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Analysis of Gene Co-Expression Networks of Two Coccolithophore Species

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Analysis of Gene Co-expression Networks of Two Coccolithophore Species In affiliation, with California State University, San Marcos In partial fulfillment of the Requirements for the Degree of Master of Computer Science By Nitesh Balasaheb Sabankar Summer 2018 1 ABSTRACT Emiliania Huxleyi (E. Huxleyi) and Gephyrocapsa oceanica (G. Oceanica) are some of the most abundant species of Coccolithophores in the ocean. G. Oceanica and E. Huxleyi produce coccoliths, making it feasible to use comparative genomics [1, 2]. Coccolithophores play an important role in the oceanic carbon cycle through calcification of coccoliths and photosynthesis. The main objective of the project was to study and compare two sister coccolithophores E. Huxleyi and G. Oceanica, using differential gene co-expression network analysis. Gene Co-Expression network involves nodes which corresponds to genes and edges corresponding to co-expression relationship between genes. The direction and type of co-expression relationships are not determined in gene co-expression networks. Gene Co-expression networks allows identification of different candidate biomarkers and curative targets. Such networks enable the inference of diseases and system-level functionality of genes. These inferences are helpful in identifying genes characteristics [4, 5]. In this project, E. Huxleyi and G. Oceanica RNA-Seq data was compared with each other and divided into different co-expressed group of genes between two species which are also called as ‘modules’. These modules are then compared with external traits to find out how the modules and traits are related. Functional enrichment analysis was also performed on these modules to identify significantly enriched genes in the interesting modules. After the analysis, by using 12 modules which are highly preserved in both the data sets, lipid metabolism genes and biomineralization genes to these modules are related and biological functions for these genes are obtained. Similarly, lists of genes which can be related to set of biomineralization genes was obtained which can vastly help us study biomineralization process in detail in the two species. 2 ACKNOWLEDGEMENT I would first like to express my sincere gratitude and thanks to Dr. Xiaoyu Zhang, without whose constant support and advice this project would not be possible. I would also like to thank Dr. Ahmad Hadaegh and Dr. Betsy Read for taking time off their busy schedule to guide me through the entire process. I am deeply indebted to all of them. My sincere appreciation also goes out to other faculty members and staff of the Computer Science department, my fellow students and my family members, without whose support and motivation I would not have been able to complete this project. 3 TABLE OF CONTENTS ABSTRACT .................................................................................................................................... 2 ACKNOWLEDGEMENT .............................................................................................................. 3 1 INTRODUCTION ................................................................................................................... 6 2 BACKGROUND ..................................................................................................................... 8 Gene co-expression networks........................................................................................... 8 Software for co-expression network analysis................................................................... 8 Weighted gene co-expression network analysis (WGCNA) ............................................ 9 3 ARCHITECTURE ................................................................................................................. 10 Input Data ....................................................................................................................... 11 Data pre-processing ........................................................................................................ 11 Low-count filtering ................................................................................................. 12 Log-transforming data ............................................................................................ 12 Normalization ......................................................................................................... 12 Co-expression network construction .............................................................................. 13 Soft-thresholding power selection .......................................................................... 13 Adjacency matrix construction ............................................................................... 14 Topological Overlap Matrix based network construction ...................................... 14 Hierarchical Clustering and module identification ........................................................ 15 Assessing module preservation ...................................................................................... 18 Module-trait relationship ................................................................................................ 19 Functional enrichment analysis ...................................................................................... 19 4 IMPLEMENTATION AND RESULTS ............................................................................... 20 Input Data sets ................................................................................................................ 20 Data pre-processing ........................................................................................................ 21 4 Evaluating correlation between the datasets .................................................................. 23 Co-expression network construction .............................................................................. 25 Adjacency matrix construction ............................................................................... 25 Topological Overlap Matrix based network construction .............................................. 26 Scaling of Topological Overlap Matrices ............................................................... 27 Hierarchical clustering and module assignment............................................................. 30 Imposing unmerged modules .................................................................................. 31 Imposing merged modules ...................................................................................... 31 Assessing module preservation ...................................................................................... 32 Relating modules to external information ...................................................................... 34 Module-trait relationship ........................................................................................ 34 Functional Enrichment Analysis ............................................................................. 38 Relating modules to biomineralization genes. ........................................................ 45 Relating modules to lipid metabolism genes. ......................................................... 52 5 CONCLUSION AND FUTURE WORK .............................................................................. 54 6 REFERENCES ...................................................................................................................... 55 7 APPENDICES ....................................................................................................................... 57 APPENDIX 1 – GO analysis table for unmerged E. Huxleyi data modules ................. 57 APPENDIX 2 – Table relating unmerged modules to biomineralization genes ............ 65 APPENDIX 3 – Table relating unmerged modules to lipid metabolism genes ............. 83 APPENDIX 4 – GO analysis table for merged E. Huxleyi data modules ..................... 86 APPENDIX 5 – Table relating merged modules to biomineralization genes ................ 94 APPENDIX 6 – Table relating merged modules to lipid metabolism genes ............... 106 5 1 INTRODUCTION G. Oceanica and E. Huxleyi are type of phytoplankton microscopic organisms. These microscopic organisms are found mostly in the upper layers of oceans where there is sufficient sunlight. Similar to plants, phytoplanktons derive energy through the process of photosynthesis [6]. Spherical cells about 5-100 micrometers across, enclosed by calcareous (coccoliths) plates are called 'Coccolithophores'. They are one of the most important micro-algae, and the third-most prominent group of phytoplanktons [7]. Coccolithophores are exclusively marine organisms, and, like other phytoplanktons, are found in abundance in those parts of the ocean that receive sufficient sunlight [7]. Some coccolithophores differ from other oceanic phytoplanktons in that they have an exclusive outer sphere of calcite plates known as coccoliths. Because of their unique properties, research on coccolithophores has emerged as a major area of interest among scientists who study global climate change. E. Huxleyi is one of the most abundant species of coccolithophores. This species received its name from Cesare Emiliani and Thomas Huxley, two scientists who discovered coccoliths that were embedded in the sediments at the bottom of the ocean [8]. The coccoliths of E. Huxleyi are generally transparent and colorless, and are made up of calcites that can refract light very efficiently in water. In this study the focus
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  • UTP-Bound and Apo Structures of a Minimal RNA Uridylyltransferase

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    ÔØ ÅÒÙ×Ö ÔØ UTP-Bound and Apo Structures of a Minimal RNA Uridylyltransferase Jason Stagno, Inna Aphasizheva, Anja Rosengarth, Hartmut Luecke, Ruslan Aphasizhev PII: S0022-2836(06)01624-X DOI: doi: 10.1016/j.jmb.2006.11.065 Reference: YJMBI 58909 To appear in: Journal of Molecular Biology Received date: 6 September 2006 Revised date: 9 November 2006 Accepted date: 20 November 2006 Please cite this article as: Stagno, J., Aphasizheva, I., Rosengarth, A., Luecke, H. & Aphasizhev, R., UTP-Bound and Apo Structures of a Minimal RNA Uridylyltransferase, Journal of Molecular Biology (2006), doi: 10.1016/j.jmb.2006.11.065 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT UTP-Bound and Apo Structures of a Minimal RNA Uridylyltransferase Jason Stagno1,*, Inna Aphasizheva2,*, Anja Rosengarth1, Hartmut Luecke1,3,4,& and Ruslan Aphasizhev2,& 1Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA 2Department of Microbiology and Molecular Genetics, University of California, Irvine, CA 92697, USA 3Department of Physiology & Biophysics, University of California, Irvine, CA 92697, USA
  • All Enzymes in BRENDA™ the Comprehensive Enzyme Information System

    All Enzymes in BRENDA™ the Comprehensive Enzyme Information System

    All enzymes in BRENDA™ The Comprehensive Enzyme Information System http://www.brenda-enzymes.org/index.php4?page=information/all_enzymes.php4 1.1.1.1 alcohol dehydrogenase 1.1.1.B1 D-arabitol-phosphate dehydrogenase 1.1.1.2 alcohol dehydrogenase (NADP+) 1.1.1.B3 (S)-specific secondary alcohol dehydrogenase 1.1.1.3 homoserine dehydrogenase 1.1.1.B4 (R)-specific secondary alcohol dehydrogenase 1.1.1.4 (R,R)-butanediol dehydrogenase 1.1.1.5 acetoin dehydrogenase 1.1.1.B5 NADP-retinol dehydrogenase 1.1.1.6 glycerol dehydrogenase 1.1.1.7 propanediol-phosphate dehydrogenase 1.1.1.8 glycerol-3-phosphate dehydrogenase (NAD+) 1.1.1.9 D-xylulose reductase 1.1.1.10 L-xylulose reductase 1.1.1.11 D-arabinitol 4-dehydrogenase 1.1.1.12 L-arabinitol 4-dehydrogenase 1.1.1.13 L-arabinitol 2-dehydrogenase 1.1.1.14 L-iditol 2-dehydrogenase 1.1.1.15 D-iditol 2-dehydrogenase 1.1.1.16 galactitol 2-dehydrogenase 1.1.1.17 mannitol-1-phosphate 5-dehydrogenase 1.1.1.18 inositol 2-dehydrogenase 1.1.1.19 glucuronate reductase 1.1.1.20 glucuronolactone reductase 1.1.1.21 aldehyde reductase 1.1.1.22 UDP-glucose 6-dehydrogenase 1.1.1.23 histidinol dehydrogenase 1.1.1.24 quinate dehydrogenase 1.1.1.25 shikimate dehydrogenase 1.1.1.26 glyoxylate reductase 1.1.1.27 L-lactate dehydrogenase 1.1.1.28 D-lactate dehydrogenase 1.1.1.29 glycerate dehydrogenase 1.1.1.30 3-hydroxybutyrate dehydrogenase 1.1.1.31 3-hydroxyisobutyrate dehydrogenase 1.1.1.32 mevaldate reductase 1.1.1.33 mevaldate reductase (NADPH) 1.1.1.34 hydroxymethylglutaryl-CoA reductase (NADPH) 1.1.1.35 3-hydroxyacyl-CoA
  • Supporting Information Musashi-1: an Example of How Polyalanine Tracts

    Supporting Information Musashi-1: an Example of How Polyalanine Tracts

    Supporting Information Musashi-1: an example of how polyalanine tracts contribute to self-association in the intrinsically disordered regions of RNA-binding proteins Tsai-Chen Chen and Jie-rong Huang Institute of Biochemistry and Molecular Biology and Institute of Biomedical Informatics, National Yang-Ming University, No. 155 Section 2 Li-nong Street, Taipei, Taiwan Assignment strategy We followed a denaturation-then-titration strategy – assigning the protein under harsh conditions and then titrating back to physiological conditions – because Musashi-1’s IDR has a strong tendency to aggregate. We also used (H)N(COCO)NH and (HN)CO(CO)NH pulse sequences to help complete the sequential assignment, using long-range (i, i+2) connections between backbone nitrogen and carbonyl-carbon atoms to overcome disruptions due to the 20 prolines (which make up about 12 % in the primary sequence). We first prepared the 0.7 mM 15N/13C-labeled sample in 10 mM glycine buffer with 8 M urea at pH 2.5, conditions under which the NMR peaks are well-dispersed (Figure A1). The assignment was facilitated by (H)N(COCO)NH and (HN)CO(CO)NH data. 15N-labeled samples of four different constructs were used to distinguish assignments that remained ambiguous (Figure A2) under the same buffer condition. 15N-labeled sample (~70 µM) in 10 mM phosphate buffer at pH 5.5 was titrated to different pHs till pH=2.5 using phosphoric acid (Figure A3). 15N-labeled samples (~70 µM) in 20 mM MES buffer at pH 5.5 with urea concentrations ranging from 0 to 8 M are shown in Figure A4.
  • (12) Patent Application Publication (10) Pub. No.: US 2015/0240226A1 Mathur Et Al

    (12) Patent Application Publication (10) Pub. No.: US 2015/0240226A1 Mathur Et Al

    US 20150240226A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2015/0240226A1 Mathur et al. (43) Pub. Date: Aug. 27, 2015 (54) NUCLEICACIDS AND PROTEINS AND CI2N 9/16 (2006.01) METHODS FOR MAKING AND USING THEMI CI2N 9/02 (2006.01) CI2N 9/78 (2006.01) (71) Applicant: BP Corporation North America Inc., CI2N 9/12 (2006.01) Naperville, IL (US) CI2N 9/24 (2006.01) CI2O 1/02 (2006.01) (72) Inventors: Eric J. Mathur, San Diego, CA (US); CI2N 9/42 (2006.01) Cathy Chang, San Marcos, CA (US) (52) U.S. Cl. CPC. CI2N 9/88 (2013.01); C12O 1/02 (2013.01); (21) Appl. No.: 14/630,006 CI2O I/04 (2013.01): CI2N 9/80 (2013.01); CI2N 9/241.1 (2013.01); C12N 9/0065 (22) Filed: Feb. 24, 2015 (2013.01); C12N 9/2437 (2013.01); C12N 9/14 Related U.S. Application Data (2013.01); C12N 9/16 (2013.01); C12N 9/0061 (2013.01); C12N 9/78 (2013.01); C12N 9/0071 (62) Division of application No. 13/400,365, filed on Feb. (2013.01); C12N 9/1241 (2013.01): CI2N 20, 2012, now Pat. No. 8,962,800, which is a division 9/2482 (2013.01); C07K 2/00 (2013.01); C12Y of application No. 1 1/817,403, filed on May 7, 2008, 305/01004 (2013.01); C12Y 1 1 1/01016 now Pat. No. 8,119,385, filed as application No. PCT/ (2013.01); C12Y302/01004 (2013.01); C12Y US2006/007642 on Mar. 3, 2006.