Browsing by Author "Siper, M. C."

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    Analyzing causal relationships in proteomic profiles using CausalPath
    (Cell Press, 2021-12-17) Luna, A.; Siper, M. C.; Korkut, A.; Durupinar, F.; Aslan, J. E.; Sander, C.; Demir, E.; Babur, O.; Doğrusöz, Uğur
    CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset.
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    Causal interactions from proteomic profiles: Molecular data meet pathway knowledge
    (Cell Press, 2021-06) Babur, Ö.; Luna, A.; Korkut, A.; Durupınar, F.; Siper, M. C.; Doğrusöz, Uğur; Jacome, A. S. V.; Peckner, R.; Christiansen, K. E.; Jaffe, J.D; Spellman, P.T.; Aslan, J. E.; Sander, C.; Demir, E.
    We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.
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    Pathway commons 2019 update: integration, analysis and exploration of pathway data
    (Oxford University Press, 2020) Rodchenkov, I.; Babur, Ö.; Luna, A.; Aksoy, B. A.; Wong, J. V.; Fong, D.; Franz, M.; Siper, M. C.; Cheung, M.; Wrana, M.; Mistry, H.; Mosier, L.; Dlin, J.; Wen, Q.; O’Callaghan, C.; Li, W.; Elder, G.; Smith, P. T.; Dallago, C.; Cerami, E.; Gross, B.; Doğrusöz, Uğur; Demir, E.; Bader, G. D.; Sander, C.
    Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.