Conservation in Mammals of Genes Associated with Aggression-Related Behavioral Phenotypes in Honey Bees
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Migration-Selection Balance Drives Genetic Differentiation in Genes Associated with High-Altitude Function in the Speckled
GBE Migration-Selection Balance Drives Genetic Differentiation in Genes Associated with High-Altitude Function in the Speckled Teal (Anas flavirostris) in the Andes Downloaded from https://academic.oup.com/gbe/article-abstract/10/1/14/4685994 by University of Texas at El Paso user on 18 January 2019 Allie M. Graham1,*, Philip Lavretsky2, Violeta Munoz-Fuentes~ 3,4,AndyJ.Green4,RobertE.Wilson5,and Kevin G. McCracken1,5,6,7 1Department of Biology, University of Miami 2Department of Biological Sciences, University of Texas El Paso 3European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom 4Estacion Biologica de Donana,~ EBD-CSIC, Sevilla, Spain 5Institute of Arctic Biology and University of Alaska Museum, University of Alaska, Fairbanks 6Rosenstiel School of Marine and Atmospheric Sciences, University of Miami 7John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine *Corresponding author: E-mail: [email protected]. Accepted: November 30, 2017 Data deposition: DNA sequence GenBank accession information is available in supplementary table S2, Supplementary Material online. Parsed Illumina reads for the RAD-Seq data sets are already available through NCBI short read archive (SRA PRJEB11624). Abstract Local adaptation frequently occurs across populations as a result of migration-selection balance between divergent selective pressures and gene flow associated with life in heterogeneous landscapes. Studying the effects of selection and gene flow on the adaptation process can be achieved in systems that have recently colonized extreme environments. This study utilizes an endemic South American duck species, the speckled teal (Anas flavirostris), which has both high- and low-altitude populations. -
Gene Ontology Analysis with Cytoscape
Introduction to Systems Biology Exercise 6: Gene Ontology Analysis Overview: Gene Ontology (GO) is a useful resource in bioinformatics and systems biology. GO defines a controlled vocabulary of terms in biological process, molecular function, and cellular location, and relates the terms in a somewhat-organized fashion. This exercise outlines the resources available under Cytoscape to perform analyses for the enrichment of gene annotation (GO terms) in networks or sub-networks. In this exercise you will: • Learn how to navigate the Cytoscape Gene Ontology wizard to apply GO annotations to Cytoscape nodes • Learn how to look for enriched GO categories using the BiNGO plugin. What you will need: • The BiNGO plugin, developed by the Computational Biology Division, Dept. of Plant Systems Biology, Flanders Interuniversitary Institute for Biotechnology (VIB), described in Maere S, et al. Bioinformatics. 2005 Aug 15;21(16):3448-9. Epub 2005 Jun 21. • galFiltered.sif used in the earlier exercises and found under sampleData. Please download any missing files from http://www.cbs.dtu.dk/courses/27041/exercises/Ex3/ Download and install the BiNGO plugin, as follows: 1. Get BiNGO from the course page (see above) or go to the BiNGO page at http://www.psb.ugent.be/cbd/papers/BiNGO/. (This site also provides documentation on BiNGO). 2. Unzip the contents of BiNGO.zip to your Cytoscape plugins directory (make sure the BiNGO.jar file is in the plugins directory and not in a subdirectory). 3. If you are currently running Cytoscape, exit and restart. First, we will load the Gene Ontology data into Cytoscape. 1. Start Cytoscape, under Edit, Preferences, make sure your default species, “defaultSpeciesName”, is set to “Saccharomyces cerevisiae”. -
Mmnet: Metagenomic Analysis of Microbiome Metabolic Network
mmnet: Metagenomic analysis of microbiome metabolic network Yang Cao, Fei Li, Xiaochen Bo May 15, 2016 Contents 1 Introduction 1 1.1 Installation . .2 2 Analysis Pipeline: from raw Metagenomic Sequence Data to Metabolic Network Analysis 2 2.1 Prepare metagenomic sequence data . .3 2.2 Annotation of Metagenomic Sequence Reads . .3 2.3 Estimating the abundance of enzymatic genes . .5 2.4 Building reference metabolic dataset . .7 2.5 Constructing State Specific Network . .8 2.6 Topological network analysis . .9 2.7 Differential network analysis . 10 2.8 Network Visualization . 10 3 Analysis in Cytoscape 11 1 Introduction This manual is a brief introduction to structure, functions and usage of mmnet pack- age. The mmnet package provides a set of functions to support systems analysis of metagenomic data in R, including annotation of raw metagenomic sequence data, con- struction of metabolic network, and differential network analysis based on state specific metabolic network and enzymatic gene abundance. Meanwhile, the package supports an analysis pipeline for metagenomic systems bi- ology. We can simply start from raw metagenomic sequence data, optionally use MG- RAST to rapidly retrieve metagenomic annotation, fetch pathway data from KEGG using its API to construct metabolic network, and then utilize a metagenomic systems biology computational framework mentioned in [Greenblum et al., 2012] to establish further differential network analysis. The main features of mmnet: • Annotation of metagenomic sequence reads with MG-RAST • Estimating abundances of enzymatic gene based on functional annotation 1 • Constructing State Specific metabolic Network • Topological network analysis • Differential network analysis 1.1 Installation mmnet requires these packages: KEGGREST, igraph, Biobase, XML, RCurl, RJSO- NIO, stringr, ggplot2 and biom. -
The Role of Cyclin B3 in Mammalian Meiosis
THE ROLE OF CYCLIN B3 IN MAMMALIAN MEIOSIS by Mehmet Erman Karasu A Dissertation Presented to the Faculty of the Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy New York, NY November, 2018 Scott Keeney, PhD Date Dissertation Mentor Copyright © Mehmet Erman Karasu 2018 DEDICATION I would like to dedicate this thesis to my parents, Mukaddes and Mustafa Karasu. I have been so lucky to have their support and unconditional love in this life. ii ABSTRACT Cyclins and cyclin dependent kinases (CDKs) lie at the center of the regulation of the cell cycle. Cyclins as regulatory partners of CDKs control the switch-like cell cycle transitions that orchestrate orderly duplication and segregation of genomes. Similar to somatic cell division, temporal regulation of cyclin-CDK activity is also important in meiosis, which is the specialized cell division that generates gametes for sexual production by halving the genome. Meiosis does so by carrying out one round of DNA replication followed by two successive divisions without another intervening phase of DNA replication. In budding yeast, cyclin-CDK activity has been shown to have a crucial role in meiotic events such as formation of meiotic double-strand breaks that initiate homologous recombination. Mammalian cells express numerous cyclins and CDKs, but how these proteins control meiosis remains poorly understood. Cyclin B3 was previously identified as germ cell specific, and its restricted expression pattern at the beginning of meiosis made it an interesting candidate to regulate meiotic events. -
PMAP: Databases for Analyzing Proteolytic Events and Pathways Yoshinobu Igarashi1, Emily Heureux1, Kutbuddin S
Published online 8 October 2008 Nucleic Acids Research, 2009, Vol. 37, Database issue D611–D618 doi:10.1093/nar/gkn683 PMAP: databases for analyzing proteolytic events and pathways Yoshinobu Igarashi1, Emily Heureux1, Kutbuddin S. Doctor1, Priti Talwar1, Svetlana Gramatikova1, Kosi Gramatikoff1, Ying Zhang1, Michael Blinov3, Salmaz S. Ibragimova2, Sarah Boyd4, Boris Ratnikov1, Piotr Cieplak1, Adam Godzik1, Jeffrey W. Smith1, Andrei L. Osterman1 and Alexey M. Eroshkin1,* 1The Center on Proteolytic Pathways, The Cancer Research Center and The Inflammatory and Infectious Disease Center at The Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, 2Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Lavrentieva 10, Novosibirsk 630090, Russia, 3Center of Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA and 4Faculty of Information Technology, Monash University, Clayton, Victoria 3800, Australia Received August 15, 2008; Revised September 19, 2008; Accepted September 23, 2008 ABSTRACT Proteolysis is essential to almost all fundamental cellular processes including proliferation, death and migration The Proteolysis MAP (PMAP, http://www. (1–4). Equally as important, mis-regulated proteolysis proteolysis.org) is a user-friendly website intended can cause diseases ranging from emphysema (5) and to aid the scientific community in reasoning about thrombosis (6), to arthritis (7) and Alzheimer’s (8). There proteolytic networks and pathways. PMAP is com- are a number of online resources containing information prised of five databases, linked together in one on proteases including SwissProt (the oldest), HPRD environment. The foundation databases, Protease- (human protein reference database) (9) and UniProt (10). -
How to Use and Integrate.Pdf
Journal of Proteomics 171 (2018) 37–52 Contents lists available at ScienceDirect Journal of Proteomics journal homepage: www.elsevier.com/locate/jprot How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study Fábio Trindade a,b,⁎, Rita Ferreira c, Beatriz Magalhães a, Adelino Leite-Moreira b, Inês Falcão-Pires b,RuiVitorinoa,b a Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal b Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal c QOPNA, Mass Spectrometry Center, Department of Chemistry, University of Aveiro, Aveiro, Portugal article info abstract Article history: Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large Received 22 December 2016 amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, Received in revised form 28 February 2017 we used a specific clinical problem – to discriminate pathophysiology and potential biomarkers between two Accepted 19 March 2017 similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) – to make a Available online 21 March 2017 step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with Keywords: fi fi Proteomics AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could nd speci cbio- Bioinformatics logical phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such STRING as up-regulation of plasminogen activation. -
Integrated Analysis of the Critical Region 5P15.3–P15.2 Associated with Cri-Du-Chat Syndrome
Genetics and Molecular Biology, 42, 1(suppl), 186-196 (2019) Copyright © 2019, Sociedade Brasileira de Genética. Printed in Brazil DOI: http://dx.doi.org/10.1590/1678-4685-GMB-2018-0173 Research Article Integrated analysis of the critical region 5p15.3–p15.2 associated with cri-du-chat syndrome Thiago Corrêa1, Bruno César Feltes2 and Mariluce Riegel1,3* 1Post-Graduate Program in Genetics and Molecular Biology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. 2Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. 3Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil. Abstract Cri-du-chat syndrome (CdCs) is one of the most common contiguous gene syndromes, with an incidence of 1:15,000 to 1:50,000 live births. To better understand the etiology of CdCs at the molecular level, we investigated theprotein–protein interaction (PPI) network within the critical chromosomal region 5p15.3–p15.2 associated with CdCs using systemsbiology. Data were extracted from cytogenomic findings from patients with CdCs. Based on clin- ical findings, molecular characterization of chromosomal rearrangements, and systems biology data, we explored possible genotype–phenotype correlations involving biological processes connected with CdCs candidate genes. We identified biological processes involving genes previously found to be associated with CdCs, such as TERT, SLC6A3, and CTDNND2, as well as novel candidate proteins with potential contributions to CdCs phenotypes, in- cluding CCT5, TPPP, MED10, ADCY2, MTRR, CEP72, NDUFS6, and MRPL36. Although further functional analy- ses of these proteins are required, we identified candidate proteins for the development of new multi-target genetic editing tools to study CdCs. -
A Roadmap for Metagenomic Enzyme Discovery
Natural Product Reports View Article Online REVIEW View Journal A roadmap for metagenomic enzyme discovery Cite this: DOI: 10.1039/d1np00006c Serina L. Robinson, * Jorn¨ Piel and Shinichi Sunagawa Covering: up to 2021 Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, Creative Commons Attribution-NonCommercial 3.0 Unported Licence. shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a ‘needle in a haystack’ without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, This article is licensed under a genomic context, 3D structure-based approaches, and machine learning techniques. -
Some Considerations for Analyzing Biodiversity Using Integrative
Some considerations for analyzing biodiversity using integrative metagenomics and gene networks Lucie Bittner, Sébastien Halary, Claude Payri, Corinne Cruaud, Bruno de Reviers, Philippe Lopez, Eric Bapteste To cite this version: Lucie Bittner, Sébastien Halary, Claude Payri, Corinne Cruaud, Bruno de Reviers, et al.. Some considerations for analyzing biodiversity using integrative metagenomics and gene networks. Biology Direct, BioMed Central, 2010, 5 (5), pp.47. 10.1186/1745-6150-5-47. hal-02922363 HAL Id: hal-02922363 https://hal.archives-ouvertes.fr/hal-02922363 Submitted on 26 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. Bittner et al. Biology Direct 2010, 5:47 http://www.biology-direct.com/content/5/1/47 HYPOTHESIS Open Access Some considerations for analyzing biodiversity using integrative metagenomics and gene networks Lucie Bittner1†, Sébastien Halary2†, Claude Payri3, Corinne Cruaud4, Bruno de Reviers1, Philippe Lopez2, Eric Bapteste2* Abstract Background: Improving knowledge of biodiversity will benefit conservation biology, enhance bioremediation studies, and could lead to new medical treatments. However there is no standard approach to estimate and to compare the diversity of different environments, or to study its past, and possibly, future evolution. -
Node and Edge Attributes
Cytoscape User Manual Table of Contents Cytoscape User Manual ........................................................................................................ 3 Introduction ...................................................................................................................... 48 Development .............................................................................................................. 4 License ...................................................................................................................... 4 What’s New in 2.7 ....................................................................................................... 4 Please Cite Cytoscape! ................................................................................................. 7 Launching Cytoscape ........................................................................................................... 8 System requirements .................................................................................................... 8 Getting Started .......................................................................................................... 56 Quick Tour of Cytoscape ..................................................................................................... 12 The Menus ............................................................................................................... 15 Network Management ................................................................................................. 18 The -
Identification and Characterisation of Spontaneous Mutations Causing Deafness from a Targeted Knockout Programme
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.30.450312; this version posted June 30, 2021. 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. Collateral damage: Identification and characterisation of spontaneous mutations causing deafness from a targeted knockout programme Morag A. Lewis1,2*, Neil J. Ingham1,2 , Jing Chen1,2, Selina Pearson2, Francesca Di Domenico1, Sohinder Rekhi1, Rochelle Allen1, Matthew Drake1, Annelore Willaert3, Victoria Rook1, Johanna Pass1,2, Thomas Keane2, David Adams2, Abigail S. Tucker4, Jacqueline K. White2, Karen P. Steel1,2 1. Wolfson Centre for Age-Related Diseases, King’s College London, London SE1 1UL 2. Wellcome Sanger Institute, Hinxton, CB10 1SA 3. Research Group of Experimental Oto-rhino-laryngology, Department of Neurosciences, KU Leuven – University of Leuven, Leuven, Belgium 4. Centre for Craniofacial and Regenerative Biology, King’s College London, London SE1 9RT* Corresponding author: [email protected] Acknowledgements We are grateful to Cassandra Whelan for assistance with the MYO7A staining on the Atp2b2Tkh and Tbx1ttch mutants, Seham Ebrahim for additional analysis on the Atp2b2Tkh mutants, Elysia James for protein modelling of KLHL18, Maria Lachgar-Ruiz for assistance with genotyping, Samoela Rexhaj for help with the mapping of the rhme mutation, Hannah Thompson for initial analysis of the Tbx1ttch mutants, Zahra Hance for her work on the vthr mutant, Rosalind Lacey and James Bussell for assistance with mouse colony management, and the Mouse Genetics Project for initial phenotyping of all these mutants. -
Algorithms to Integrate Omics Data for Personalized Medicine
ALGORITHMS TO INTEGRATE OMICS DATA FOR PERSONALIZED MEDICINE by MARZIEH AYATI Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Thesis Adviser: Mehmet Koyut¨urk Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY August, 2018 Algorithms to Integrate Omics Data for Personalized Medicine Case Western Reserve University Case School of Graduate Studies We hereby approve the thesis1 of MARZIEH AYATI for the degree of Doctor of Philosophy Mehmet Koyut¨urk 03/27/2018 Committee Chair, Adviser Date Department of Electrical Engineering and Computer Science Mark R. Chance 03/27/2018 Committee Member Date Center of Proteomics Soumya Ray 03/27/2018 Committee Member Date Department of Electrical Engineering and Computer Science Vincenzo Liberatore 03/27/2018 Committee Member Date Department of Electrical Engineering and Computer Science 1We certify that written approval has been obtained for any proprietary material contained therein. To the greatest family who I owe my life to Table of Contents List of Tables vi List of Figures viii Acknowledgements xxi Abstract xxiii Abstract xxiii Chapter 1. Introduction1 Chapter 2. Preliminaries6 Complex Diseases6 Protein-Protein Interaction Network6 Genome-Wide Association Studies7 Phosphorylation 10 Biweight midcorrelation 10 Chapter 3. Identification of Disease-Associated Protein Subnetworks 12 Introduction and Background 12 Methods 15 Results and Discussion 27 Conclusion 40 Chapter 4. Population Covering Locus Sets for Risk Assessment in Complex Diseases 43 Introduction and Background 43 iv Methods 47 Results and Discussion 59 Conclusion 75 Chapter 5. Application of Phosphorylation in Precision Medicine 80 Introduction and Background 80 Methods 83 Results 89 Conclusion 107 Chapter 6.