A layout algorithm for undirected compound graphs

Doğrusöz, Uğur; Giral, Erhan; Çetintaş, Ahmet; Çivril, Ali; Demir, Emek

doi:10.1016/j.ins.2008.11.017

Cited by 87 publications

(63 citation statements)

References 15 publications

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“…Contour plots for GI map experiments were generated using the fast_kde script written by Joe Kington (https://gist.github.com/joferkington/d95101a61a02e0ba63e5). Additional visualization tools available on Mendeley ([doi:10.17632/rdzk59n6j4.1]) and at weissmanlab.ucsf.edu/CRISPR/GImaps.html were generated using Bokeh 0.12.15 and Cytoscape.js 3.2.8 with Compound Spring Embedded-Bilkent layout (Dogrusoz et al, 2009). …”

Section: Star Methodsmentioning

confidence: 99%

Mapping the Genetic Landscape of Human Cells

Horlbeck

Wang

et al. 2018

Cell

266

241

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Seminal yeast studies have established the value of comprehensively mapping genetic interactions (GIs) for inferring gene function. Efforts in human cells using focused gene sets underscore the utility of this approach, but the feasibility of generating large-scale, diverse human GI maps remains unresolved. We developed a CRISPR interference platform for large-scale quantitative mapping of human GIs. We systematically perturbed 222,784 gene pairs in two cancer cell lines. The resultant maps cluster functionally related genes, assigning function to poorly characterized genes, including TMEM261, a new electron transport chain component. Individual GIs pinpoint unexpected relationships between pathways, exemplified by a specific cholesterol biosynthesis intermediate whose accumulation induces deoxynucleotide depletion, causing replicative DNA damage and a synthetic-lethal interaction with the ATR/9-1-1 DNA repair pathway. Our map provides a broad resource, establishes GI maps as a high-resolution tool for dissecting gene function, and serves as a blueprint for mapping the genetic landscape of human cells.

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Section: Star Methodsmentioning

confidence: 99%

Mapping the Genetic Landscape of Human Cells

Horlbeck

Wang

et al. 2018

Cell

266

241

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“…The thickness of an edges represents the correlation coefficient between the gene pair (the thicker, the higher). Visualized using the CoSE layout (Dogrusoz et al, 2009) in Cytoscape (Shannon et al, 2003). respectively, which are less significant.…”

Section: ) St-steiner Vs No Temporal Effectmentioning

confidence: 99%

Spatio-Temporal Gene Discovery For Autism Spectrum Disorder

Norman

2018

Preprint

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Abstract-Large-scale whole exome sequencing studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes because the genetic architecture of the disorder is highly complex, with roughly a thousand genes involved. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed in the literature, which assume ASD risk genes are working as a functional cluster. Even though all these methods use static gene interaction networks, the functional clustering of genes is bound to evolve during brain development and any disruptions are likely to have a cascading effect on the future associations. Thus, any approach that disregards the dynamic nature of neurodevelopment is limited in its power to detect ASD risk genes. While the assumption of a clustering of ASD genes is sensible, we hypothesize that the clustering is not necessarily static, but rather dynamic and spatiotemporal. Here, we present a spatio-temporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm (ST-Steiner) solves a prize-collecting Steiner forest-based problem on coexpression networks as a model for brain development and transfers information from precursor neurodevelopmental windows. We applied the algorithm on exome sequencing data of 3,871 samples and identified risk clusters using gene coexpression networks of early-fetal and mid-fetal periods. On an independent dataset, we demonstrate that incorporation of temporal dimension increases the prediction power and that the predicted clusters are hit (validated) more, compared to the state-of-theart methods. We also show that our predicted clusters show higher overlap with known ASD risk genes and genes associated with known ASD-related functionalities. ST-Steiner is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner

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“…Results from protein-protein interaction screens can be exported as network objects into the Perseus workflow. A specialized node-link visualization based on the opensource cytoscape.js library 25,26 with multiple layers of information, allows for easy interpretation of the results (Fig. 4c).…”

Section: Affinity Enrichment Ms Interactomicsmentioning

confidence: 99%

A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis

Rudolph

Cox

2018

Preprint

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Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g. with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plug-in architecture in a multi-lingual way, integrating analyses in C#, Python and R and is freely available at http://www.perseus-framework.org.

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A layout algorithm for undirected compound graphs

Cited by 87 publications

References 15 publications

Mapping the Genetic Landscape of Human Cells

Mapping the Genetic Landscape of Human Cells

Spatio-Temporal Gene Discovery For Autism Spectrum Disorder

A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis

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