GraphSpace: stimulating interdisciplinary collaborations in network biology

Bharadwaj, Aditya; Singh, Divit P.; Ritz, Anna; Tegge, Allison N.; Poirel, Christopher L.; Kraikivski, Pavel; Adames, Neil R.; Luther, Kurt; Kale, Shiv D.; Peccoud, Jean; Tyson, John J.; Murali, T. M.

doi:10.1093/bioinformatics/btx382

Cited by 28 publications

(25 citation statements)

References 6 publications

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“…Hypergraph visualization. We visualize hypergraphs using GraphSpace [48], a webbased collaborative network visualization tool. The hypergraphs are available as interactive networks on GraphSpace using the with the GLBio2019 tag (http://graphspace.org/graphs/?…”

Section: Data Formats and Representationsmentioning

confidence: 99%

Hypergraph-based connectivity measures for signaling pathway topologies

et al. 2019

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Author summarySignaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules "connected" in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are "connected" in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is "downstream" of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases.Hypergraph-based connectivity measures for signaling pathway topologies PLOS Computational Biology | https://doi.org/10.

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Section: Data Formats and Representationsmentioning

confidence: 99%

Hypergraph-based connectivity measures for signaling pathway topologies

et al. 2019

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“…proteins may belong to multiple compartments (and will be lighter shades). Networks were generated using GraphSpace [39], and are available at http://graphspace.org/graphs/?query=tags:LocPL.…”

Section: Comparison Of Pathway Reconstructionsmentioning

confidence: 99%

Integrating Protein Localization with Automated Signaling Pathway Reconstruction

Youssef

Law

Ritz

2019

Preprint

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Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information. We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways. LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

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“…The width of an edge is proportional to its weight. This graph, as well as the network connecting the 30 genes with the highest score computed by Local, are available on GraphSpace, the interactive graph sharing website (FastSinkSource: https://graphspace.org/graphs/27042, Local: https://graphspace.org/graphs/27041) [24]. The 30 highest scoring genes computed by FastSinkSource and Local contained 13 and 9 of the 15 left-out positive examples, respectively.…”

Section: Examining the Improvement Of Network Propagation Over The Bamentioning

confidence: 99%

Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network

Law

Kale

Murali

2019

Preprint

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The rapid rise in newly sequenced genomes requires the development of computational methods to supplement experimental functional annotations. The challenge that arises is to develop methods for gene function prediction that integrate information for multiple species while also operating on a genomewide scale. We develop a label propagation algorithm called FastSinkSource and apply it to a sequence similarity network integrated with species-specific heterogeneous data for 19 pathogenic bacterial species. By using mathematically-provable bounds on the rate of progress of FastSinkSource during power iteration, we decrease the running time by a factor of 100 or more without sacrificing prediction accuracy. To demonstrate scalability, we expand to a 73-million edge network across 200 bacterial species while maintaining accuracy and efficiency improvements. Our results point to the feasibility and promise of multi-species, genomewide gene function prediction, especially as more experimental data and annotations become available for a diverse variety of organisms. * Corresponding author: murali@cs.vt.edu 1 iterations. This property enables us to decrease the running time of FastSinkSource by a factor of 100 or more without sacrificing prediction accuracy.We demonstrate the applicability of FastSinkSource using a leave-one-species-out (LOSO) validation framework that mimics the challenge of making large-scale functional predictions for the genome of a newlysequenced species where none of its genes have any annotations. We apply this evaluation framework to a sequence similarity network (SSN) of 19 bacterial species integrated with intra-species networks from the STRING database. FastSinkSource maintains accuracy and efficiency improvements even when applied to a large-scale 200-species network with 73 million edges.

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GraphSpace: stimulating interdisciplinary collaborations in network biology

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 28 publications

References 6 publications

Hypergraph-based connectivity measures for signaling pathway topologies

Hypergraph-based connectivity measures for signaling pathway topologies

Integrating Protein Localization with Automated Signaling Pathway Reconstruction

Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network

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