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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. -
Signaling Pathway Activities Improve Prognosis for Breast Cancer Yunlong Jiao1,2,3,4, Marta R
bioRxiv preprint doi: https://doi.org/10.1101/132357; this version posted April 29, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Signaling Pathway Activities Improve Prognosis for Breast Cancer Yunlong Jiao1,2,3,4, Marta R. Hidalgo5, Cankut Çubuk6, Alicia Amadoz5, José Carbonell- Caballero5, Jean-Philippe Vert1,2,3,4, and Joaquín Dopazo6,7,8,* 1MINES ParisTech, PSL Research University, Centre for Computational Biology, 77300 Fontainebleau, France; 2Institut Curie, 75248 Paris Cedex, Franc; 3INSERM, U900, 75248 Paris Cedex, France; 4Ecole Normale Supérieure, Department of Mathematics and their Applications, 75005 Paris, France; 5 Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), 46012 Valencia, Spain; 6Clinical Bioinformatics Research Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013, Sevilla, Spain; 7Functional Genomics Node (INB), FPS, Hospital Virgen del Rocío, 41013 Sevilla, Spain; 8 Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013, Sevilla, Spain *To whom correspondence should be addressed. Abstract With the advent of high-throughput technologies for genome-wide expression profiling, a large number of methods have been proposed to discover gene-based signatures as biomarkers to guide cancer prognosis. However, it is often difficult to interpret the list of genes in a prognostic signature regarding the underlying biological processes responsible for disease progression or therapeutic response. A particularly interesting alternative to gene-based biomarkers is mechanistic biomarkers, derived from signaling pathway activities, which are known to play a key role in cancer progression and thus provide more informative insights into cellular functions involved in cancer mechanism. -
Transcription Regulation by Histone Methylation: Interplay Between Different Covalent Modifications of the Core Histone Tails
Downloaded from genesdev.cshlp.org on February 2, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Transcription regulation by histone methylation: interplay between different covalent modifications of the core histone tails Yi Zhang1,3 and Danny Reinberg2,4 1Department of Biochemistry and Biophysics, Curriculum in Genetics and Molecular Biology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, North Carolina 27599-7295, USA; 2Howard Hughes Medical Institute, Division of Nucleic Acids Enzymology, Department of Biochemistry, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA In this review, we discuss recent advances made on his- vealed that there are different types of protein complexes tone methylation and its diverse functions in regulating capable of altering the chromatin, and these may act in a gene expression. Methylation of histone polypeptides physiological context to modulate DNA accessibility. might be static and might mark a gene to be or not be One family includes multiprotein complexes that utilize transcribed. However, the decision to methylate or not the energy derived from ATP hydrolysis to mobilize or methylate a specific residue in the histone polypeptides alter the structure of nucleosomes (Kingston and Narl- is an active process that requires coordination among ikar 1999; Vignali et al. 2000). The other family includes different covalent modifications occurring at the amino protein complexes that modify the histone polypeptides termini of the histone polypeptides, the histone tails. covalently, primarily within residues located at the his- Below, we summarize recent advances on histone meth- tone tails (Wu and Grunstein 2000). -
Interactome Analyses Revealed That the U1 Snrnp Machinery Overlaps Extensively with the RNAP II Machinery and Contains Multiple
www.nature.com/scientificreports OPEN Interactome analyses revealed that the U1 snRNP machinery overlaps extensively with the RNAP II Received: 12 April 2018 Accepted: 24 May 2018 machinery and contains multiple Published: xx xx xxxx ALS/SMA-causative proteins Binkai Chi1, Jeremy D. O’Connell1,2, Tomohiro Yamazaki1, Jaya Gangopadhyay1, Steven P. Gygi1 & Robin Reed1 Mutations in multiple RNA/DNA binding proteins cause Amyotrophic Lateral Sclerosis (ALS). Included among these are the three members of the FET family (FUS, EWSR1 and TAF15) and the structurally similar MATR3. Here, we characterized the interactomes of these four proteins, revealing that they largely have unique interactors, but share in common an association with U1 snRNP. The latter observation led us to analyze the interactome of the U1 snRNP machinery. Surprisingly, this analysis revealed the interactome contains ~220 components, and of these, >200 are shared with the RNA polymerase II (RNAP II) machinery. Among the shared components are multiple ALS and Spinal muscular Atrophy (SMA)-causative proteins and numerous discrete complexes, including the SMN complex, transcription factor complexes, and RNA processing complexes. Together, our data indicate that the RNAP II/U1 snRNP machinery functions in a wide variety of molecular pathways, and these pathways are candidates for playing roles in ALS/SMA pathogenesis. Te neurodegenerative disease Amyotrophic Lateral Sclerosis (ALS) has no known treatment, and elucidation of disease mechanisms is urgently needed. Tis problem has been especially daunting, as mutations in greater than 30 genes are ALS-causative, and these genes function in numerous cellular pathways1. Tese include mitophagy, autophagy, cytoskeletal dynamics, vesicle transport, DNA damage repair, RNA dysfunction, apoptosis, and pro- tein aggregation2–6. -
Miz1 Is Required to Maintain Autophagic Flux
ARTICLE Received 3 Apr 2013 | Accepted 3 Sep 2013 | Published 3 Oct 2013 DOI: 10.1038/ncomms3535 Miz1 is required to maintain autophagic flux Elmar Wolf1,*, Anneli Gebhardt1,*, Daisuke Kawauchi2, Susanne Walz1, Bjo¨rn von Eyss1, Nicole Wagner3, Christoph Renninger3, Georg Krohne1, Esther Asan3, Martine F. Roussel2 & Martin Eilers1,4 Miz1 is a zinc finger protein that regulates the expression of cell cycle inhibitors as part of a complex with Myc. Cell cycle-independent functions of Miz1 are poorly understood. Here we use a Nestin-Cre transgene to delete an essential domain of Miz1 in the central nervous system (Miz1DPOZNes). Miz1DPOZNes mice display cerebellar neurodegeneration characterized by the progressive loss of Purkinje cells. Chromatin immunoprecipitation sequencing and biochemical analyses show that Miz1 activates transcription upon binding to a non-palin- dromic sequence present in core promoters. Target genes of Miz1 encode regulators of autophagy and proteins involved in vesicular transport that are required for autophagy. Miz1DPOZ neuronal progenitors and fibroblasts show reduced autophagic flux. Consistently, polyubiquitinated proteins and p62/Sqtm1 accumulate in the cerebella of Miz1DPOZNes mice, characteristic features of defective autophagy. Our data suggest that Miz1 may link cell growth and ribosome biogenesis to the transcriptional regulation of vesicular transport and autophagy. 1 Theodor Boveri Institute, Biocenter, University of Wu¨rzburg, Am Hubland, 97074 Wu¨rzburg, Germany. 2 Department of Tumor Cell Biology, MS#350, Danny Thomas Research Center, 5006C, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA. 3 Institute for Anatomy and Cell Biology, University of Wu¨rzburg, Koellikerstrasse 6, 97070 Wu¨rzburg, Germany. 4 Comprehensive Cancer Center Mainfranken, Josef-Schneider-Strasse 6, 97080 Wu¨rzburg, Germany. -
Association Analyses Based on False Discovery Rate Implicate Many New Loci for Coronary Artery Disease
1 Association analyses based on false discovery rate implicate many new loci 2 for coronary artery disease 3 Christopher P. Nelson1,2,61, Anuj Goel3,4,61, Adam S Butterworth5,6,61, Stavroula Kanoni7,8,61, 4 Tom R. Webb1,2, Eirini Marouli7,8, Lingyao Zeng9, Ioanna Ntalla7,8, Florence Y. Lai1,2, Jemma C. 5 Hopewell10, Olga Giannakopoulou7,8, Tao Jiang5, Stephen E. Hamby1,2, Emanuele Di 6 Angelantonio5,6, Themistocles L. Assimes11, Erwin P. Bottinger12, John C. Chambers13,14,15, 7 Robert Clarke10, Colin NA Palmer16,17, Richard M. Cubbon18, Patrick Ellinor19, Raili Ermel20, 8 Evangelos Evangelou21,22, Paul W. Franks23,24,25, Christopher Grace3,4, Dongfeng Gu26, Aroon 9 D. Hingorani27, Joanna M. M. Howson5, Erik Ingelsson28, Adnan Kastrati9, Thorsten Kessler9, 10 Theodosios Kyriakou3,4, Terho Lehtimäki29, Xiangfeng Lu26, Yingchang Lu12,30, Winfried 11 März31,32,33, Ruth McPherson34, Andres Metspalu35, Mar Pujades-Rodriguez36, Arno 12 Ruusalepp20,37, Eric E. Schadt38, Amand F. Schmidt39, Michael J. Sweeting5, Pierre A. 13 Zalloua40,41, Kamal AIGhalayini42, Bernard D. Keavney43,44, Jaspal S. Kooner14,15,45, Ruth J. F. 14 Loos12,46, Riyaz S. Patel47,48, Martin K. Rutter49,50, Maciej Tomaszewski51,52, Ioanna Tzoulaki21,22, 15 Eleftheria Zeggini53, Jeanette Erdmann54,55,56, George Dedoussis57, Johan L. M. 16 Björkegren37,38,58, EPIC-CVD Consortium, CARDIoGRAMplusC4D, The UK Biobank 17 CardioMetabolic Consortium CHD working group, Heribert Schunkert9,61, Martin Farrall3,4,61, 18 John Danesh5,6,59,61, Nilesh J. Samani1,2,61, Hugh Watkins3,4,61, Panos Deloukas7,8,60,61 19 1. Department of Cardiovascular Sciences, University of Leicester, Leicester LE3 9QP, UK 20 2. -
Atlas Journal
Atlas of Genetics and Cytogenetics in Oncology and Haematology Home Genes Leukemias Solid Tumours Cancer-Prone Deep Insight Portal Teaching X Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 NA Atlas Journal Atlas Journal versus Atlas Database: the accumulation of the issues of the Journal constitutes the body of the Database/Text-Book. TABLE OF CONTENTS Volume 12, Number 6, Nov-Dec 2008 Previous Issue / Next Issue Genes BCL8 (B-cell CLL/lymphoma 8) (15q11). Silvia Rasi, Gianluca Gaidano. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 781-784. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/BCL8ID781ch15q11.html CDC25A (Cell division cycle 25A) (3p21). Dipankar Ray, Hiroaki Kiyokawa. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 785-791. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/CDC25AID40004ch3p21.html CDC73 (cell division cycle 73, Paf1/RNA polymerase II complex component, homolog (S. cerevisiae)) (1q31.2). Leslie Farber, Bin Tean Teh. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 792-797. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/CDC73D181ch1q31.html EIF3C (eukaryotic translation initiation factor 3, subunit C) (16p11.2). Daniel R Scoles. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 798-802. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/EIF3CID44187ch16p11.html ELAC2 (elaC homolog 2 (E. coli)) (17p11.2). Yang Chen, Sean Tavtigian, Donna Shattuck. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 803-806. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/ELAC2ID40437ch17p11.html FOXM1 (forkhead box M1) (12p13). Jamila Laoukili, Monica Alvarez Fernandez, René H Medema. -
12619 CTR9 (D1Z4F) Rabbit Mab
Revision 1 C 0 2 - t CTR9 (D1Z4F) Rabbit mAb a e r o t S Orders: 877-616-CELL (2355) [email protected] 9 Support: 877-678-TECH (8324) 1 6 Web: [email protected] 2 www.cellsignal.com 1 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source/Isotype: UniProt ID: Entrez-Gene Id: WB, IP H M R Endogenous 140 Rabbit IgG Q6PD62 9646 Product Usage Information 7. Farber, L.J. et al. (2010) Mol Carcinog 49, 215-23. Application Dilution Western Blotting 1:1000 Immunoprecipitation 1:50 Storage Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA, 50% glycerol and less than 0.02% sodium azide. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity CTR9 (D1Z4F) Rabbit mAb recognizes endogenous levels of total CTR9 protein. Species Reactivity: Human, Mouse, Rat Species predicted to react based on 100% sequence homology: Hamster, Chicken, Xenopus, Zebrafish, Bovine, Dog, Pig Source / Purification Monoclonal antibody is produced by immunizing animals with a synthetic peptide corresponding to residues near the amino terminus of human CTR9 protein. Background The PAF (RNA polymerase II (RNAPII) associated factor) complex was initially identified in yeast and is comprised of subunits PAF1, Leo1, Ctr9, Cdc73, RTF1 and Ski8 (1,2). The PAF complex plays an important role in transcription initiation and elongation by RNAPII by regulating the establishment of proper histone modifications such as histone H2B ubiquitination and the recruitment of the histone chaperone FACT (facilitates chromatin transcription) (3-5). -
Unbiased Data Mining Identifies Cell Cycle Transcripts That Predict Non-Indolent Gleason Score 7 Prostate Cancer Wendy L
Johnston et al. BMC Urology (2019) 19:4 https://doi.org/10.1186/s12894-018-0433-5 RESEARCHARTICLE Open Access Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer Wendy L. Johnston1* , Charles N. Catton1,2 and Carol J. Swallow3,4,5,6 Abstract Background: Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used to both predict clinical outcome and to inform initial management. However, significant heterogeneity is observed in outcome, particularly within the intermediate risk group, and there is an urgent need for additional markers to more accurately hone risk prediction. Recently developed web-based visualization and analysis tools have facilitated rapid interrogation of large transcriptome datasets, and querying broadly across multiple large datasets should identify predictors that are widely applicable. Methods: We used camcAPP, cBioPortal, CRN, and NIH NCI GDC Data Portal to data mine publicly available large prostate cancer datasets. A test set of biomarkers was developed by identifying transcripts that had: 1) altered abundance in prostate cancer, 2) altered expression in patients with Gleason score 7 tumours and biochemical recurrence, 3) correlation of expression with time until biochemical recurrence across three datasets (Cambridge, Stockholm, MSKCC). Transcripts that met these criteria were then examined in a validation dataset (TCGA-PRAD) using univariate and multivariable models to predict biochemical recurrence in patients with Gleason score 7 tumours. Results: Twenty transcripts met the test criteria, and 12 were validated in TCGA-PRAD Gleason score 7 patients. -
A Single Gene Network Accurately Predicts Phenotypic Effects of Gene Perturbation in Caenorhabditis Elegans
ARTICLES A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans Insuk Lee1,4, Ben Lehner2–4, Catriona Crombie2, Wendy Wong2, Andrew G Fraser2 & Edward M Marcotte1 The fundamental aim of genetics is to understand how an organism’s phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions—we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse http://www.nature.com/naturegenetics cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets. The central goal of genetics is to understand how heritable informa- not explicitly reflect multiple cell types, tissues or stages of -
Supplementary Information – Postema Et Al., the Genetics of Situs Inversus Totalis Without Primary Ciliary Dyskinesia
1 Supplementary information – Postema et al., The genetics of situs inversus totalis without primary ciliary dyskinesia Table of Contents: Supplementary Methods 2 Supplementary Results 5 Supplementary References 6 Supplementary Tables and Figures Table S1. Subject characteristics 9 Table S2. Inbreeding coefficients per subject 10 Figure S1. Multidimensional scaling to capture overall genomic diversity 11 among the 30 study samples Table S3. Significantly enriched gene-sets under a recessive mutation model 12 Table S4. Broader list of candidate genes, and the sources that led to their 13 inclusion Table S5. Potential recessive and X-linked mutations in the unsolved cases 15 Table S6. Potential mutations in the unsolved cases, dominant model 22 2 1.0 Supplementary Methods 1.1 Participants Fifteen people with radiologically documented SIT, including nine without PCD and six with Kartagener syndrome, and 15 healthy controls matched for age, sex, education and handedness, were recruited from Ghent University Hospital and Middelheim Hospital Antwerp. Details about the recruitment and selection procedure have been described elsewhere (1). Briefly, among the 15 people with radiologically documented SIT, those who had symptoms reminiscent of PCD, or who were formally diagnosed with PCD according to their medical record, were categorized as having Kartagener syndrome. Those who had no reported symptoms or formal diagnosis of PCD were assigned to the non-PCD SIT group. Handedness was assessed using the Edinburgh Handedness Inventory (EHI) (2). Tables 1 and S1 give overviews of the participants and their characteristics. Note that one non-PCD SIT subject reported being forced to switch from left- to right-handedness in childhood, in which case five out of nine of the non-PCD SIT cases are naturally left-handed (Table 1, Table S1). -
Identification and Characterization of HIRIP3 As a Novel Histone H2A Chaperone Maria Ignatyeva
Identification and characterization of HIRIP3 as a novel histone H2A chaperone Maria Ignatyeva To cite this version: Maria Ignatyeva. Identification and characterization of HIRIP3 as a novel histone H2A chaperone. Ge- nomics [q-bio.GN]. Université de Strasbourg, 2017. English. NNT : 2017STRAJ028. tel-02917943 HAL Id: tel-02917943 https://tel.archives-ouvertes.fr/tel-02917943 Submitted on 20 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. UNIVERSITÉ DE STRASBOURG ÉCOLE DOCTORALE DES SCIENCES DE LA VIE ET DE LA SANTE [Institut de génétique et de biologie moléculaire et cellulaire (IGBMC) - UM 41/UMR 7104/UMR_S 964] THÈSE présentée par : [Maria IGNATYEVA] soutenue le : 31 Mai 2017 pour obtenir le grade de : Docteur de l’université de Strasbourg Discipline/ Spécialité : Aspects Moleculaires et Cellulaires Biologie Identification et caractérisation de HIRIP3 comme nouveau chaperon d'histone H2A [Identification and characterization of HIRIP3 as a novel histone H2A chaperone] THÈSE dirigée par : [M. HAMICHE Ali] Directeur de recherches, Université de Strasbourg RAPPORTEURS : [Mme. AMEYAR-ZAZOUA Maya] Chercheur statutaire, Institut Necker Enfants Malades [M. THIRIET Christophe] Directeur de recherches, Université de Nantes AUTRES MEMBRES DU JURY : [M.