Global analysis of protein localization in budding yeast

By W. Huh, J. Falvo, Luke C. Gerke, Adam S. Carroll, R. Howson, Jonathan S. Weissman, E. O’Shea. Nature, 2003

4,508 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/417a636bad28c4ba50b5e4913c51935dba26a056

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In Molecular Biology

Citations in the corpus

Detecting overlapping protein complexes in protein-protein interaction networks (2012, methodology): “In our benchmarks, we have used the localization classification of Huh et al [35].”
Network-based prediction of protein function (2007, background): “Additional large-scale genomic data, such as deletion phenotypes (Brown et al , 2006), proteomic measurements (Kislinger et al , 2006) and protein cellular localization (Huh et al , 2003), can also be used in an integrative framework.”
Global analysis of protein expression in yeast (2003, background): “This set of proteins shows excellent overlap (>90%) with the set of green fluorescent protein (GFP) fusion proteins detected by fluorescence microscop”
Biogenesis and function of multivesicular bodies. (2007, background): “Although loss of Sna3 does not cause a sorting defect, other Sna3-like candidates exist among other polytopic membrane proteins that localize to the vacuolar lumen and exhibit no known function (Huh et al. 2003).”
Analyzing proteome topology and function by automated multidimensional fluorescence microscopy (2006, background): “, or the location of the cellular proteome in intact yeas”
Cell-type-specific isolation of ribosome-associated mRNA from complex tissues (2009, background): “Based on examination of large-scale yeast and mammalian protein tagging studies (9, 10), four core ribosome protein subunits (RPS17, RPS25, RPL22, and RPL36) that localized to the appropriate cellular compartments (nucleolus and cytoplasm) when C-terminally tagged with GFP were identified.”
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae (2006, methodology): “…in at least one organism associatedwith thatgroup and,importantly, notpossessingsimilarity toany gene from more distantly related organisms. calculated the weighted average of the fraction of proteins in each complex that maps to the same localization categories 5 (see Supplementary Information).”
Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010, background): “, Gene Ontology data [25]) as well as other biological information [26], may also be exploited to enhance the quality of predicted complexes.”
Classifying and segmenting microscopy images with deep multiple instance learning (2015, methodology): “Yeast protein localization screen : We used a genome wide screen of protein localization in yeast [28] containing images of 4,144 yeast strains from the yeast GFP (Green Fluorescent Protein) collection [29] representing ~71% of the yeast proteome.”