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Data and -Omics-Driven Approaches to Understand the Heat Stress Response

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Data and -Omics-Driven Approaches to Understand the Heat Stress Response DATA AND -OMICS-DRIVEN APPROACHES TO UNDERSTAND THE HEAT STRESS RESPONSE: THE DEVELOPMENT OF SCALABLE TOOLS AND METHODS TO DRIVE HYPOTHESIS GENERATION by Allen Henry Hubbard, Jr. A dissertation submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics and Systems Biology Spring 2018 © 2018 Allen Henry Hubbard, Jr. All Rights Reserved DATA AND -OMICS-DRIVEN APPROACHES TO UNDERSTAND THE HEAT STRESS RESPONSE: THE DEVELOPMENT OF SCALABLE TOOLS AND METHODS TO DRIVE HYPOTHESIS GENERATION by Allen Henry Hubbard, Jr. Approved: __________________________________________________________ Limin Kung, Jr., Ph.D. Chair of the Department of Animal and Food Science Approved: __________________________________________________________ Mark W. Rieger, Ph.D. Dean of the College of Agriculture and Natural Resources Approved: __________________________________________________________ Ann L. Ardis, Ph.D. Senior Vice Provost for Graduate and Professional Education I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Carl J. Schmidt, Ph.D. Professor in charge of dissertation I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Abhyudai Singh, Ph.D. Member of dissertation committee I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Eric H. Lyons, Ph.D. Member of dissertation committee I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Hagit Shatkay, Ph.D. Member of dissertation committee I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Shawn W. Polson, Ph.D. Member of dissertation committee ACKNOWLEDGMENTS I could not have done this work without the support of my lab-mates, friends and family. Special thanks to my advisor, Dr. Carl Schmidt, and IT staff member Gregory Keane for his technical support. Also, great thanks to Heidi Van Every for her support and editorial skills. v TABLE OF CONTENTS LIST OF TABLES .................................................................................................... ix LIST OF FIGURES .................................................................................................... x ABSTRACT .......................................................................................................... xviii Chapter 1 INTRODUCTION .......................................................................................... 1 1.1 Context .................................................................................................. 1 1.2 RNA-seq ................................................................................................ 3 1.3 Data Burden to fRNAkenseq Software ................................................... 6 1.4 Integrating Bioinformatics APIs ........................................................... 11 1.5 Using API Driven Development and Downstream Analysis ................. 13 1.6 Tools to Proceed From Datasets to Insight ............................................ 18 1.7 Conclusion and the Way Forward ......................................................... 24 2 A POWERED-BY-CYVERSE TOOL FRNAKENSEQ ................................ 26 2.1 Introduction.......................................................................................... 26 2.1.1 CyVerse and the Science APIs .................................................. 28 2.1.2 Agave APIs to Run Sequencing Applications ........................... 29 2.1.3 API Integration Blueprint and Utility to Biologists ................... 30 2.1.4 fRNAkenseq Components: MapCount ...................................... 33 2.1.5 fRNAkenseq Components II: DiffExpress ................................ 39 2.1.6 Management of Resources Across Systems............................... 43 2.2 Discussion: Description of an Agave Apps as a Foundational Unit ....... 44 2.2.1 Comparison with Galaxy and CoGe Integration ........................ 47 3 BEYOND DIFFERENTIAL EXPRESSION: TISSUE ENRICHMENT ........ 49 3.1 Introduction.......................................................................................... 49 3.1.1 Motivation ................................................................................ 50 3.1.2 Criteria for Enrichment ............................................................. 50 vi 3.1.3 GTEx and Other Enrichment Approaches. ................................ 52 3.1.4 Improvement Over GTEx ......................................................... 53 3.1.5 Dealing with Non-normality ..................................................... 55 3.1.6 Models of Read Alignment Associated with Non-normality ...... 59 3.1.7 Using Chebyshev’s Theorem, as an extension of Markov’s Inequality ................................................................................. 61 3.1.8 Read Concentration and Probability .......................................... 62 3.2 Results ................................................................................................. 66 3.2.1 Comparison with GTEx ............................................................ 67 3.3 Discussion: FAANG and Community Need for Enrichment Strategies . 72 3.3.1 Breast Muscle Ubiquitin Profile ................................................ 74 3.3.2 Breast Muscle Transcription Factors ......................................... 76 3.3.3 Breast Muscle Spliceosome and Metabolic Physiology ............. 78 3.3.4 Cardiac Enriched Genes - TCA Cycle Metabolism and Mitochondrial Genes ................................................................ 80 3.3.5 Cardiac Transcription Factors ................................................... 84 3.3.6 Text Mining Comparison and Structural Differences ................ 86 3.3.7 Selective Enrichment of TCA Cycle Genes ............................... 88 3.3.8 Relationship Between Metabolism and Organelles .................... 90 3.3.9 Value of Enrichment Threshold: Comparative Evolution and Feature Subsetting .................................................................... 91 4 FROM ORGAN-ENRICHED MODULES TO MECHANISMS ................... 94 4.1 Introduction: Context for Metabolic Forks ............................................ 94 4.1.1 Established Context for Ratios as Extension of Previous Studies ...................................................................................... 95 4.1.2 Interpretation of Metabolite Ratios from a Biochemical Standpoint .............................................................................. 100 4.1.3 Context for Integrating Tissue Enrichment, Statistical Learning Techniques and Linear Models ............................................... 103 4.1.4 Identifying Biomolecules Associated with Heat Stress in the Liver ....................................................................................... 103 4.2 Methods: Combination of Statistical Learning Techniques ................. 105 4.3 Results ............................................................................................... 110 4.3.1 Geometric and Biological Consideration of each Statistical Learning Step ......................................................................... 115 vii 4.4 Discussion: ......................................................................................... 117 4.4.1 Heat Stress, Membranes and Lipids ........................................ 118 4.4.2 Antioxidants and Energy Burden ............................................ 120 4.4.3 The Metabolic Fork Consistent with Statistical Learning Pipeline .................................................................................. 122 5 TOWARDS CIRCUITRY REGULATING CARBON FLOW UNDER HEAT STRESS .......................................................................................... 127 5.1 Introduction........................................................................................ 127 5.1.1 Iterative Linear Models ........................................................... 129 5.2 Results ............................................................................................... 133 5.3 Discussion .......................................................................................... 142 5.3.1 Interpretation of Ratios ........................................................... 142 5.3.2 Regulation of Individual Forks ............................................... 144 5.3.3 Relationship between Cysteine and Stearoyl Ethanolamide, Accounted for by Circuit ........................................................ 149 5.3.4 Discussion of Mechanistic Regulation
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