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Dissertation_Tyler_Cowman.pdf (5.81 MB)

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Compression and Version Control of Biological Networks

Cowman, Tyler
http://rave.ohiolink.edu/etdc/view?acc_num=case160701146645758

Abstract Details

2021, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
Due to advances in experimental techniques, modern biological research provides an extensive and diverse set of data for computational analyses. At the genomic level, high throughput sequencing is capable of producing massive amounts of patient specific genetic information. Moving toward the fields of proteomics and cellular signaling, biological association data such as protein interactions are frequently studied though a graph theoretical model. These protein-protein interaction networks (PPIs) can then be extended by adding additional forms of network data such expression quantitative trait loci (eQTL), and disease associations, resulting in expansive heterogeneous networks. Furthermore, these networks are often tissue specific, all together representing a massive number of semantically useful network variations. This motivates the development of efficient compressed data structures and algorithms for working with versioned biological network data. In this dissertation I present algorithms and data-structures for efficiently compressing and querying biological data in real time. LinDen is a method for detecting epistatically interacting loci in genome wide association (GWAS) data. By hierarchically compressing loci according to their linkage disequilibrium between one another, it is possible to perform a highly accurate heuristic search for epistatically interacting locus pairs. VerTIoN is a compressed versioned sparse graph data-structure applied to the storage, retrieval, and integration of heterogeneous tissue specific networks including: protein interactions, eQTL interactions, and disease associations. I show that this method substantially improves the storage efficiency of tissue specific network data, while allowing fast decompression and composition. Finally, the work with VerTIoN is extended by utilizing it as the back-end of a multi-user versioned network query engine, enabling arbitrary on the fly version composition. To demonstrate the utility of this approach I present PerKinE, a web tool for researchers to explore signaling pathways involved in kinase perturbation experiments.
Mehmet Koyutürk (Committee Chair)
Jing Li (Committee Member)
Yinghui Wu (Committee Member)
Rong Xu (Committee Member)
128 p.
Bioinformatics; Computer Science
Biological Networks; Graphs; Compression; Versioning;

Recommended Citations

Citations

  • Cowman, T. (2021). Compression and Version Control of Biological Networks [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case160701146645758

    APA Style (7th edition)

  • Cowman, Tyler. Compression and Version Control of Biological Networks. 2021. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case160701146645758.

    MLA Style (8th edition)

  • Cowman, Tyler. "Compression and Version Control of Biological Networks." Doctoral dissertation, Case Western Reserve University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case160701146645758

    Chicago Manual of Style (17th edition)

case160701146645758
116
© 2021, some rights reserved.Creative Commons License Compression and Version Control of Biological Networks by Tyler Cowman is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.