Sharyl L. Wong

Harvard University

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Combining biological networks to predict genetic interactions

Proceedings of the National Academy of Sciences (2004)

Sharyl L. Wong Lan V. Zhang Amy H.Y. Tong Zhijian Li Debra S. Goldberg

Genetic interactions define overlapping functions and compensatory pathways. In particular, synthetic sick or lethal (SSL) genetic interactions are important for understanding how an organism tolerates random mutation, i.e., genetic robustness. Comprehensive identification of SSL relationships remains far from complete in any organism, because mapping these networks is highly labor intensive. The ability to predict SSL interactions, however, could efficiently guide further SSL discovery. Toward this end, we predicted pairs of SSL genes in Saccharomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, including localization, mRNA expression, physical interaction, protein function, and characteristics of network topology. Experimental evidence demonstrated the reliability of this strategy, which, when extended to human SSL interactions, may prove valuable in discovering drug targets for cancer therapy and in identifying genes responsible for multigenic diseases.

Robustness

Identification

Model Organism

10.1073/pnas.0406614101

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Citations (248)

Transcriptional Compensation for Gene Loss Plays a Minor Role in Maintaining Genetic Robustness in Saccharomyces cerevisiae

Genetics (2005)

Sharyl L. Wong Frederick P. Roth

If a gene is mutated and its function lost, are compensatory genes upregulated? We investigated whether genes are transcriptionally upregulated when their synthetic sick or lethal (SSL) partners are lost. We identified several new examples; however, remarkably few SSL pairs exhibited this phenomenon, suggesting that transcriptional compensation by SSL partners is a rare mechanism for maintaining genetic robustness.

Robustness

Genetic screen

Loss function

10.1534/genetics.105.046060

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Citations (27)

Editorial board and contents

Trends in Genetics (2005)

Simone Hoegg Axel Meyer Sharyl L. Wong Lan Zhang Frederick P. Roth

Editorial board

10.1016/s0168-9525(05)00176-9

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Citations (0)

Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiaeinteraction network

Journal of Biology (2005)

Lan V. Zhang Oliver D. King Sharyl L. Wong Debra S. Goldberg Amy HY Tong

Abstract Background Large-scale studies have revealed networks of various biological interaction types, such as protein-protein interaction, genetic interaction, transcriptional regulation, sequence homology, and expression correlation. Recurring patterns of interconnection, or 'network motifs', have revealed biological insights for networks containing either one or two types of interaction. Results To study more complex relationships involving multiple biological interaction types, we assembled an integrated Saccharomyces cerevisiae network in which nodes represent genes (or their protein products) and differently colored links represent the aforementioned five biological interaction types. We examined three- and four-node interconnection patterns containing multiple interaction types and found many enriched multi-color network motifs. Furthermore, we showed that most of the motifs form 'network themes' – classes of higher-order recurring interconnection patterns that encompass multiple occurrences of network motifs. Network themes can be tied to specific biological phenomena and may represent more fundamental network design principles. Examples of network themes include a pair of protein complexes with many inter-complex genetic interactions – the 'compensatory complexes' theme. Thematic maps – networks rendered in terms of such themes – can simplify an otherwise confusing tangle of biological relationships. We show this by mapping the S. cerevisiae network in terms of two specific network themes. Conclusion Significantly enriched motifs in an integrated S. cerevisiae interaction network are often signatures of network themes, higher-order network structures that correspond to biological phenomena. Representing networks in terms of network themes provides a useful simplification of complex biological relationships.

Thematic map

10.1186/jbiol23

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Citations (194)

Global Mapping of the Yeast Genetic Interaction Network

Science (2004)

Amy H.Y. Tong Guillaume Lesage Gary D. Bader Huiming Ding Hong Xu

A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Interaction network

Epistasis

Gene regulatory network

Gene interaction

10.1126/science.1091317

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Citations (2,099)

Discovering functional relationships: biochemistry versus genetics

Trends in Genetics (2005)

Sharyl L. Wong Lan V. Zhang Frederick P. Roth

Budding yeast

10.1016/j.tig.2005.06.006

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Citations (23)

Predicting co-complexed protein pairs using genomic and proteomic data integration

BMC Bioinformatics (2004)

Lan V. Zhang Sharyl L. Wong Oliver D. King Frederick P. Roth

Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. Using a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs. We demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

Proteome

10.1186/1471-2105-5-38

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Citations (181)

Towards a proteome-scale map of the human protein–protein interaction network

Nature (2005)

Jean‐François Rual K. Venkatesan Tong Hao Tomoko Hirozane-Kishikawa Amélie Dricot

Interactome

Proteome

Human proteome project

Protein Interaction Networks

Interaction network

Human proteins

10.1038/nature04209

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Citations (2,928)