalho,Héctor Corrada Bravo,Sean Davis,Laurent Gatto,Thomas Girke,Raphael Gottardo,Florian Hahne,Kasper D. Hansen,Rafael A. Irizarry,Michael S. Lawrence,Michael I. Love,James W. MacDonald,Valerie Obenchain,Andrzej K. Oleś,Hervé Pagès,Alejandro Reyes,Paul Shannon,Gordon K. Smyth,Dan Tenenbaum,Levi Waldron,Martin Morgan +25 more
TL;DR: An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.
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FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model
TL;DR: A new stochastic model of the spread of influenza across a large population is developed, which has realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza.
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VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants
Valerie Obenchain,Michael F. Lawrence,Vincent J. Carey,Stephanie M. Gogarten,Paul Shannon,Martin Morgan +5 more
TL;DR: VariantAnnotation allows ready access to additional R / Bioconductor facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources.
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Accessible, curated metagenomic data through ExperimentHub
Edoardo Pasolli,Lucas Schiffer,Paolo Manghi,Audrey Renson,Valerie Obenchain,Duy Tin Truong,Francesco Beghini,Faizan Malik,Marcel Ramos,Marcel Ramos,Jennifer Beam Dowd,Jennifer Beam Dowd,Curtis Huttenhower,Curtis Huttenhower,Martin Morgan,Nicola Segata,Levi Waldron +16 more
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about the human microbiome and its role in disease and disease progression.
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Risk prediction using genome‐wide association studies
TL;DR: The results suggest that utilizing a larger number of SNPs than those which reach genome‐wide significance, for example using the lasso, improves the construction of risk prediction models.