Author

Aviad Tsherniak

Broad InstituteORCID 0000-0002-3797-1877Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:statistical model· 5 Organism:human· 4 Research Focus:benchmarking· 3 Assay:bulk RNA-seq· 3 Assay:CRISPR screen· 3 Research Focus:differential expression· 2 Research Focus:variant effect prediction· 2 Approach:machine learning (classical)· 1

Frequent Co-Authors

James M. McFarlandiD· 5 shared Jesse S. BoehmiD· 4 shared David E. RootiD· 3 shared Francisca VázqueziD· 3 shared Joshua M. DempsteriD· 3 shared William C. HahniD· 3 shared Isabella BoyleiD· 2 shared Jordan BryaniD· 2 shared

Activity by Year

2017-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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5 shared papersBioinformatics

James M. McFarlandORCID

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Author profile →

6 papers

Recent·Most cited

Integrated cross-study datasets of genetic dependencies in cancer

Clare Pacini, Joshua M. Dempster, Isabella Boyle, Emanuel Gonçalves, Hanna Najgebauer, Emre Karakoç, Dieudonne J. van der Meer, Andrew S. Barthorpe, Howard W. Lightfoot, Patricia Jaaks, James M. McFarland, Mathew J. Garnett, Aviad Tsherniak, Francesco Iorio

The paper integrates two large CRISPR-Cas9 screens to improve the detection of gene dependencies and cancer vulnerabilities by addressing biases and batch effects.

Nature Communications · Mar 12, 2021Peer-reviewedBioinformatics

Chronos: a CRISPR cell population dynamics model

Joshua M. Dempster, Isabella Boyle, Francisca Vázquez, David E. Root, Jesse S. Boehm, William C. Hahn, Aviad Tsherniak, James M. McFarland

Chronos infers gene knockout fitness effects by explicitly modelling cell proliferation dynamics after knockout, using longitudinal readouts and correcting several known screen biases.

bioRxiv · Feb 25, 2021 · PreprintBioinformatics

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Andrew Jones, Aviad Tsherniak, James M. McFarland

The paper integrates transcriptional profiles with viability data to identify a generalized transcriptional signature linked to reduced viability in cancer cell lines across various perturbations.

bioRxiv · Mar 4, 2020 · PreprintBioinformatics

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

James M. McFarland, Zandra V. Ho, Guillaume Kugener, Joshua M. Dempster, Philip G. Montgomery, Jordan Bryan, John Michael Krill-Burger, Thomas M. Green, Francisca Vázquez, Jesse S. Boehm, Todd R. Golub, William C. Hahn, David E. Root, Aviad Tsherniak

The paper improves gene dependency estimation from RNAi screens by using model-based normalization and data integration, enhancing accuracy and enabling cross-dataset analysis.

Nature Communications · Oct 29, 2018Peer-reviewedGenomics

GeNets: A unified web platform for network-based analyses of genomic data

Taibo Li, April Kim, Joseph Rosenbluh, Heiko Horn, Liraz Greenfeld, David An, Andrew R. Zimmer, Arthur Liberzon, Jon Bistline, Ted Natoli, Yang Li, Aviad Tsherniak, Rajiv Narayan, Aravind Subramanian, Ted Liefeld, , Kasper Lage

GeNets provides a platform to compare genomic networks using machine learning and allows analysis of genetic and RNA sequencing data.

Nature Methods · Jun 15, 2018Peer-reviewedGenomics

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

Robin M. Meyers, Jordan Bryan, James M. McFarland, Barbara A. Weir, Ann E. Sizemore, Han Xu, Neekesh V. Dharia, Philip G. Montgomery, Glenn S. Cowley, Sasha Pantel, Amy B. Goodale, Yenarae Lee, Levi D. Ali, Guozhi Jiang, Rakela Lubonja, , Aviad Tsherniak

The paper develops CERES, a computational method to correct copy-number effects in CRISPR-Cas9 essentiality screens, improving specificity in cancer cell line studies.

Nature Genetics · Oct 30, 2017Peer-reviewedGenomics