Rabbit Anti-TAF9 Antibody-SL12968R
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Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Advancing a Clinically Relevant Perspective of the Clonal Nature of Cancer
Advancing a clinically relevant perspective of the clonal nature of cancer Christian Ruiza,b, Elizabeth Lenkiewicza, Lisa Eversa, Tara Holleya, Alex Robesona, Jeffrey Kieferc, Michael J. Demeurea,d, Michael A. Hollingsworthe, Michael Shenf, Donna Prunkardf, Peter S. Rabinovitchf, Tobias Zellwegerg, Spyro Moussesc, Jeffrey M. Trenta,h, John D. Carpteni, Lukas Bubendorfb, Daniel Von Hoffa,d, and Michael T. Barretta,1 aClinical Translational Research Division, Translational Genomics Research Institute, Scottsdale, AZ 85259; bInstitute for Pathology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland; cGenetic Basis of Human Disease, Translational Genomics Research Institute, Phoenix, AZ 85004; dVirginia G. Piper Cancer Center, Scottsdale Healthcare, Scottsdale, AZ 85258; eEppley Institute for Research in Cancer and Allied Diseases, Nebraska Medical Center, Omaha, NE 68198; fDepartment of Pathology, University of Washington, Seattle, WA 98105; gDivision of Urology, St. Claraspital and University of Basel, 4058 Basel, Switzerland; hVan Andel Research Institute, Grand Rapids, MI 49503; and iIntegrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, AZ 85004 Edited* by George F. Vande Woude, Van Andel Research Institute, Grand Rapids, MI, and approved June 10, 2011 (received for review March 11, 2011) Cancers frequently arise as a result of an acquired genomic insta- on the basis of morphology alone (8). Thus, the application of bility and the subsequent clonal evolution of neoplastic cells with purification methods such as laser capture microdissection does variable patterns of genetic aberrations. Thus, the presence and not resolve the complexities of many samples. A second approach behaviors of distinct clonal populations in each patient’s tumor is to passage tumor biopsies in tissue culture or in xenografts (4, 9– may underlie multiple clinical phenotypes in cancers. -
Molecular Mechanisms of Ribosomal Protein Gene Coregulation
Downloaded from genesdev.cshlp.org on October 3, 2021 - Published by Cold Spring Harbor Laboratory Press Molecular mechanisms of ribosomal protein gene coregulation Rohit Reja, Vinesh Vinayachandran, Sujana Ghosh, and B. Franklin Pugh Center for Eukaryotic Gene Regulation, Pennsylvania State University, University Park, Pennsylvania 16802, USA The 137 ribosomal protein genes (RPGs) of Saccharomyces provide a model for gene coregulation. We examined the positional and functional organization of their regulators (Rap1 [repressor activator protein 1], Fhl1, Ifh1, Sfp1, and Hmo1), the transcription machinery (TFIIB, TFIID, and RNA polymerase II), and chromatin at near-base-pair res- olution using ChIP-exo, as RPGs are coordinately reprogrammed. Where Hmo1 is enriched, Fhl1, Ifh1, Sfp1, and Hmo1 cross-linked broadly to promoter DNA in an RPG-specific manner and demarcated by general minor groove widening. Importantly, Hmo1 extended 20–50 base pairs (bp) downstream from Fhl1. Upon RPG repression, Fhl1 remained in place. Hmo1 dissociated, which was coupled to an upstream shift of the +1 nucleosome, as reflected by the Hmo1 extension and core promoter region. Fhl1 and Hmo1 may create two regulatable and positionally distinct barriers, against which chromatin remodelers position the +1 nucleosome into either an activating or a repressive state. Consistent with in vitro studies, we found that specific TFIID subunits, in addition to cross-linking at the core promoter, made precise cross-links at Rap1 sites, which we interpret to reflect native Rap1–TFIID interactions. Our findings suggest how sequence-specific DNA binding regulates nucleosome positioning and transcription complex assembly >300 bp away and how coregulation coevolved with coding sequences. -
Novel Candidate Genes of Thyroid Tumourigenesis Identified in Trk-T1 Transgenic Mice
Endocrine-Related Cancer (2012) 19 409–421 Novel candidate genes of thyroid tumourigenesis identified in Trk-T1 transgenic mice Katrin-Janine Heiliger*, Julia Hess*, Donata Vitagliano1, Paolo Salerno1, Herbert Braselmann, Giuliana Salvatore 2, Clara Ugolini 3, Isolde Summerer 4, Tatjana Bogdanova5, Kristian Unger 6, Gerry Thomas6, Massimo Santoro1 and Horst Zitzelsberger Research Unit of Radiation Cytogenetics, Helmholtz Zentrum Mu¨nchen, Ingolsta¨dter Landstr. 1, 85764 Neuherberg, Germany 1Istituto di Endocrinologia ed Oncologia Sperimentale del CNR, c/o Dipartimento di Biologia e Patologia Cellulare e Molecolare, Universita` Federico II, Naples 80131, Italy 2Dipartimento di Studi delle Istituzioni e dei Sistemi Territoriali, Universita` ‘Parthenope’, Naples 80133, Italy 3Division of Pathology, Department of Surgery, University of Pisa, 56100 Pisa, Italy 4Institute of Radiation Biology, Helmholtz Zentrum Mu¨nchen, 85764 Neuherberg, Germany 5Institute of Endocrinology and Metabolism, Academy of Medical Sciences of the Ukraine, 254114 Kiev, Ukraine 6Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London W12 0HS, UK (Correspondence should be addressed to H Zitzelsberger; Email: [email protected]) *(K-J Heiliger and J Hess contributed equally to this work) Abstract For an identification of novel candidate genes in thyroid tumourigenesis, we have investigated gene copy number changes in a Trk-T1 transgenic mouse model of thyroid neoplasia. For this aim, 30 thyroid tumours from Trk-T1 transgenics were investigated by comparative genomic hybridisation. Recurrent gene copy number alterations were identified and genes located in the altered chromosomal regions were analysed by Gene Ontology term enrichment analysis in order to reveal gene functions potentially associated with thyroid tumourigenesis. In thyroid neoplasms from Trk-T1 mice, a recurrent gain on chromosomal bands 1C4–E2.3 (10.0% of cases), and losses on 3H1–H3 (13.3%), 4D2.3–E2 (43.3%) and 14E4–E5 (6.7%) were identified. -
Comprehensive Analyses of DNA Repair Pathways, Smoking and Bladder Cancer Risk in Los Angeles and Shanghai
IJC International Journal of Cancer Comprehensive analyses of DNA repair pathways, smoking and bladder cancer risk in Los Angeles and Shanghai Roman Corral1,2, Juan Pablo Lewinger1,2, David Van Den Berg1,2, Amit D. Joshi3, Jian-Min Yuan4, Manuela Gago-Dominguez1,2,5, Victoria K. Cortessis1,2,6, Malcolm C. Pike1,2,7, David V. Conti1,2, Duncan C. Thomas1,2, Christopher K. Edlund2, Yu-Tang Gao8, Yong-Bing Xiang8, Wei Zhang8, Yu-Chen Su1,2 and Mariana C. Stern1,2 1 Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 2 Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 3 Department of Epidemiology, Harvard School of Public Health, Boston, MA 4 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh Cancer Institute, University of Pittsbugh, Pittsburgh, PA 5 Galician Foundation of Genomic Medicine, Complexo Hospitalario Universitario Santiago, Servicio Galego de Saude, IDIS, Santiago de Compostela, Spain 6 Department of Obstetrics and Gynecology, Keck School of Medicine of University of Southern California, Los Angeles, CA 7 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 8 Department of Epidemiology, Shanghai Cancer Institute and Cancer Institute of Shanghai Jiaotong University, Shanghai, China Tobacco smoking is a bladder cancer risk factor and a source of carcinogens that induce DNA damage to urothelial cells. Using data and samples from 988 cases and 1,004 controls enrolled in the Los Angeles County Bladder Cancer Study and the Shanghai Bladder Cancer Study, we investigated associations between bladder cancer risk and 632 tagSNPs that comprehensively capture genetic variation in 28 DNA repair genes from four DNA repair pathways: base excision repair (BER), nucleotide excision repair (NER), non-homologous end-joining (NHEJ) and homologous recombination repair (HHR). -
Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways
biomolecules Article Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways Aayushi Srivastava 1,2,3,4 , Abhishek Kumar 1,5,6 , Sara Giangiobbe 1, Elena Bonora 7, Kari Hemminki 1, Asta Försti 1,2,3 and Obul Reddy Bandapalli 1,2,3,* 1 Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany; [email protected] (A.S.); [email protected] (A.K.); [email protected] (S.G.); [email protected] (K.H.); [email protected] (A.F.) 2 Hopp Children’s Cancer Center (KiTZ), D-69120 Heidelberg, Germany 3 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), D-69120 Heidelberg, Germany 4 Medical Faculty, Heidelberg University, D-69120 Heidelberg, Germany 5 Institute of Bioinformatics, International Technology Park, Bangalore 560066, India 6 Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India 7 S.Orsola-Malphigi Hospital, Unit of Medical Genetics, 40138 Bologna, Italy; [email protected] * Correspondence: [email protected]; Tel.: +49-6221-42-1709 Received: 29 August 2019; Accepted: 10 October 2019; Published: 13 October 2019 Abstract: Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC families to better understand its pathogenesis with the final aim of identifying one novel high/moderate-penetrance germline predisposition variant segregating with the disease in each studied family. -
TAF10 Complex Provides Evidence for Nuclear Holo&Ndash;TFIID Assembly from Preform
ARTICLE Received 13 Aug 2014 | Accepted 2 Dec 2014 | Published 14 Jan 2015 DOI: 10.1038/ncomms7011 OPEN Cytoplasmic TAF2–TAF8–TAF10 complex provides evidence for nuclear holo–TFIID assembly from preformed submodules Simon Trowitzsch1,2, Cristina Viola1,2, Elisabeth Scheer3, Sascha Conic3, Virginie Chavant4, Marjorie Fournier3, Gabor Papai5, Ima-Obong Ebong6, Christiane Schaffitzel1,2, Juan Zou7, Matthias Haffke1,2, Juri Rappsilber7,8, Carol V. Robinson6, Patrick Schultz5, Laszlo Tora3 & Imre Berger1,2,9 General transcription factor TFIID is a cornerstone of RNA polymerase II transcription initiation in eukaryotic cells. How human TFIID—a megadalton-sized multiprotein complex composed of the TATA-binding protein (TBP) and 13 TBP-associated factors (TAFs)— assembles into a functional transcription factor is poorly understood. Here we describe a heterotrimeric TFIID subcomplex consisting of the TAF2, TAF8 and TAF10 proteins, which assembles in the cytoplasm. Using native mass spectrometry, we define the interactions between the TAFs and uncover a central role for TAF8 in nucleating the complex. X-ray crystallography reveals a non-canonical arrangement of the TAF8–TAF10 histone fold domains. TAF2 binds to multiple motifs within the TAF8 C-terminal region, and these interactions dictate TAF2 incorporation into a core–TFIID complex that exists in the nucleus. Our results provide evidence for a stepwise assembly pathway of nuclear holo–TFIID, regulated by nuclear import of preformed cytoplasmic submodules. 1 European Molecular Biology Laboratory, Grenoble Outstation, 6 rue Jules Horowitz, 38042 Grenoble, France. 2 Unit for Virus Host-Cell Interactions, University Grenoble Alpes-EMBL-CNRS, 6 rue Jules Horowitz, 38042 Grenoble, France. 3 Cellular Signaling and Nuclear Dynamics Program, Institut de Ge´ne´tique et de Biologie Mole´culaire et Cellulaire, UMR 7104, INSERM U964, 1 rue Laurent Fries, 67404 Illkirch, France. -
Structure and Mechanism of the RNA Polymerase II Transcription Machinery
Downloaded from genesdev.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Structure and mechanism of the RNA polymerase II transcription machinery Allison C. Schier and Dylan J. Taatjes Department of Biochemistry, University of Colorado, Boulder, Colorado 80303, USA RNA polymerase II (Pol II) transcribes all protein-coding ingly high resolution, which has rapidly advanced under- genes and many noncoding RNAs in eukaryotic genomes. standing of the molecular basis of Pol II transcription. Although Pol II is a complex, 12-subunit enzyme, it lacks Structural biology continues to transform our under- the ability to initiate transcription and cannot consistent- standing of complex biological processes because it allows ly transcribe through long DNA sequences. To execute visualization of proteins and protein complexes at or near these essential functions, an array of proteins and protein atomic-level resolution. Combined with mutagenesis and complexes interact with Pol II to regulate its activity. In functional assays, structural data can at once establish this review, we detail the structure and mechanism of how enzymes function, justify genetic links to human dis- over a dozen factors that govern Pol II initiation (e.g., ease, and drive drug discovery. In the past few decades, TFIID, TFIIH, and Mediator), pausing, and elongation workhorse techniques such as NMR and X-ray crystallog- (e.g., DSIF, NELF, PAF, and P-TEFb). The structural basis raphy have been complemented by cryoEM, cross-linking for Pol II transcription regulation has advanced rapidly mass spectrometry (CXMS), and other methods. Recent in the past decade, largely due to technological innova- improvements in data collection and imaging technolo- tions in cryoelectron microscopy. -
Supplementary Materials and Tables a and B
SUPPLEMENTARY MATERIAL 1 Table A. Main characteristics of the subset of 23 AML patients studied by high-density arrays (subset A) WBC BM blasts MYST3- MLL Age/Gender WHO / FAB subtype Karyotype FLT3-ITD NPM status (x109/L) (%) CREBBP status 1 51 / F M4 NA 21 78 + - G A 2 28 / M M4 t(8;16)(p11;p13) 8 92 + - G G 3 53 / F M4 t(8;16)(p11;p13) 27 96 + NA G NA 4 24 / M PML-RARα / M3 t(15;17) 5 90 - - G G 5 52 / M PML-RARα / M3 t(15;17) 1.5 75 - - G G 6 31 / F PML-RARα / M3 t(15;17) 3.2 89 - - G G 7 23 / M RUNX1-RUNX1T1 / M2 t(8;21) 38 34 - + ND G 8 52 / M RUNX1-RUNX1T1 / M2 t(8;21) 8 68 - - ND G 9 40 / M RUNX1-RUNX1T1 / M2 t(8;21) 5.1 54 - - ND G 10 63 / M CBFβ-MYH11 / M4 inv(16) 297 80 - - ND G 11 63 / M CBFβ-MYH11 / M4 inv(16) 7 74 - - ND G 12 59 / M CBFβ-MYH11 / M0 t(16;16) 108 94 - - ND G 13 41 / F MLLT3-MLL / M5 t(9;11) 51 90 - + G R 14 38 / F M5 46, XX 36 79 - + G G 15 76 / M M4 46 XY, der(10) 21 90 - - G NA 16 59 / M M4 NA 29 59 - - M G 17 26 / M M5 46, XY 295 92 - + G G 18 62 / F M5 NA 67 88 - + M A 19 47 / F M5 del(11q23) 17 78 - + M G 20 50 / F M5 46, XX 61 59 - + M G 21 28 / F M5 46, XX 132 90 - + G G 22 30 / F AML-MD / M5 46, XX 6 79 - + M G 23 64 / M AML-MD / M1 46, XY 17 83 - + M G WBC: white blood cell. -
Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma
Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen Submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in the Graduate School of Arts and Sciences Columbia University 2013 ! 2013 James Chen All rights reserved ABSTRACT Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma. TABLE OF CONTENTS CHAPTER 1… Introduction and Background 1 Glioblastoma and the Mesenchymal Subtype 3 Systems Biology and Master Regulators 9 Thesis Project: Genetics and Genomics 20 CHAPTER 2… TCGA Data Processing 23 CHAPTER 3… DIGGIn Part 1 – Selecting f-CNVs 33 Mutual Information 40 Application and Analysis 45 CHAPTER 4… DIGGIn Part 2 – Selecting drivers 52 CHAPTER 5… KLHL9 Manuscript 63 Methods 90 CHAPTER 5a… Revisions work-in-progress 105 CHAPTER 6… Discussion 109 APPENDICES… 132 APPEND01 – TCGA classifications 133 APPEND02 – GBM f-CNV list 136 APPEND03 – MES f-CNV candidate drivers 152 APPEND04 – Scripts 149 APPEND05 – Manuscript Figures and Legends 175 APPEND06 – Manuscript Supplemental Materials 185 i ACKNOWLEDGEMENTS I would like to thank the Califano Lab and my mentor, Andrea Califano, for their intellectual and motivational support during my stay in their lab. -
Developing a Neural Implant to Enable Controlled Alterations to Brain Architecture Nicholas J. Weir (N0389094) a Thesis Submitte
Developing a Neural Implant to Enable Controlled Alterations to Brain Architecture Nicholas J. Weir (N0389094) A thesis submitted in partial fulfilment of the requirements of Nottingham Trent University for the degree of Doctor of Philosophy December 2019 1 Copyright Statement This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner(s) of the Intellectual Property Rights. 2 Acknowledgements First, I would like to thank my Director of Studies, Chris Tinsley, for his support and encouragement throughout the course of the PhD. His kind words and ability to find the silver lining in any situation made the past few years that much easier. Thanks also to my supervisory team, Alan Hargreaves, Bob Stevens and Martin McGinnity for their advice and thoughts on experiments and data that helped shape the PhD. Whilst not on my supervisory team, I’m also grateful to Amanda Miles for her help with mass spectrometry and endless technical knowledge and to Rich Hulse for his advice and support throughout. Special thanks go to my colleagues. Notably, to Awais and Jordan for their ability to distract and take my mind out of the lab during stressful times and to Charlotte for being a role model of scientific rigour and discipline. -
A High-Resolution Comparative Map of Porcine Chromosome 4 (SSC4).Pdf
SHORT COMMUNICATION doi:10.1111/j.1365-2052.2010.02140.x A high-resolution comparative map of porcine chromosome 4 (SSC4) J.-G. Ma*†, T.-C. Chang†, H. Yasue‡, A. D. Farmer§, J. A. Crow§, K. Eyer¶, H. Hiraiwa‡, T. Shimogiri**, S. N. Meyers††, J. E. Beever††, L. B. Schook††, E. F. Retzel§, C. W. Beattie‡‡ and W.-S. Liu† *Department of Biological Science and Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, XiÕan Jiaotong University, XiÕan 710049, China. †Department of Dairy and Animal Science, College of Agricultural Sciences, Pennsylvania State University, 305 Henning Building, University Park, PA 16802, USA. ‡Genome Research Department, National Institute of Agrobiological Sciences, Ikenodai, Tsukuba, Ibaraki 305-0901, Japan. §National Center for Genome Resources, Santa Fe, NM 87505 USA. ¶Department of Biology, College of Sciences, University of Nevada, Reno, NV 89557, USA. **Faculty of Agriculture, Kagoshima University, Korimoto, Kagoshima 890-0065, Japan. ††Department of Animal Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA. ‡‡Department of Surgical Oncology, Room 618 820 CSB, University of Illinois COM, 840 South Wood St., Chicago, IL, USA Summary We used the IMNpRH212 000-rad RH and IMpRH7 000-rad panels to integrate 2019 tran- scriptome (RNA-seq)-generated contigs with markers from the porcine genetic and radia- tion hybrid (RH) maps and bacterial artificial chromosome finger-printed contigs, into 1) parallel framework maps (LOD ‡10) on both panels for swine chromosome (SSC) 4, and 2) a high-resolution comparative map of SSC4, thus and human chromosomes (HSA) 1 and 8.