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Sparse dictionary learning recovers pleiotropy from human cell fitness screens. | LitMetric

Sparse dictionary learning recovers pleiotropy from human cell fitness screens.

Cell Syst

Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02215, USA; Brigham and Women's Hospital and Harvard Medical School, Department of Medicine, Boston, MA 02215, USA. Electronic address:

Published: April 2022

AI Article Synopsis

  • High-throughput functional genomic screens traditionally assume each gene has a single function, but gene perturbations can actually lead to multiple biological effects due to a concept called pleiotropy.* -
  • The authors developed a modeling approach called Webster to analyze pleiotropy in gene fitness screens, successfully identifying multiple functions for proteins, distinct signaling pathways, and predicting the composition of protein complexes.* -
  • This method also improved the understanding of how compounds affect genetic functions and helped unravel the intricate genetic interactions in high-dimensional datasets from gene perturbation studies.*

Article Abstract

In high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway. In practice, a single gene perturbation may induce multiple cascading functional outcomes, a genetic principle known as pleiotropy. Here, we model pleiotropy in fitness screen collections by representing each gene perturbation as the sum of multiple perturbations of biological functions, each harboring independent fitness effects inferred empirically from the data. Our approach (Webster) recovered pleiotropic functions for DNA damage proteins from genotoxic fitness screens, untangled distinct signaling pathways upstream of shared effector proteins from cancer cell fitness screens, and predicted the stoichiometry of an unknown protein complex subunit from fitness data alone. Modeling compound sensitivity profiles in terms of genetic functions recovered compound mechanisms of action. Our approach establishes a sparse approximation mechanism for unraveling complex genetic architectures underlying high-dimensional gene perturbation readouts.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035054PMC
http://dx.doi.org/10.1016/j.cels.2021.12.005DOI Listing

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