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Merck

The genetic landscape of a cell.

Science (New York, N.Y.) (2010-01-23)
Michael Costanzo, Anastasia Baryshnikova, Jeremy Bellay, Yungil Kim, Eric D Spear, Carolyn S Sevier, Huiming Ding, Judice L Y Koh, Kiana Toufighi, Sara Mostafavi, Jeany Prinz, Robert P St Onge, Benjamin VanderSluis, Taras Makhnevych, Franco J Vizeacoumar, Solmaz Alizadeh, Sondra Bahr, Renee L Brost, Yiqun Chen, Murat Cokol, Raamesh Deshpande, Zhijian Li, Zhen-Yuan Lin, Wendy Liang, Michaela Marback, Jadine Paw, Bryan-Joseph San Luis, Ermira Shuteriqi, Amy Hin Yan Tong, Nydia van Dyk, Iain M Wallace, Joseph A Whitney, Matthew T Weirauch, Guoqing Zhong, Hongwei Zhu, Walid A Houry, Michael Brudno, Sasan Ragibizadeh, Balázs Papp, Csaba Pál, Frederick P Roth, Guri Giaever, Corey Nislow, Olga G Troyanskaya, Howard Bussey, Gary D Bader, Anne-Claude Gingras, Quaid D Morris, Philip M Kim, Chris A Kaiser, Chad L Myers, Brenda J Andrews, Charles Boone
ZUSAMMENFASSUNG

A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

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Marke
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Trypsin, Singles, Proteomik-Qualität