Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways

2003 ◽  
Vol 22 (1) ◽  
pp. 62-69 ◽  
Author(s):  
Ainslie B Parsons ◽  
Renée L Brost ◽  
Huiming Ding ◽  
Zhijian Li ◽  
Chaoying Zhang ◽  
...  
Keyword(s):  
Bioactive Compounds ◽  
Genetic Interaction ◽  
Interaction Data ◽  
Chemical Genetic ◽  
Data Links ◽  
Cellular Target ◽  
Genetic Interaction Data

scholarly journals MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action

2017 ◽  
Vol 34 (7) ◽  
pp. 1251-1252 ◽  
Author(s):  
Justin Nelson ◽  
Scott W Simpkins ◽  
Hamid Safizadeh ◽  
Sheena C Li ◽  
Jeff S Piotrowski ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Data Repository ◽  
Interaction Data ◽  
Chemical Genetic ◽  
Web Resource ◽  
Modes Of Action ◽  
Genetic Interaction Data

scholarly journals Improving prediction of compound function from chemical structure using chemical-genetic networks

2017 ◽  
Author(s):  
Hamid Safizadeh ◽  
Scott W. Simpkins ◽  
Justin Nelson ◽  
Chad L. Myers
Keyword(s):  
Machine Learning ◽  
Biological Activity ◽  
Chemical Structure ◽  
Genetic Interaction ◽  
Structural Similarity ◽  
Superior Performance ◽  
Interaction Data ◽  
Similarity Coefficients ◽  
Chemical Genetic ◽  
Genetic Interaction Data

ABSTRACTThe drug discovery process can be significantly improved through understanding how the structure of chemical compounds relates to their function. A common paradigm that has been used to filter and prioritize compounds is ligand-based virtual screening, where large libraries of compounds are queried for high structural similarity to a target molecule, with the assumption that structural similarity is predictive of similar biological activity. Although the chemical informatics community has already proposed a wide range of structure descriptors and similarity coefficients, a major challenge has been the lack of systematic and unbiased benchmarks for biological activity that covers a broad range of targets to definitively assess the performance of the alternative approaches.We leveraged a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae that our labs have recently generated, covering more than 13,000 compounds from the RIKEN NPDepo and several NCI, NIH, and GlaxoSmithKline (GSK) compound collections. Supportive of the idea that chemical-genetic interaction data provide an unbiased proxy for biological functions, we found that many commonly used structural similarity measures were able to predict the compounds that exhibited similar chemical-genetic interaction profiles, although these measures did exhibit significant differences in performance. Using the chemical-genetic interaction profiles as a basis for our evaluation, we performed a systematic benchmarking of 10 different structure descriptors, each combined with 12 different similarity coefficients. We found that the All-Shortest Path (ASP) structure descriptor paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several different compound collections.We further describe a machine learning approach that improves the ability of the ASP metric to capture biological activity. We used the ASP fingerprints as input for several supervised machine learning models and the chemical-genetic interaction profiles as the standard for learning. We found that the predictive power of the ASP fingerprints (as well as several other descriptors) could be substantially improved by using support vector machines. For example, on held-out data, we measured a 5-fold improvement in the recall of biologically similar compounds at a precision of 50% based upon the ASP fingerprints. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining chemical structure descriptors can be a powerful approach to improving prediction of biological function from structure.

scholarly journals Maximal Extraction of Biological Information from Genetic Interaction Data

2009 ◽  
Vol 5 (4) ◽  
pp. e1000347 ◽  
Author(s):  
Gregory W. Carter ◽  
David J. Galas ◽  
Timothy Galitski
Keyword(s):  
Genetic Interaction ◽  
Biological Information ◽  
Interaction Data ◽  
Genetic Interaction Data

scholarly journals Automated identification of pathways from quantitative genetic interaction data

2010 ◽  
Vol 6 (1) ◽  
pp. 379 ◽  
Author(s):  
Alexis Battle ◽  
Martin C Jonikas ◽  
Peter Walter ◽  
Jonathan S Weissman ◽  
Daphne Koller
Keyword(s):  
Genetic Interaction ◽  
Interaction Data ◽  
Automated Identification ◽  
Quantitative Genetic ◽  
Genetic Interaction Data

scholarly journals Efficient strategies for screening large-scale genetic interaction networks

2017 ◽  
Author(s):  
Raamesh Deshpande ◽  
Justin Nelson ◽  
Scott W. Simpkins ◽  
Michael Costanzo ◽  
Jeff S. Piotrowski ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimal Algorithm ◽  
Genetic Interaction ◽  
Genetic Screens ◽  
Chemical Genetic ◽  
Genome Wide ◽  
Powerful Approach ◽  
Interaction Screening ◽  
Genetic Interaction Data

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.To address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

scholarly journals Porting and using PanGIA for Cytoscape 3: challenges and solutions

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 150 ◽  
Author(s):  
David Welker ◽  
Barry Demchak
Keyword(s):  
Data Model ◽  
Genetic Interaction ◽  
Third Party ◽  
Physical Interaction ◽  
Interaction Data ◽  
Scientific Analysis ◽  
Genetic Interaction Data

Much of the biologically significant functionality in Cytoscape is contained within third-party add-ons, called plugins in Cytoscape 2 and apps in Cytoscape 3. In the transition from Cytoscape 2 to Cystoscape 3, some of the underlying assumptions upon which plugins relied changed, requiring a significant porting effort for plugins to work as Cytoscape 3 apps. PanGIA is a Cytoscape add-on (http://apps.cytoscape.org/apps/pangia) designed to analyze and visualize genetic interaction data in light of physical interaction data. In order to convert the PanGIA plugin to an app, various challenges, including those related to a transformed data model, concurrency, and randomization had to be overcome. In the process, the ability to control randomization was added to the GUI, a feature which was not only integral to the porting process, but which also ensures more easily reproducible scientific analysis for PanGIA users. Most authors of Cytoscape 2 plugins will face similar challenges porting their software to work with Cytoscape 3, and this paper gives details of how the PanGIA port addressed them.

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