DOCSLIB.ORG

CLC Microbial Genomics Module

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
CLC Microbial Genomics Module CLC Microbial Genomics Module USER MANUAL User manual for CLC Microbial Genomics Module 21.1 Windows, macOS and Linux June 25, 2021 This software is for research purposes only. QIAGEN Aarhus Silkeborgvej 2 Prismet DK-8000 Aarhus C Denmark Contents I Introduction 11 1 Introduction 12 1.1 The concept of CLC Microbial Genomics Module................... 12 1.2 Contact information.................................. 13 2 System requirements and installation 14 2.1 System requirements................................. 14 2.2 Installation of modules................................ 15 2.3 Licensing modules................................... 16 2.4 Uninstalling modules................................. 17 2.5 Server License..................................... 17 II Metagenomics 21 3 Introduction to Metagenomics 22 4 De Novo Assemble Metagenome 23 5 Amplicon-Based Analysis 25 5.1 Normalize OTU Table by Copy Number........................ 25 5.2 Filter Samples Based on Number of Reads..................... 26 5.3 OTU clustering..................................... 27 5.3.1 OTU clustering parameters.......................... 28 5.3.2 OTU clustering tool outputs.......................... 31 5.3.3 Visualization of OTU abundance tables.................... 32 5.3.4 Create taxonomic level subtables for heat maps............... 35 3 CONTENTS 4 5.3.5 Importing and exporting OTU abundance tables............... 36 5.4 Remove OTUs with Low Abundance.......................... 37 5.5 Align OTUs with MUSCLE............................... 37 5.6 Amplicon-Based Analysis Workflows......................... 38 5.6.1 Data QC and OTU Clustering workflow.................... 38 5.6.2 Estimate Alpha and Beta Diversities workflow................ 39 6 Taxonomic Analysis 41 6.1 Contig Binning..................................... 41 6.1.1 Bin Pangenomes by Taxonomy........................ 41 6.1.2 Bin Pangenomes by Sequence........................ 44 6.2 Taxonomic Profiling.................................. 45 6.2.1 Taxonomic profiling abundance table..................... 47 6.2.2 Taxonomic Profiling report........................... 51 6.2.3 Sorted Reads Files.............................. 51 6.3 Identify Viral Integration Sites............................. 52 6.3.1 The Viral Integration Viewer.......................... 55 6.3.2 The Viral Integration Report.......................... 57 6.4 Workflows....................................... 57 6.4.1 Analyze Viral Hybrid Capture Panel Data................... 57 6.4.2 Data QC and Clean Host DNA......................... 64 6.4.3 Data QC and Taxonomic Profiling....................... 65 6.4.4 Merge and Estimate Alpha and Beta diversities............... 67 6.4.5 QC, Assemble and Bin Pangenomes..................... 68 7 Abundance Analysis 72 7.1 Merge Abundance Tables............................... 72 7.2 Alpha Diversity..................................... 72 7.2.1 Alpha diversity measures........................... 76 7.3 Beta Diversity..................................... 77 7.3.1 Beta diversity measures............................ 79 7.4 PERMANOVA Analysis................................. 80 7.5 Differential Abundance Analysis............................ 81 CONTENTS 5 7.6 Create Heat Map for Abundance Table........................ 83 7.7 Add Metadata to Abundance Table.......................... 86 7.8 Convert Abundance Table to Experiment....................... 87 III Typing and Epidemiology 89 8 Introduction to Typing and Epidemiology 90 9 Handling of metadata and analysis results 92 9.1 Importing Metadata Tables.............................. 92 9.2 Associating data elements with metadata...................... 94 9.3 Create a Result Metadata Table........................... 95 9.4 Running an analysis directly from a Result Metadata Table............. 97 9.4.1 Filtering in Result Metadata Table...................... 98 9.4.2 Filtering in a SNP-Tree creation scenario................... 99 9.5 Extend Result Metadata Table............................ 99 9.6 Add to Result Metadata Table (legacy)........................ 101 9.7 Use Genome as Result................................ 102 10 Workflow templates 105 10.1 Map to Specified Reference.............................. 105 10.1.1 How to run the Map to Specified Reference workflow............ 107 10.2 Type Among Multiple Species............................. 112 10.2.1 Preliminary steps to run the Type Among Multiple Species workflow.... 113 10.2.2 How to run the Type Among Multiple Species workflow........... 113 10.2.3 Example of results obtained using the Type Among Multiple Species workflow121 10.3 Type a Known Species................................ 123 10.3.1 Preliminary steps to run the Type a Known Species workflow........ 123 10.3.2 How to run the Type a Known Species workflow............... 124 10.3.3 Example of results obtained using the Type a Known Species workflow.. 130 10.4 Extract Regions from Tracks............................. 132 10.5 Create Large MLST Scheme with Sequence Types.................. 133 10.6 Compare Variants Across Samples.......................... 135 CONTENTS 6 11 Find the best matching reference 140 11.1 Find Best Matches using K-mer Spectra....................... 140 11.2 From samples best matches to a common reference for all............. 143 11.3 Find Best References using Read Mapping...................... 144 11.3.1 The Find Best References using Read Mapping Report........... 145 12 Phylogenetic trees using SNPs or k-mers 147 12.1 Create SNP Tree.................................... 147 12.1.1 SNP tree output report............................ 151 12.1.2 Visualization of SNP Tree including metadata and analysis result metadata 153 12.1.3 SNP Tree Variants editor viewer........................ 153 12.1.4 SNP Matrix................................... 154 12.2 Create K-mer Tree................................... 155 12.2.1 Visualization of K-mer Tree for identification of common reference..... 157 13 Large MLST Scheme Tools 159 13.1 Getting started with the Large MLST Scheme tools................. 159 13.2 Large MLST Scheme Visualization and Management................ 160 13.3 Minimum Spanning Trees............................... 163 13.3.1 The Minimum Spanning Tree view...................... 164 13.3.2 Navigating the Tree view........................... 164 13.3.3 The Layout panel............................... 165 13.3.4 The Metadata panel.............................. 166 13.4 Type With Large MLST Scheme............................ 169 13.4.1 Type With Large MLST Scheme results.................... 171 13.4.2 The Large MLST Typing Result element.................... 172 13.5 Add Typing Results to Large MLST Scheme..................... 174 13.6 Identify Large MLST Scheme from Genomes..................... 175 IV Functional and Drug Resistance Analyses 177 14 Functional Analysis 178 14.1 Find Prokaryotic Genes................................ 178 CONTENTS 7 14.2 Annotate with BLAST................................. 181 14.3 Annotate with DIAMOND................................ 184 14.4 Annotate CDS with Best BLAST Hit.......................... 187 14.5 Annotate CDS with Best DIAMOND Hit........................ 189 14.6 Annotate CDS with Pfam Domains.......................... 190 14.7 Build Functional Profile................................ 192 14.7.1 Functional profile abundance table...................... 193 14.8 Infer Functional Profile................................. 197 15 Drug Resistance Analysis 199 15.1 Find Resistance with PointFinder........................... 199 15.2 Find Resistance with Nucleotide DB......................... 201 15.3 Find Resistance with ShortBRED........................... 202 15.3.1 Resistance abundance table......................... 204 V Databases 208 16 Databases for Large MLST Schemes 209 16.1 Create Large MLST Scheme.............................. 209 16.2 Download Large MLST Scheme............................ 213 16.3 Import Large MLST Scheme.............................. 218 17 Databases for Amplicon-Based Analysis 220 17.1 Download Amplicon-Based Reference Database................... 220 18 Databases for Taxonomic Analysis 221 18.1 Download Curated Microbial Reference Database.................. 221 18.1.1 Extracting a subset of a database...................... 222 18.2 Download Custom Microbial Reference Database.................. 223 18.2.1 Database Builder............................... 225 18.3 Download Pathogen Reference Database...................... 228 18.3.1 Download Pathogen Reference Database output report........... 229 18.4 Create Taxonomic Profiling Index........................... 230 CONTENTS 8 19 Databases for Functional Analysis 232 19.1 Download Protein Database.............................. 232 19.2 Download Ontology Database............................. 232 19.2.1 The GO Database View............................ 233 19.2.2 The EC Database View............................ 234 19.3 Create DIAMOND Index................................ 235 19.4 Import RNAcentral Database............................. 236 19.5 Import PICRUSt2 Multiplication Table......................... 237 20 Databases for Drug Resistance Analysis 240 20.1 Download Resistance Database........................... 240 20.1.1 ARES Database................................ 242 VI Panel Support 248 21 QIAseq 16S/ITS Demultiplexer 249 VII Tools 252 22 Tools 253 22.1 Split Sequence List.................................. 253 22.2 Create Annotated Sequence List........................... 255 22.2.1 Examples of databases...........................
Load more

Recommended publications
  • The Enigmatic Charcot-Leyden Crystal Protein
    C al & ellu ic la n r li Im C m f u Journal of o n l o a l n o r Clarke et al., J Clin Cell Immunol 2015, 6:2 g u y o J DOI: 10.4172/2155-9899.1000323 ISSN: 2155-9899 Clinical & Cellular Immunology Review Article Open Access The Enigmatic Charcot-Leyden Crystal Protein (Galectin-10): Speculative Role(s) in the Eosinophil Biology and Function Christine A Clarke1,3, Clarence M Lee2 and Paulette M Furbert-Harris1,3,4* 1Department of Microbiology, Howard University College of Medicine, Washington, D.C., USA 2Department of Biology, Howard University College of Arts and Sciences, Washington, D.C., USA 3Howard University Cancer Center, Howard University College of Medicine, Washington, D.C., USA 4National Human Genome Center, Howard University College of Medicine, Washington, D.C., USA *Corresponding author: Furbert-Harris PM, Department of Microbiology, Howard University Cancer Center, 2041 Georgia Avenue NW, Room #530, 20060, Washington, D.C., USA, Tel: 202-806-7722, Fax: 202-667-1686; Email: [email protected] Received date: Febuary 22, 2015, Accepted date: April 25, 2015, Published date: April 29, 2015 Copyright: © 2015 Clarke CA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Eosinophilic inflammation in peripheral tissues is typically marked by the deposition of a prominent eosinophil protein, Galectin-10, better known as Charcot-Leyden crystal protein (CLC). Unlike the eosinophil’s four distinct toxic cationic proteins and enzymes [major basic protein (MBP), eosinophil-derived neurotoxin (EDN), eosinophil cationic protein (ECP), and eosinophil peroxidase (EPO)], there is a paucity of information on the precise role of the crystal protein in the biology of the eosinophil.
    [Show full text]
  • Human Lectins, Their Carbohydrate Affinities and Where to Find Them
    biomolecules Review Human Lectins, Their Carbohydrate Affinities and Where to Review HumanFind Them Lectins, Their Carbohydrate Affinities and Where to FindCláudia ThemD. Raposo 1,*, André B. Canelas 2 and M. Teresa Barros 1 1, 2 1 Cláudia D. Raposo * , Andr1 é LAQVB. Canelas‐Requimte,and Department M. Teresa of Chemistry, Barros NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829‐516 Caparica, Portugal; [email protected] 12 GlanbiaLAQV-Requimte,‐AgriChemWhey, Department Lisheen of Chemistry, Mine, Killoran, NOVA Moyne, School E41 of ScienceR622 Co. and Tipperary, Technology, Ireland; canelas‐ [email protected] NOVA de Lisboa, 2829-516 Caparica, Portugal; [email protected] 2* Correspondence:Glanbia-AgriChemWhey, [email protected]; Lisheen Mine, Tel.: Killoran, +351‐212948550 Moyne, E41 R622 Tipperary, Ireland; [email protected] * Correspondence: [email protected]; Tel.: +351-212948550 Abstract: Lectins are a class of proteins responsible for several biological roles such as cell‐cell in‐ Abstract:teractions,Lectins signaling are pathways, a class of and proteins several responsible innate immune for several responses biological against roles pathogens. such as Since cell-cell lec‐ interactions,tins are able signalingto bind to pathways, carbohydrates, and several they can innate be a immuneviable target responses for targeted against drug pathogens. delivery Since sys‐ lectinstems. In are fact, able several to bind lectins to carbohydrates, were approved they by canFood be and a viable Drug targetAdministration for targeted for drugthat purpose. delivery systems.Information In fact, about several specific lectins carbohydrate were approved recognition by Food by andlectin Drug receptors Administration was gathered for that herein, purpose. plus Informationthe specific organs about specific where those carbohydrate lectins can recognition be found by within lectin the receptors human was body.
    [Show full text]
  • CLC and IFNAR1 Are Differentially Expressed and a Global Immunity Score Is Distinct Between Early- and Late-Onset Colorectal Cancer
    Genes and Immunity (2011) 12, 653–662 & 2011 Macmillan Publishers Limited All rights reserved 1466-4879/11 www.nature.com/gene ORIGINAL ARTICLE CLC and IFNAR1 are differentially expressed and a global immunity score is distinct between early- and late-onset colorectal cancer TH A˚ gesen1,2, M Berg1,2, T Clancy3, E Thiis-Evensen4, L Cekaite1,2, GE Lind1,2, JM Nesland5,6, A Bakka7, T Mala8, HJ Hauss9, T Fetveit10, MH Vatn4,11, E Hovig3,12,13, A Nesbakken2,6,8, RA Lothe1,2,6 and RI Skotheim1,2 1Department of Cancer Prevention, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway; 2Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway; 3Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway; 4Department for Organ Transplantation, Gastroenterology and Nephrology, Rikshospitalet, Oslo University Hospital, Oslo, Norway; 5Division of Pathology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway; 6Faculty of Medicine, The University of Oslo, Oslo, Norway; 7Department of Digestive Surgery, Akershus University Hospital, Lørenskog, Norway; 8Department of Gastrointestinal Surgery, Aker, Oslo University Hospital, Oslo, Norway; 9Department of Gastrointestinal Surgery, Sørlandet Hospital, Kristiansand, Norway; 10Department of Surgery, Sørlandet Hospital, Arendal, Norway; 11Epigen, Akershus University Hospital, Lørenskog, Norway; 12Institute of Medical Informatics, The Norwegian Radium Hospital, Oslo University
    [Show full text]
  • A Software for Detection of Alternative Splicing Events in Human
    www.nature.com/scientificreports OPEN SpliceDetector: a software for detection of alternative splicing events in human and model Received: 21 June 2017 Accepted: 2 March 2018 organisms directly from transcript Published: xx xx xxxx IDs Mandana Baharlou Houreh1, Payam Ghorbani Kalkhajeh2, Ali Niazi1, Faezeh Ebrahimi3 & Esmaeil Ebrahimie 1,4,5,6 In eukaryotes, diferent combinations of exons lead to multiple transcripts with various functions in protein level, in a process called alternative splicing (AS). Unfolding the complexity of functional genomics through genome-wide profling of AS and determining the altered ultimate products provide new insights for better understanding of many biological processes, disease progress as well as drug development programs to target harmful splicing variants. The current available tools of alternative splicing work with raw data and include heavy computation. In particular, there is a shortcoming in tools to discover AS events directly from transcripts. Here, we developed a Windows-based user-friendly tool for identifying AS events from transcripts without the need to any advanced computer skill or database download. Meanwhile, due to online working mode, our application employs the updated SpliceGraphs without the need to any resource updating. First, SpliceGraph forms based on the frequency of active splice sites in pre-mRNA. Then, the presented approach compares query transcript exons to SpliceGraph exons. The tool provides the possibility of statistical analysis of AS events as well as AS visualization compared to SpliceGraph. The developed application works for transcript sets in human and model organisms. Transcripts are products of pre-mRNA splicing processes. Novel transcripts discover each day1,2 and add to public databases.
    [Show full text]
  • Informatics and Clinical Genome Sequencing: Opening the Black Box
    ©American College of Medical Genetics and Genomics REVIEW Informatics and clinical genome sequencing: opening the black box Sowmiya Moorthie, PhD1, Alison Hall, MA 1 and Caroline F. Wright, PhD1,2 Adoption of whole-genome sequencing as a routine biomedical tool present an overview of the data analysis and interpretation pipeline, is dependent not only on the availability of new high-throughput se- the wider informatics needs, and some of the relevant ethical and quencing technologies, but also on the concomitant development of legal issues. methods and tools for data collection, analysis, and interpretation. Genet Med 2013:15(3):165–171 It would also be enormously facilitated by the development of deci- sion support systems for clinicians and consideration of how such Key Words: bioinformatics; data analysis; massively parallel; information can best be incorporated into care pathways. Here we next-generation sequencing INTRODUCTION Each of these steps requires purpose-built databases, algo- Technological advances have resulted in a dramatic fall in the rithms, software, and expertise to perform. By and large, issues cost of human genome sequencing. However, the sequencing related to primary analysis have been solved and are becom- assay is only the beginning of the process of converting a sample ing increasingly automated, and are therefore not discussed of DNA into meaningful genetic information. The next step of further here. Secondary analysis is also becoming increasingly data collection and analysis involves extensive use of various automated for human genome resequencing, and methods of computational methods for converting raw data into sequence mapping reads to the most recent human genome reference information, and the application of bioinformatics techniques sequence (GRCh37), and calling variants from it, are becom- for the interpretation of that sequence.
    [Show full text]
  • RNA-Seq Reveals Divergent Gene Expression Between Larvae
    www.nature.com/scientificreports OPEN RNA‑Seq reveals divergent gene expression between larvae with contrasting trophic modes in the poecilogonous polychaete Boccardia wellingtonensis Álvaro Figueroa1*, Antonio Brante2,3 & Leyla Cárdenas1,4 The polychaete Boccardia wellingtonensis is a poecilogonous species that produces diferent larval types. Females may lay Type I capsules, in which only planktotrophic larvae are present, or Type III capsules that contain planktotrophic and adelphophagic larvae as well as nurse eggs. While planktotrophic larvae do not feed during encapsulation, adelphophagic larvae develop by feeding on nurse eggs and on other larvae inside the capsules and hatch at the juvenile stage. Previous works have not found diferences in the morphology between the two larval types; thus, the factors explaining contrasting feeding abilities in larvae of this species are still unknown. In this paper, we use a transcriptomic approach to study the cellular and genetic mechanisms underlying the diferent larval trophic modes of B. wellingtonensis. By using approximately 624 million high-quality reads, we assemble the de novo transcriptome with 133,314 contigs, coding 32,390 putative proteins. We identify 5221 genes that are up-regulated in larval stages compared to their expression in adult individuals. The genetic expression profle difered between larval trophic modes, with genes involved in lipid metabolism and chaetogenesis over expressed in planktotrophic larvae. In contrast, up-regulated genes in adelphophagic larvae were associated with DNA replication and mRNA synthesis. Marine invertebrates exhibit contrasting developmental modes that may afect the speciation, extinction, and connectivity of species1. In species that encapsulate their ofspring, the indirect developmental mode is char- acterized by embryos that develop partially inside capsules and hatch as planktotrophic larvae.
    [Show full text]
  • Blockade of PD-1, PD-L1, and TIM-3 Altered Distinct Immune- and Cancer-Related Signaling Pathways in the Transcriptome of Human Breast Cancer Explants
    G C A T T A C G G C A T genes Article Blockade of PD-1, PD-L1, and TIM-3 Altered Distinct Immune- and Cancer-Related Signaling Pathways in the Transcriptome of Human Breast Cancer Explants 1, 1, 2 1 1, Reem Saleh y, Salman M. Toor y, Dana Al-Ali , Varun Sasidharan Nair and Eyad Elkord * 1 Cancer Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha 34110, Qatar; [email protected] (R.S.); [email protected] (S.M.T.); [email protected] (V.S.N.) 2 Department of Medicine, Weil Cornell Medicine-Qatar, Doha 24144, Qatar; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +974-4454-2367 Authors contributed equally to this work. y Received: 21 May 2020; Accepted: 21 June 2020; Published: 25 June 2020 Abstract: Immune checkpoint inhibitors (ICIs) are yet to have a major advantage over conventional therapies, as only a fraction of patients benefit from the currently approved ICIs and their response rates remain low. We investigated the effects of different ICIs—anti-programmed cell death protein 1 (PD-1), anti-programmed death ligand-1 (PD-L1), and anti-T cell immunoglobulin and mucin-domain containing-3 (TIM-3)—on human primary breast cancer explant cultures using RNA-Seq. Transcriptomic data revealed that PD-1, PD-L1, and TIM-3 blockade follow unique mechanisms by upregulating or downregulating distinct pathways, but they collectively enhance immune responses and suppress cancer-related pathways to exert anti-tumorigenic effects.
    [Show full text]
  • Crystal Structure of Human Charcot-Leyden Crystal Protein, An
    Crystal structure of human Charcot-Leyden crystal protein, an eosinophil lysophospholipase, identifies it as a new member of the carbohydrate-binding family of galectins Demetrios D Leonidas, Boris L Elbert2, Zeqi Zhou2, Hakon Leffler3, Steven J Ackerman 2 and K Ravi Acharyal * 'School of Biology and Biochemistry, University of Bath, Claverton Down, Bath BA2 7AY, UK, 2Division of Infectious Diseases, Department of Medicine, Beth Israel Hospital and Harvard Medical School, Boston, MA 02215, USA and 3Center for Neurobiology and Psychiatry, Departments of Psychiatry and Pharmaceutical Chemistry, University of California, California, CA 94143, USA Background: The Charcot-Leyden crystal (CLC) pro- the highest resolution structure so far determined for any tein is a major autocrystallizing constituent of human member of the galectin family. eosinophils and basophils, comprising -10% of the total Conclusions: The CLC protein structure possesses a car- cellular protein in these granulocytes. Identification of the bohydrate-recognition domain comprising most, but not distinctive hexagonal bipyramidal crystals of CLC protein all, of the carbohydrate-binding residues that are con- in body fluids and secretions has long been considered a served among the galectins. The protein exhibits specific hallmark of eosinophil-associated allergic inflammation. (albeit weak) carbohydrate-binding activity for simple Although CLC protein possesses lysophospholipase activ- saccharides including N-acetyl-D-glucosamine and lac- ity, its role(s) in eosinophil or basophil function or associ- tose. Despite CLC protein having no significant sequence ated inflammatory responses has remained speculative. or structural similarities to other lysophospholipases or Results: The crystal structure of the CLC protein has lipolytic enzymes, a possible lysophospholipase catalytic been determined at 1.8 A resolution using X-ray crystal- triad has also been identified within the CLC structure, lography.
    [Show full text]
  • Draft Genome of the Common Snapping Turtle, Chelydra Serpentina, a Model for Phenotypic Plasticity in Reptiles
    FEATURED ARTICLE GENOME REPORT Draft Genome of the Common Snapping Turtle, Chelydra serpentina, a Model for Phenotypic Plasticity in Reptiles Debojyoti Das,*,1 Sunil Kumar Singh,*,1 Jacob Bierstedt,* Alyssa Erickson,* Gina L. J. Galli,† Dane A. Crossley, II,‡ and Turk Rhen*,2 *Department of Biology, University of North Dakota, Grand Forks, North Dakota 58202, †Division of Cardiovascular Sciences, School of Medical Sciences, University of Manchester, Manchester M13 9NT, UK, and ‡Department of Biological Sciences, University of North Texas, Denton, Texas 76203 ABSTRACT Turtles are iconic reptiles that inhabit a range of ecosystems from oceans to deserts and climates KEYWORDS from the tropics to northern temperate regions. Yet, we have little understanding of the genetic adaptations Snapping turtle that allow turtles to survive and reproduce in such diverse environments. Common snapping turtles, Chelydra Chelydra serpentina, are an ideal model species for studying adaptation to climate because they are widely distributed serpentina from tropical to northern temperate zones in North America. They are also easy to maintain and breed in genome captivity and produce large clutch sizes, which makes them amenable to quantitative genetic and molecular assembly genetic studies of traits like temperature-dependent sex determination. We therefore established a captive genome breeding colony and sequenced DNA from one female using both short and long reads. After trimming and annotation filtering, we had 209.51Gb of Illumina reads, 25.72Gb of PacBio reads, and 21.72 Gb of Nanopore reads. The phenotypic assembled genome was 2.258 Gb in size and had 13,224 scaffolds with an N50 of 5.59Mb.
    [Show full text]
  • The Emerging Role of Galectins and O-Glcnac Homeostasis in Processes of Cellular Differentiation
    cells Review The Emerging Role of Galectins and O-GlcNAc Homeostasis in Processes of Cellular Differentiation Rada Tazhitdinova and Alexander V. Timoshenko * Department of Biology, The University of Western Ontario, London, ON N6A 5B7, Canada; [email protected] * Correspondence: [email protected] Received: 29 June 2020; Accepted: 24 July 2020; Published: 28 July 2020 Abstract: Galectins are a family of soluble β-galactoside-binding proteins with diverse glycan-dependent and glycan-independent functions outside and inside the cell. Human cells express twelve out of sixteen recognized mammalian galectin genes and their expression profiles are very different between cell types and tissues. In this review, we summarize the current knowledge on the changes in the expression of individual galectins at mRNA and protein levels in different types of differentiating cells and the effects of recombinant galectins on cellular differentiation. A new model of galectin regulation is proposed considering the change in O-GlcNAc homeostasis between progenitor/stem cells and mature differentiated cells. The recognition of galectins as regulatory factors controlling cell differentiation and self-renewal is essential for developmental and cancer biology to develop innovative strategies for prevention and targeted treatment of proliferative diseases, tissue regeneration, and stem-cell therapy. Keywords: galectins; O-GlcNAc; cellular differentiation; unconventional secretion; stem cells; signaling 1. Introduction Galectins are a family of soluble β-galactoside-binding proteins with diverse glycan-dependent and glycan-independent functions outside and inside the animal cell [1,2]. Galectins are identified by numbers (from -1 to -16) and 12 of them have homologues in human cells [3–6]. As such, galectins create a complex network of soluble proteins localized both outside cells and in different subcellular compartments (cytosol, mitochondria, nucleus).
    [Show full text]
  • Brief View of Advanced Molecular Detection and Bioinformatics Training Opportunities
    Brief View of Advanced Molecular Detection and Bioinformatics Training Opportunities Title Topic Course Description This class describes how to access information about genes and their variants associated with Introduction to Clinical Genomics Variant Analysis; Databases diseases and the impact of variants on drug response and dosing guidelines. More… Ingenuity Pathway Analysis Metabolomics More… This course describes how to obtain information about a human gene at all levels of the central dogma Gene Resources: From Transcription Factor Databases; Functional Analysis of life, genome, transcript and protein, and prediction of transcription factors regulating its expression. Binding Sites to Function More… Expression Data Analysis on Microarray and Expression Analysis More… NGS in Partek Pathway Studio Metabolomics; Functional Analysis More… Taught in the context of biological research, this course teaches biologists how to use the scripting Languages CS101A Perl for Biologists, Level 1 language Perl to automate certain tasks. More… Taught in the context of biological research, this course shows biologists how to use the scripting CS101A Perl for Biologists, Level 2 Languages language Perl to automate certain tasks. It is a continuation of CS101A Perl for Biologists, Level 1 and covers advanced topics and projects. More… Taught in the context of biological research, this course shows biologists how to use the scripting language Perl to automate certain tasks. It is a continuation of CS102A Perl for Biologists, Level 2 and CS103A Perl for Biologists, Level 3 Languages covers advanced topics in regular expressions, objects, modules will be covered, along with tips and tricks to fine-tune programs and resolve bugs. More... Taught in the context of biological research, this course helps biologists learn how to use the statistical CS101B R for Biologists, Level 1 Languages scripting language R for data analysis.
    [Show full text]
  • CLC Sequence Viewer Manual for CLC Sequence Viewer 6.5 Windows, Mac OS X and Linux
    CLC Sequence Viewer Manual for CLC Sequence Viewer 6.5 Windows, Mac OS X and Linux January 26, 2011 This software is for research purposes only. CLC bio Finlandsgade 10-12 DK-8200 Aarhus N Denmark Contents I Introduction7 1 Introduction to CLC Sequence Viewer 8 1.1 Contact information.................................9 1.2 Download and installation..............................9 1.3 System requirements................................ 12 1.4 About CLC Workbenches.............................. 12 1.5 When the program is installed: Getting started................... 14 1.6 Plug-ins........................................ 15 1.7 Network configuration................................ 18 1.8 The format of the user manual........................... 19 2 Tutorials 20 2.1 Tutorial: Getting started............................... 20 2.2 Tutorial: View sequence............................... 22 2.3 Tutorial: Side Panel Settings............................ 23 2.4 Tutorial: GenBank search and download...................... 27 2.5 Tutorial: Align protein sequences.......................... 28 2.6 Tutorial: Create and modify a phylogenetic tree.................. 29 2.7 Tutorial: Find restriction sites............................ 31 II Core Functionalities 34 3 User interface 35 3.1 Navigation Area................................... 36 3.2 View Area....................................... 43 3 CONTENTS 4 3.3 Zoom and selection in View Area.......................... 49 3.4 Toolbox and Status Bar............................... 50 3.5 Workspace.....................................
    [Show full text]