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Exploring genetic interaction manifolds constructed from rich phenotypes
by
Jost, Marco
, Norman, Thomas M
, Horlbeck, Max A
, Replogle, Joseph M
, Xu, Albert
, Ge, Alex Y
, Weissman, Jonathan S
, Gilbert, Luke A
in
CRISPR
/ Genomics
/ Learning algorithms
/ Phenotypes
/ Ribonucleic acid
/ RNA
2019
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Do you wish to request the book?
Exploring genetic interaction manifolds constructed from rich phenotypes
by
Jost, Marco
, Norman, Thomas M
, Horlbeck, Max A
, Replogle, Joseph M
, Xu, Albert
, Ge, Alex Y
, Weissman, Jonathan S
, Gilbert, Luke A
in
CRISPR
/ Genomics
/ Learning algorithms
/ Phenotypes
/ Ribonucleic acid
/ RNA
2019
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Exploring genetic interaction manifolds constructed from rich phenotypes
Paper
Exploring genetic interaction manifolds constructed from rich phenotypes
2019
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Overview
Synergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.
Publisher
Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
Subject
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