Some Considerations for Analyzing Biodiversity Using Integrative
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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. -
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. -
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. -
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 -
Snps Reveal Geographical Population Structure of Corallina Officinalis (Corallinaceae, Rhodophyta)
SNPs reveal geographical population structure of Corallina officinalis (Corallinaceae, Rhodophyta) Chris Yesson1, Amy Jackson2, Steve Russell2, Christopher J. Williamson2,3 and Juliet Brodie2 1 Institute of Zoology, Zoological Society of London, London, UK 2 Natural History Museum, Department of Life Sciences, London, UK 3 Schools of Biological and Geographical Sciences, University of Bristol, Bristol, UK CONTACT: Chris Yesson. Email: [email protected] 1 Abstract We present the first population genetics study of the calcifying coralline alga and ecosystem engineer Corallina officinalis. Eleven novel SNP markers were developed and tested using Kompetitive Allele Specific PCR (KASP) genotyping to assess the population structure based on five sites around the NE Atlantic (Iceland, three UK sites and Spain), spanning a wide latitudinal range of the species’ distribution. We examined population genetic patterns over the region using discriminate analysis of principal components (DAPC). All populations showed significant genetic differentiation, with a marginally insignificant pattern of isolation by distance (IBD) identified. The Icelandic population was most isolated, but still had genotypes in common with the population in Spain. The SNP markers presented here provide useful tools to assess the population connectivity of C. officinalis. This study is amongst the first to use SNPs on macroalgae and represents a significant step towards understanding the population structure of a widespread, habitat forming coralline alga in the NE Atlantic. KEYWORDS Marine red alga; Population genetics; Calcifying macroalga; Corallinales; SNPs; Corallina 2 Introduction Corallina officinalis is a calcified geniculate (i.e. articulated) coralline alga that is wide- spread on rocky shores in the North Atlantic (Guiry & Guiry, 2017; Brodie et al., 2013; Williamson et al., 2016). -
Southern California Tidepool Organisms
Southern California Tidepool Organisms Bryozoans – colonial moss animals Cnidarians – stinging invertebrates Derby Hat Bryozoan Red Bryozoan Aggregating Anemone Giant Green Anemone Sunburst Anemone Eurystomella spp. Watersipora spp. Anthopleura elegantissima Anthopleura xanthogrammica Anthopleura sola closed closed closed open 2 in (5 cm) open 6.7 in (17 cm) open 6.5 in (12cm) Echinoderms – spiny-skinned invertebrates Sea Stars note signs of wasting Bat Star Brittle Star Ochre Star Giant Pink Sea Star Six Armed Sea Star Sunflower Star Patiria miniata (various genuses) Pisaster ochraceus Pisaster brevispinus Leptasterias spp. Pycnopodia helianthoides Purple or Red webbed arms 10 in 11 in 31.5 in Various sizes 4.7 in (12 cm) Long, thin arms (25 cm) (28 cm) 6 arms, 2.4 in(6 cm) (80 cm) Sand Dollar Sea Cucumbers Urchins note signs of balding Eccentric Sand Dollar California Sea Cucumber Warty Sea Cucumber Purple Urchins Red Urchins Dendraster excentricus Parastichopus californicus Parastichopus parvimensis Strongylocentrotus Strongylocentrotus purpuratus franciscanus has small 4 in 7in black tipped warts (10 cm) (17 cm) 4 in (10 cm) 16 in (40 cm) 10 in (25 cm) long (projections) Mollusks – soft invertebrates with a shell or remnant shell Snails (single, spiraled shelled invertebrate) Turban Snail Periwinkle Snail Kellet’s Whelk Snail Dog Whelk Snail Unicorn Whelk Snail Scaly Tube Snail Tegula spp. Littorina spp. Kelletia kelletii (Dogwinkles) Acanthinucella spp. Serpulorbis squamigerus Nucella spp. Top view 6 ½ in 2 in 1.6 in (16.5 cm) (5 cm) 1 in (2.5 cm) ½ in (1.5 cm) (4cm) 5 in (13 cm) Bi-Valves (2 shelled invertebrates) Abalone California Mussel Blue Mussel Olympia Oyster Pacific Oyster Rock Scallop Haliotis spp. -
A Computational Approach for Identifying the Impact Of
ering & B ne io Harishchander, J Bioengineer & Biomedical Sci 2017, 7:3 gi m n e e d io i c DOI: 10.4172/2155-9538.1000235 B a f l S Journal of o l c a i e n n r c u e o J ISSN: 2155-9538 Bioengineering & Biomedical Science Short Communication Open Access A Computational Approach for Identifying the Impact of Pharmacogenomics in Regulatory Networks to Understand Disease Pathology Harishchander A* Department of Bioinformatics, Sathyabama University, Chennai, Tamil Nadu, India Abstract In the current era of post genomics, there exist a varity of computational methodologies to understand the nature of disease pathology like RegNetworks, DisgiNet, pharmGkb, pharmacomiR and etc. In most of these computational methodologies either a seed pairing approach or a base pair complement is being followed. Hence there exists a gap in understanding the nature of disease pathology in a holistic view point. In this manuscript we combine the approach of seed pairing and graph theory to illustrate the impact of Pharmacogenomics in Regulatory Networks to understand disease Pathology. Keywords: Pharmacogenomics; Macromolecules; Phenotypes potentials [16,17]. This information can still be used to predict the new regulatory relationships between TFs and genes by matching Introduction the binding motifs in DNA sequences. Hence, these predictions were Events in gene regulation play vital roles in a various developmental based on TFBS to integrate into PharmacoReg to provide a more and physiological processes in a cell, in which macromolecules such as comprehensive landscape in gene regulation. Moreover, to include the RNAs, genes and proteins to coordinate and organize responses under post- transcriptional regulatory relationship, consideration of miRNAs ∼ various conditions [1]. -
Genome-Wide Analysis Identifies 12 Loci Influencing Human Reproductive Behavior
Genome-wide analysis identifies 12 loci influencing human reproductive behavior Correspondence to: Melinda C. Mills ([email protected]), Nicola Barban ([email protected]), Harold Snieder ([email protected]) or Marcel den Hoed ([email protected]) Authors: Nicola Barban1,*, Rick Jansen2,#, Ronald de Vlaming3,4,5,#, Ahmad Vaez6,#, Jornt J. Mandemakers7,#, Felix C. Tropf1,#, Xia Shen8,9,10,#, James F. Wilson10,9,#, Daniel I. Chasman11,#, Ilja M. Nolte6,#, Vinicius Tragante,#12, Sander W. van der Laan13,#, John R. B. Perry14,#, Augustine Kong32,15 ,#, BIOS Consortium, Tarunveer Ahluwalia16,17,18, Eva Albrecht19, Laura Yerges-Armstrong20, Gil Atzmon21,22, Kirsi Auro23,24 , Kristin Ayers25, Andrew Bakshi26, Danny Ben-Avraham27, Klaus Berger28, Aviv Bergman29, Lars Bertram30, Lawrence F. Bielak31, Gyda Bjornsdottir32, Marc Jan Bonder33, Linda Broer34, Minh Bui35, Caterina Barbieri36, Alana Cavadino37,38, Jorge E Chavarro39,40,41, Constance Turman41, Maria Pina Concas42, Heather J. Cordell25, Gail Davies43,44, Peter Eibich45, Nicholas Eriksson46, Tõnu Esko47,48, Joel Eriksson49, Fahimeh Falahi6, Janine F. Felix50,4,51, Mark Alan Fontana52, Lude Franke33, Ilaria Gandin53, Audrey J. Gaskins39, Christian Gieger54,55, Erica P. Gunderson56, Xiuqing Guo57, Caroline Hayward9, Chunyan He58, Edith Hofer59,60, Hongyan Huang41, Peter K. Joshi10, Stavroula Kanoni61, Robert Karlsson8, Stefan Kiechl62, Annette Kifley63, Alexander Kluttig64, Peter Kraft 41,65, Vasiliki Lagou66,67,68,Cecile Lecoeur,99 Jari Lahti69,70,71, Ruifang Li-Gao72, Penelope A. Lind73, Tian Liu74, Enes Makalic35, Crysovalanto Mamasoula25, Lindsay Matteson75, Hamdi Mbarek76,77, Patrick F. McArdle20, George McMahon78, S. Fleur W. Meddens79, 5, Evelin Mihailov47, Mike Miller80, Stacey A. Missmer81,41, Claire Monnereau50,4,51 , Peter J. -
Corallinales, Rhodophyta) Based on Molecular and Morphological Data: a Reappraisal of Jania1
J. Phycol. 43, 1310–1319 (2007) Ó 2007 Phycological Society of America DOI: 10.1111/j.1529-8817.2007.00410.x PHYLOGENETIC RELATIONSHIPS WITHIN THE TRIBE JANIEAE (CORALLINALES, RHODOPHYTA) BASED ON MOLECULAR AND MORPHOLOGICAL DATA: A REAPPRAISAL OF JANIA1 Ji Hee Kim Korea Polar Research Institute, KORDI, 7-50 Songdo-dong, Incheon 408-840, Korea Michael D. Guiry Martin Ryan Institute, The National University of Ireland, Galway, Ireland Jung Hyun Oak Department of Biology, Gyeongsang National University, Jinju 660-701, Korea Do-Sung Choi Department of Science Education, Gwangju National University of Education, Gwangju 500-703, Korea Sung-Ho Kang, Hosung Chung Korea Polar Research Institute, KORDI, 7-50 Songdo-dong, Incheon 408-840, Korea and Han-Gu Choi2 Korea Polar Research Institute, KORDI, 7-50 Songdo-dong, Incheon 408-840, Korea Institute of Basic Sciences, Kongju National University, Kongju 314-701, Korea Generic boundaries among the genera Cheilosporum, Key index words: Corallinales; Jania; Janieae; Haliptilon,andJania—currently referred to the tribe morphology; nuclear SSU rDNA; phylogeny; Janieae (Corallinaceae, Corallinales, Rhodophyta)— Rhodophyta; systematics were reassessed. Phylogenetic relationships among Abbreviations: bp, base pair; GTR, general time 42 corallinoidean taxa were determined based on 26 reversible; TBR, tree bisection reconnection anatomical characters and nuclear SSU rDNA sequence data for 11 species (with two duplicate plants) referred to the tribe Corallineae and 15 species referred to the tribe Janieae (two species All members of the subfamily Corallinoideae of Cheilosporum, seven of Haliptilon, and six of (Aresch.) Foslie are constructed of uncalcified geni- Jania, with five duplicate plants). Results from our cula and calcified intergenicula and form branched approach were consistent with the hypothesis that fronds. -
Network Generation and Analysis Through Cytoscape and PSICQUIC
(v6, 6/6/13) Network generation and analysis through Cytoscape and PSICQUIC Author: Pablo Porras Millán IntAct Scientific Database Curator This work is licensed under the Creative Commons Attribution-Share Alike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA. Contents Summary ........................................................................................................................................................ 3 Objectives ....................................................................................................................................................... 3 Software requirements ............................................................................................................................... 3 Introduction to Cytoscape ........................................................................................................................ 3 Tutorial ........................................................................................................................................................... 4 Dataset description ................................................................................................................................. 4 Representing an interaction network using Cytoscape ............................................................... 8 Filtering with edge and node attributes ..........................................................................................