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Machine Learning Tools for Mrna Isoform Function Prediction

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Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2019 Machine learning tools for mRNA isoform function prediction Gaurav Kandoi Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Bioinformatics Commons, Computer Sciences Commons, and the Genetics Commons Recommended Citation Kandoi, Gaurav, "Machine learning tools for mRNA isoform function prediction" (2019). Graduate Theses and Dissertations. 17479. https://lib.dr.iastate.edu/etd/17479 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Machine learning tools for mRNA isoform function prediction by Gaurav Kandoi A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Bioinformatics and Computational Biology Program of Study Committee: Julie A. Dickerson, Co-major Professor Carolyn Lawrence-Dill, Co-major Professor Iddo Friedberg Justin Walley Kris De Brabanter The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2019 Copyright © Gaurav Kandoi, 2019. All rights reserved. ii DEDICATION To my parents, my younger sister and brother for their unconditional support, commitment and encouragement throughout my life. iii TABLE OF CONTENTS Page LIST OF FIGURES ............................................................................................................ v LIST OF TABLES ............................................................................................................. xi ACKNOWLEDGMENTS ................................................................................................ xii ABSTRACT ..................................................................................................................... xiv CHAPTER 1. GENERAL INTRODUCTION ................................................................... 1 1.1. Alternative Splicing .......................................................................................... 1 1.2. Gene Ontology .................................................................................................. 2 1.3. Machine Learning ............................................................................................. 4 1.4. Recommendation Systems ................................................................................ 6 1.5. Problem Formulations ...................................................................................... 7 1.6. Dissertation Organization ................................................................................. 9 References ................................................................................................................... 12 CHAPTER 2. DIFFERENTIAL ALTERNATIVE SPLICING PATTERNS WITH DIFFERENTIAL EXPRESSION TO COMPUTATIONALLY EXTRACT PLANT MOLECULAR PATHWAYS .......................................................................................... 15 Abstract ........................................................................................................................ 15 Introduction ................................................................................................................. 16 Methods ....................................................................................................................... 18 Results ......................................................................................................................... 20 Discussion .................................................................................................................... 25 Conclusions ................................................................................................................. 26 References ................................................................................................................... 38 CHAPTER 3. TISSUE-SPECIFIC MOUSE MRNA ISOFORM NETWORKS ............. 40 Abstract ........................................................................................................................ 40 Introduction ................................................................................................................. 41 Methods ....................................................................................................................... 44 Results ......................................................................................................................... 59 Discussions .................................................................................................................. 72 Data availability ........................................................................................................... 75 Acknowledgement ....................................................................................................... 75 Competing interests ..................................................................................................... 76 References ................................................................................................................... 96 Appendix. Supplementary material for Chapter 3 ..................................................... 102 iv CHAPTER 4. MFRECSYS: MRNA FUNCTION RECOMMENDATION SYSTEM ......................................................................................................................... 113 Introduction ............................................................................................................... 113 Methods ..................................................................................................................... 117 Results ....................................................................................................................... 125 Discussions ................................................................................................................ 128 References ................................................................................................................. 136 CHAPTER 5. GENERAL CONCLUSIONS .................................................................. 140 5.1. General Discussions ..................................................................................... 140 5.2. Future Works ................................................................................................ 144 v LIST OF FIGURES Page Figure 1.1 An overview of this dissertation. Both problems being addressed in this dissertation, 1) developing tissue-specific mRNA isoform level functional networks, and 2) developing tissue-specific mRNA isoform function recommendation systems lead to the characterization of mRNA isoforms of the same gene. ........................................................................... 11 Figure 2.1 Summary of differentially expressed genes: Number of common differentially expressed genes (DEGs) from Araport11 and AtRTD2. The diagonal represents total genes predicted for the comparison and the color is based on the natural log of the common genes (intersection). ........ 27 Figure 2.2 Summary of differentially alternatively spliced genes: Number of common differentially alternatively spliced genes (DASGs) from Araport11 and AtRTD2. The diagonal represents total genes predicted for the comparison and the color is based on the natural log of the common genes (intersection). ....................................................................... 28 Figure 2.3 Summary of differential genes: Number of common differentially expressed and differentially alternatively spliced genes from AtRTD2. The diagonal represents total genes predicted for the comparison and the color is based on the natural log of the common genes (intersection). ........ 29 Figure 2.4 Spliceosome pathway: Differentially alternatively spliced genes (blue) and differentially expressed genes mapped to the spliceosome pathway from the 16C vs 25.1C case. Genes in yellow are both differentially expressed and differentially alternatively spliced. ....................................... 30 Figure 2.5 Peroxisome pathway: Differentially alternatively spliced genes (blue) and differentially expressed genes (red) mapped to the peroxisome pathway from the 16C vs 25.1C case. Genes in yellow are both differentially expressed and differentially alternatively spliced. ....................................... 31 Figure 2.6 Purine metabolism pathway: Differentially alternatively spliced genes (blue) and differentially expressed genes (red) mapped to the purine metabolism pathway from the 25.1C vs 25.5C case. Enzymes in yellow are encoded by genes found to be both differentially expressed and differentially alternatively spliced. ............................................................... 32 Figure 3.1 Overview of our workflow. A brief overview of TENSION is provided. We also illustrate the process of generating the mRNA isoform level vi labels using two dummy gene ontology biological process terms, T1
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