Author

Chet Birger

Broad Institute

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:experimental assay· 2 Assay:whole-genome sequencing· 2 Research Focus:batch correction / integration· 1 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Assay:DNA methylation· 1 Assay:proteomics· 1

Frequent Co-Authors

Gad A. GetziD· 2 shared Megan Hanna· 2 shared Adam Novak· 1 shared Alden D. Deran· 1 shared Alexander Baumann· 1 shared Anthony D. JosephiD· 1 shared Anthony PhilippakisiD· 1 shared Arjun Rao· 1 shared

Activity by Year

2016-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Gad A. GetzORCID

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Author profile →

3 papers

Recent·Most cited

PANOPLY: A cloud-based platform for automated and reproducible proteogenomic data analysis

D.R. Mani, Myranda Maynard, Ramani Bhupendra Kothadia, Karsten Krug, Karen E. Christianson, David I. Heiman, Karl R. Clauser, Chet Birger, Gad A. Getz, Steven A. Carr

PANOPLY is a cloud-based platform that automates and reproducibly analyzes proteogenomic data by integrating genomic, transcriptomic, proteomic, and post-translational modification data.

Nature Methods · May 26, 2021Peer-reviewedBioinformatics

FireCloud, a scalable cloud-based platform for collaborative genome analysis: Strategies for reducing and controlling costs

Chet Birger, Megan Hanna, Edward A. Salinas, Jason Neff, Gordon Saksena, Dimitri G. Livitz, Daniel Rosebrock, Chip Stewart, Ignaty Leshchiner, Alexander Baumann, Douglas Voet, Kristian Cibulskis, Eric Banks, Anthony Philippakis, Gad A. Getz

Four levers for cheaper cloud genomics on FireCloud: size virtual disks dynamically, tune VM provisioning from monitoring data, buy preemptible machines, and parallelise tasks at the right width.

bioRxiv · Nov 3, 2017 · PreprintBioinformatics

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