Single-Cell and Population Transcriptomics Reveal Pan-Epithelial Remodeling in Type 2-High Asthma
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Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony
Supplementary Data Th2 and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in UBIOPRED Chih-Hsi Scott Kuo1.2, Stelios Pavlidis3, Matthew Loza3, Fred Baribaud3, Anthony Rowe3, Iaonnis Pandis2, Ana Sousa4, Julie Corfield5, Ratko Djukanovic6, Rene 7 7 8 2 1† Lutter , Peter J. Sterk , Charles Auffray , Yike Guo , Ian M. Adcock & Kian Fan 1†* # Chung on behalf of the U-BIOPRED consortium project team 1Airways Disease, National Heart & Lung Institute, Imperial College London, & Biomedical Research Unit, Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom; 2Department of Computing & Data Science Institute, Imperial College London, United Kingdom; 3Janssen Research and Development, High Wycombe, Buckinghamshire, United Kingdom; 4Respiratory Therapeutic Unit, GSK, Stockley Park, United Kingdom; 5AstraZeneca R&D Molndal, Sweden and Areteva R&D, Nottingham, United Kingdom; 6Faculty of Medicine, Southampton University, Southampton, United Kingdom; 7Faculty of Medicine, University of Amsterdam, Amsterdam, Netherlands; 8European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, France. †Contributed equally #Consortium project team members are listed under Supplementary 1 Materials *To whom correspondence should be addressed: [email protected] 2 List of the U-BIOPRED Consortium project team members Uruj Hoda & Christos Rossios, Airways Disease, National Heart & Lung Institute, Imperial College London, UK & Biomedical Research Unit, Biomedical Research Unit, Royal -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Identification of Conserved Genes Triggering Puberty in European Sea
Blázquez et al. BMC Genomics (2017) 18:441 DOI 10.1186/s12864-017-3823-2 RESEARCHARTICLE Open Access Identification of conserved genes triggering puberty in European sea bass males (Dicentrarchus labrax) by microarray expression profiling Mercedes Blázquez1,2* , Paula Medina1,2,3, Berta Crespo1,4, Ana Gómez1 and Silvia Zanuy1* Abstract Background: Spermatogenesisisacomplexprocesscharacterized by the activation and/or repression of a number of genes in a spatio-temporal manner. Pubertal development in males starts with the onset of the first spermatogenesis and implies the division of primary spermatogonia and their subsequent entry into meiosis. This study is aimed at the characterization of genes involved in the onset of puberty in European sea bass, and constitutes the first transcriptomic approach focused on meiosis in this species. Results: European sea bass testes collected at the onset of puberty (first successful reproduction) were grouped in stage I (resting stage), and stage II (proliferative stage). Transition from stage I to stage II was marked by an increase of 11ketotestosterone (11KT), the main fish androgen, whereas the transcriptomic study resulted in 315 genes differentially expressed between the two stages. The onset of puberty induced 1) an up-regulation of genes involved in cell proliferation, cell cycle and meiosis progression, 2) changes in genes related with reproduction and growth, and 3) a down-regulation of genes included in the retinoic acid (RA) signalling pathway. The analysis of GO-terms and biological pathways showed that cell cycle, cell division, cellular metabolic processes, and reproduction were affected, consistent with the early events that occur during the onset of puberty. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Mediation and Moderation of Intergenerational Epigenetic Effects of Trauma
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2017 Mediation and Moderation of Intergenerational Epigenetic Effects of Trauma Stefanie Renee Pilkay University of Tennessee, Knoxville, [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Other Genetics and Genomics Commons, and the Social Work Commons Recommended Citation Pilkay, Stefanie Renee, "Mediation and Moderation of Intergenerational Epigenetic Effects of Trauma. " PhD diss., University of Tennessee, 2017. https://trace.tennessee.edu/utk_graddiss/4753 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Stefanie Renee Pilkay entitled "Mediation and Moderation of Intergenerational Epigenetic Effects of Trauma." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Social Work. Terri Combs-Orme, Major Professor We have read this dissertation and recommend its acceptance: John G. Orme, Shandra Forrest-Bank, Matthew Cooper, Alicia K. Smith Accepted for the Council: Dixie L. Thompson Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) Mediation and Moderation of Intergenerational Epigenetic Effects of Trauma A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Stefanie Renee Pilkay December 2017 Copyright © 2017 by Stefanie R. -
Evolution of Vertebrate Solute Carrier Family 9B
etics & E en vo g lu t lo i y o h n a P r f y Journal of Phylogenetics & Holmes et al., J Phylogen Evolution Biol 2016, 4:3 o B l i a o n l r o DOI: 10.4172/2329-9002.1000167 u g o y J Evolutionary Biology ISSN: 2329-9002 Research Article Open Access Evolution of Vertebrate Solute Carrier Family 9B Genes and Proteins (SLC9B): Evidence for a Marsupial Origin for Testis Specific SLC9B1 from an Ancestral Vertebrate SLC9B2 Gene Roger S Holmes1*,2, Kimberly D Spradling-Reeves2 and Laura A Cox2 1Eskitis Institute for Drug Discovery and School of Natural Sciences, Griffith University, Nathan, QLD, Australia 2Department of Genetics and Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, USA Abstract SLC9B genes and proteins are members of the sodium/lithium hydrogen antiporter family which function as solute exchangers within cellular membranes of mammalian tissues. SLC9B2 and SLC9B1 amino acid sequences and structures and SLC9B-like gene locations were examined using bioinformatic data from several vertebrate genome projects. Vertebrate SLC9B2 sequences shared 56-98% identity as compared with ~50% identities with mammalian SLC9B1 sequences. Sequence alignments, key amino acid residues and conserved predicted transmembrane structures were also studied. Mammalian SLC9B2 and SLC9B1 genes usually contained 11 or 12 coding exons with differential tissue expression patterns: SLC9B2, broad tissue distribution; and SLC9B1, being testis specific. Transcription factor binding sites and CpG islands within the human SLC9B2 and SLC9B1 gene promoters were identified. Phylogenetic analyses suggested thatSLC9B1 originated in an ancestral marsupial genome from a SLC9B2 gene duplication event. -
Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations
IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. X, NO. X, MONTHXXX 20XX 1 Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations Davide Chicco, Fernando Palluzzi, and Marco Masseroli Abstract—Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand their test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and may contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new biomolecular annotations are both effective and efficient, especially if compared with the time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritizing their results, we introduce here a new element that can improve the accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of ”newness” (or ”originality”) of the annotations predicted for a specific gene to Gene Ontology terms, and to help prioritizing the most novel and interesting annotations predicted. We performed a thorough biological functional analysis of the prioritized annotations predicted with high accuracy by using this indicator and our previously proposed prediction algorithms. The relevance -
A Discovery Resource of Rare Copy Number Variations in Individuals with Autism Spectrum Disorder
INVESTIGATION A Discovery Resource of Rare Copy Number Variations in Individuals with Autism Spectrum Disorder Aparna Prasad,* Daniele Merico,* Bhooma Thiruvahindrapuram,* John Wei,* Anath C. Lionel,*,† Daisuke Sato,* Jessica Rickaby,* Chao Lu,* Peter Szatmari,‡ Wendy Roberts,§ Bridget A. Fernandez,** Christian R. Marshall,*,†† Eli Hatchwell,‡‡ Peggy S. Eis,‡‡ and Stephen W. Scherer*,†,††,1 *The Centre for Applied Genomics, Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto M5G 1L7, Canada, †Department of Molecular Genetics, University of Toronto, Toronto M5G 1L7, Canada, ‡Offord Centre for Child Studies, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton L8P 3B6, § Canada, Autism Research Unit, The Hospital for Sick Children, Toronto M5G 1X8, Canada, **Disciplines of Genetics and Medicine, Memorial University of Newfoundland, St. John’s, Newfoundland A1B 3V6, Canada, ††McLaughlin Centre, University of Toronto, Toronto M5G 1L7, Canada, and ‡‡Population Diagnostics, Inc., Melville, New York 11747 ABSTRACT The identification of rare inherited and de novo copy number variations (CNVs) in human KEYWORDS subjects has proven a productive approach to highlight risk genes for autism spectrum disorder (ASD). A rare variants variety of microarrays are available to detect CNVs, including single-nucleotide polymorphism (SNP) arrays gene copy and comparative genomic hybridization (CGH) arrays. Here, we examine a cohort of 696 unrelated ASD number cases using a high-resolution one-million feature CGH microarray, the majority of which were previously chromosomal genotyped with SNP arrays. Our objective was to discover new CNVs in ASD cases that were not detected abnormalities by SNP microarray analysis and to delineate novel ASD risk loci via combined analysis of CGH and SNP array cytogenetics data sets on the ASD cohort and CGH data on an additional 1000 control samples. -
Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts
University of Calgary PRISM: University of Calgary's Digital Repository Graduate Studies The Vault: Electronic Theses and Dissertations 2017 Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts Zabinyakov, Nikita Zabinyakov, N. (2017). Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/27775 http://hdl.handle.net/11023/3639 master thesis University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY Shear Stress Modulates Gene Expression in Normal Human Dermal Fibroblasts by Nikita Zabinyakov A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN BIOMEDICAL ENGINEERING CALGARY, ALBERTA JANUARY 2017 © Nikita Zabinyakov 2017 Abstract Applied mechanical forces, such as those resulting from fluid flow, trigger cells to change their functional behavior or phenotype. However, there is little known about how fluid flow affects fibroblasts. The hypothesis of this thesis is that dermal fibroblasts undergo significant changes of expression of differentiation genes after exposure to fluid flow (or shear stress). To test the hypothesis, human dermal fibroblasts were exposed to laminar steady fluid flow for 20 and 40 hours and RNA was collected for microarray analysis. -
Supplementary Material Computational Prediction of SARS
Supplementary_Material Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets Sheet_1 List of potential stem-loop structures in SARS-CoV-2 genome as predicted by VMir. Rank Name Start Apex Size Score Window Count (Absolute) Direct Orientation 1 MD13 2801 2864 125 243.8 61 2 MD62 11234 11286 101 211.4 49 4 MD136 27666 27721 104 205.6 119 5 MD108 21131 21184 110 204.7 210 9 MD132 26743 26801 119 188.9 252 19 MD56 9797 9858 128 179.1 59 26 MD139 28196 28233 72 170.4 133 28 MD16 2934 2974 76 169.9 71 43 MD103 20002 20042 80 159.3 403 46 MD6 1489 1531 86 156.7 171 51 MD17 2981 3047 131 152.8 38 87 MD4 651 692 75 140.3 46 95 MD7 1810 1872 121 137.4 58 116 MD140 28217 28252 72 133.8 62 122 MD55 9712 9758 96 132.5 49 135 MD70 13171 13219 93 130.2 131 164 MD95 18782 18820 79 124.7 184 173 MD121 24086 24135 99 123.1 45 176 MD96 19046 19086 75 123.1 179 196 MD19 3197 3236 76 120.4 49 200 MD86 17048 17083 73 119.8 428 223 MD75 14534 14600 137 117 51 228 MD50 8824 8870 94 115.8 79 234 MD129 25598 25642 89 115.6 354 Reverse Orientation 6 MR61 19088 19132 88 197.8 271 10 MR72 23563 23636 148 188.8 286 11 MR11 3775 3844 136 185.1 116 12 MR94 29532 29582 94 184.6 271 15 MR43 14973 15028 109 183.9 226 27 MR14 4160 4206 89 170 241 34 MR35 11734 11792 111 164.2 37 52 MR5 1603 1652 89 152.7 118 53 MR57 18089 18132 101 152.7 139 94 MR8 2804 2864 122 137.4 38 107 MR58 18474 18508 72 134.9 237 117 MR16 4506 4540 72 133.8 311 120 MR34 10010 10048 82 132.7 245 133 MR7 2534 2578 90 130.4 75 146 MR79 24766 24808 75 127.9 59 150 MR65 21528 21576 99 127.4 83 180 MR60 19016 19049 70 122.5 72 187 MR51 16450 16482 75 121 363 190 MR80 25687 25734 96 120.6 75 198 MR64 21507 21544 70 120.3 35 206 MR41 14500 14542 84 119.2 94 218 MR84 26840 26894 108 117.6 94 Sheet_2 List of stable stem-loop structures based on MFE.