Author

Gad Getz

Primary ORCID profile (0000-0002-0936-0753 · 2 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Gad GetziD2 papers

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Research Focus:eQTL / molecular QTL mapping· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Adam Novak· 1 shared Alden D. Deran· 1 shared Alexis Battle· 1 shared Alireza Kashani· 1 shared Andrew A. Brown· 1 shared Andrew B. Nobel· 1 shared Andrey A. Shabalin· 1 shared Anthony D. JosephiD· 1 shared

Activity by Year

2015-2016 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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1 shared paperBioinformatics

Adam Novak

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Author profile →

3 papers

Recent·Most cited

Local genetic effects on gene expression across 44 human tissues

François Aguet, Andrew A. Brown, Stephane E. Castel, Joe R. Davis, Pejman Mohammadi, Ayellet V. Segrè, Zachary Zappala, Nathan S. Abell, Laure Frésard, Eric R. Gamazon, Ellen Gelfand, Michael J. Gloudemans, Yuan He, Farhad Hormozdiari, Xiao Li, , Stephen B. Montgomery

This study identifies cis-eQTLs across 44 human tissues using GTEx data, showing 88% of genes have cis-eQTLs and demonstrating their utility in understanding complex trait etiology.

bioRxiv · Sep 9, 2016 · PreprintGenomics

Rapid and efficient analysis of 20,000 RNA-seq samples with Toil

John Vivian, Arjun Rao, Frank Austin Nothaft, Christopher Ketchum, Joel Armstrong, Adam Novak, Jacob Pfeil, Jake Narkizian, Alden D. Deran, Audrey Musselman-Brown, Hannes Schmidt, Peter Amstutz, Brian Craft, Mary Goldman, Kate Rosenbloom, , Benedict Paten

The paper demonstrates Toil's ability to efficiently process 20,000 RNA-seq samples to create a batch-effect-free meta-analysis across five datasets.

bioRxiv · Jul 7, 2016Bioinformatics

A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Heiko Horn, Michael S. Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Jesse S. Boehm, Gad Getz, Kasper Lage

The paper quantifies network mutation burdens across 21 tumor types to identify putative cancer genes, finding 62 new candidates with greater sensitivity for low-frequency mutations.

bioRxiv · Aug 25, 2015 · PreprintCancer Biology