A combinatorial test for significant codivergence between cool-season grasses and their symbiotic fungal endophytes
|
|
|
- Allen Sherman
- 10 years ago
- Views:
Transcription
1 A combinatorial test for significant codivergence between cool-season grasses and their symbiotic fungal endophytes Ruriko Yoshida Dept. of Statistics University of Kentucky Joint work with C.L. Schardl, K.D. Craven, A. Lindstrom, A. Stromberg ruriko IMA 1
2 Endophytes species IMA 2
3 Life cycles of sexual ( (Epichloë spp.) endophytes asexual cycle & vertical transmission sexual cycle & horizontal transmission
4 Epichloë/Neotyphodium /Neotyphodium in a grass plant Symbioses are: Systemic Constitutive Often heritable Often mutualistic
5 Endophytes: Constitutive, heritable symbionts H. Koya, pers.. comm. Freeman 1904
6 Endophyte effects on host fitness Tall fescue without endophyte Tall fescue w/ Neotyphodium coenophialum
7 Fruiting structures of Epichloë species
8 Grass-endophyte mutualisms nutrition shelter dispersal H O O H N H H anti-insect insect OH O O anti-vertebrate anti-nematode nematode drought tolerance etc. O O O H O O N NR 2 H N N OH O O N HN O H 3 C N N H NH 2 NH HN N
9 Transmission strategies Relative importance of vertical vs horizontal transmission. A. Exclusively or almost exclusively transmitted horizontally. The endophyte will tend to shut down host seed production ( choke disease ), diverting available plant resources to production of infectious spores. Those spores spread to developing seeds of neighboring plants. B. Exclusive vertical transmission. The host exhibits no disease symptoms due to the endophyte infection. Its seeds develop and germinate normally, but bear the endophyte and thereby transmit it to the next generation. C. Mixed vertical and horizontal transmission strategy. IMA 3
10 Host range 1. Some endophytes are restricted to individual host species. This seems rare for endophyte categories A and C above, but typical of category B. 2. Some are restricted to individual genera. 3. Some are restricted to host tribes. 4. Some are associated mainly with one host tribe, but occasionally can be identified in the sister tribe. 5. Some are present in a phylogenetically broad range of host tribes. IMA 4
11 Problem Question. We would like to analyze how grasses and their endophytes evolved together? Method. 1. We use phylogenetic trees among grass species and among endophytes species. 2. Compute pairwise distances in the grass tree and in the endophyte tree. 3. Compute MRCA pairs of two trees. 4. Estimate the probability of codivergence between two trees and compute their correlations. IMA 5
12 Phylogenetic trees Data. Sequencing of Chloroplast DNA (cpdna) Non-Coding Regions. 27 species in each group. Sequences were entered into GenBank as accession numbers AY AY and xxxxx-xxxxx. Based on published phylogenetic inference for the grass subfamily Poöideae (Soreng and Davis 1998), Brachyelytrum erectum was chosen as the outgroup for reconstructing the grass phylogenies. The corresponding endophyte, Epichloë brachyelytri, was the outgroup chosen for endophyte phylogenies. We reconstruct phylogenetic trees of grasses (the host tree, T H ) and phylogenetic trees of endophytes (the parasite tree, T P ) via a software PAUP* under the GTR+G+I model. Then we used a software r8s to make trees unltrametric (using the least square method). IMA 6
13 100 A. tenuis A. hiemalis 74 C. villosa Ech. ovatus K. cristata 100 S. obtusata Agrostideae 100 L. edwardii 97 L. multiflorum 96 L. perenne L. arundinaceum 99 Lolium sp. P4074 Lolium sp. P4078 Poeae F. rubra F. longifolia Hol. mollis Bro. erectus Bro. purgans Bro. ramosus He. europaeus El. canadensis H. brevisubulatum Bromeae Hordeeae Brp. sylvaticum Brachypodieae Ach. inebrians Stipeae G. striata Meliceae substitutions/site Bre. erectum Brachyelytreae Figure 1: Parametric ML tree estimated from cpdna intron and intergenic sequences. Numbers above branches indicate bootstrap support percentages (over 50%) obtained by 1000 maximum parsimony searches with branch swapping. IMA 7
14 Hosts Bre.erectum Brp.sylvaticum Ech.ovatus C.villosa A.tenuis A.hiemalis S.obtusata K.cristata Lolium sp.p4078 Lolium sp.p4074 L.arundinaceum L.multiflorum L.edwardii L.perenne L.perenne F.r.commutata F.longifolia Hol.mollis He.europaeus Bro.ramosus Bro.erectus Bro.purgans H.brevisubulatum El.canadensis G.striata Ach.inebrians 10 E.brachyelytri E.sylvatica E.typhina N.t.canariense N.aotearoae E.baconii E.amarillans E.amarillans E.baconii N.sp.FATG2 N.sp.FATG3 N.coenophialum N.occultans E.festucae E.festucae E.festucae N.lolii Epichloë sp. N.sp.HeTG1 Bro.ramosus E.bromicola N.sp.HbTG1 E.elymi E.elymi E.glyceriae N.gansuense Endophytes Figure 2: Ultrametric ML time trees for host grasses and their endophytes. Hosts and their endophytes are indicated opposite each other or by connecting dashed lines. Full taxon names are given in Table 1 in our paper. IMA 8
15 MRCA pairs A MRCA pair is a pair of a Most Recent Common Ancestor (MRCA) of any pair of host species and a Most Recent Common Ancestor (MRCA) of any pair of parasite species. IMA 9
16 MRCA pairs Congruent trees H 7 P 7' 5 6 5' 6' ' 2' 3' 4' MRCA pair (5,5') (6,6') (7,7') Pairs of H and P taxon pairs ((1,2),(1',2')) ((3,4),(3',4')) ((1,3),(1',3')), ((1,4),(1',4')), ((2,3),(2',3')), ((2,4),(2',4')) Incongruent trees H 7 P 7' 6' 5 6 5' ' 2' 3' 4' MRCA pair (5,5') (7,6') (7,7') (6,7') Pairs of H and P taxon pairs ((1,2),(1',2')) ((1,3),(1',3')), ((2,3),(2',3')) ((1,4),(1',4')), ((2,4),(2',4')) ((3,4),(3',4')) IMA 10
17 Analysis on codivergence [Legendre et al 2002] etc used all possible pairs of pairwise distances from the host tree and the parasite tree and used Principal Components Analysis (PCA) to compute their correlations. A problem of their method is that we possibly pick the same Most Recent Common Ancestor (MRCA) pair multiple times. This causes a bias in the result. In each tip clade a MRCA uniquely relates two taxa. However, a MRCA deeper in the tree relates multiple taxon pairs. For example, for congruent H and P trees the matrix of all pairwise distances of H taxon pairs against all pairwise distances of P taxon pairs represents each corresponding pair of tip clade MRCAs only once, and each corresponding pair of deeper MRCAs multiple times. The MRCALink algorithm samples corresponding H and P MRCA pairs only once. IMA 11
18 MRCALink algorithm We will go though the algorithm with an example. H 7 P 7' 6' 5 6 5' ' 2' 3' 4' IMA 12
19 Step 1: Assign each node a unique number such that its number is bigger than its children. Step 2: for each interior node in H, from all possible pairs of offsprings, find corresponding pairs in P. 5: From 5 = (1,2), we find a new MRCA 5 = (1,2 ) in P. 6: From 6 = (3,4) we find a new MRCA 7 = (3,4 ). 7: From 7 = (1,3) = (2,3) = (2, 4), we find new MRCAs 6 = (1,3 ) and 7 = (1,4 ). Thus, we have pairs (5,5 ), (6,7 ), (7,7 ), (7,6 ). IMA 13
20 Computing the probability of codivergence Let τ H be the set of all ultrametric host trees with n taxa and let τ P be the set of all ultrametric parasite trees with n taxa. S(X, Y, T, t) = x X,y Y time(mrca(x)) time(mrca(y)), where T τ H, t τ P, X is a set of pairs of taxa in H, and Y is a set of pairs of taxa in P. Then we estimate the probability P(S(X, Y, T H, T P ) S(X, Y,T, t) : T τ H, t τ P ) which is the estimated probability of codivergence for T H and T P, by randomly generated trees from τ H and τ P. IMA 14
21 Results We analyzed 4 pairs of host trees and parasite trees, namely the full tree and T 1 T 4 by removing some of species in the full trees, trimmed trees (T 1 T 4 ). For each pair of trimmed trees, we removed some species from the endophytes and corresponding grasses because these endophytes seem to have holizontal or mixed transmission. Plots of MRCA ages of hosts and their corresponding endophytes identified by the MRCALink algorithm from ultrametric ML trees for the full dataset or trimmed datasets T 1 T 4. Root ages were set to 100 arbitrary units for both trees. The diagonal lines represent expectation for perfect cospeciation and perfect phylogenetic reconstruction. IMA 15
22 100 Full Rel. Symbiont MRCA Ages 50 0 p = Rel. Host MRCA Ages 100 T1 100 T2 Rel. Symbiont MRCA Ages 50 0 p = Rel. Symbiont MRCA Ages 50 0 p = Rel. Host MRCA Ages Rel. Host MRCA Ages T3 T Rel. Symbiont MRCA Ages 50 p = Rel. Host MRCA Ages Rel. Symbiont MRCA Ages 50 p = IMA Rel. Host MRCA Ages
23 Questions [Huelsenbeck et al, 2000] showed an algorithm to estimate the MLE for host switching via MCMC. We would like to estimate the MLE of the probability of host switching in endophytes. All existing methods assume that the host and parasite trees are true trees. Can we reconstruct phylogenetic trees including host switching and also information of relations between hosts and parasites? How are the host tree and the parasite tree related in the tree space? More detailed analysis needs to be done. IMA 17
24 A preprint is available at arxiv: MRCALink will be available soon IMA 18
25 Thank you... IMA 19
Molecular Clocks and Tree Dating with r8s and BEAST
Integrative Biology 200B University of California, Berkeley Principals of Phylogenetics: Ecology and Evolution Spring 2011 Updated by Nick Matzke Molecular Clocks and Tree Dating with r8s and BEAST Today
Phylogenetic Trees Made Easy
Phylogenetic Trees Made Easy A How-To Manual Fourth Edition Barry G. Hall University of Rochester, Emeritus and Bellingham Research Institute Sinauer Associates, Inc. Publishers Sunderland, Massachusetts
Introduction to Phylogenetic Analysis
Subjects of this lecture Introduction to Phylogenetic nalysis Irit Orr 1 Introducing some of the terminology of phylogenetics. 2 Introducing some of the most commonly used methods for phylogenetic analysis.
Introduction to Bioinformatics AS 250.265 Laboratory Assignment 6
Introduction to Bioinformatics AS 250.265 Laboratory Assignment 6 In the last lab, you learned how to perform basic multiple sequence alignments. While useful in themselves for determining conserved residues
Bayesian Phylogeny and Measures of Branch Support
Bayesian Phylogeny and Measures of Branch Support Bayesian Statistics Imagine we have a bag containing 100 dice of which we know that 90 are fair and 10 are biased. The
Retail Lawn Seed Mixtures for Western Oregon and Western Washington
Retail Lawn Seed Mixtures for Western Oregon and Western Washington EM 9100 November 2014 Stan Baker, Alec Kowalewski, Brian McDonald, and Rob Golembiewski A number of new lawn seed products have become
A data management framework for the Fungal Tree of Life
Web Accessible Sequence Analysis for Biological Inference A data management framework for the Fungal Tree of Life Kauff F, Cox CJ, Lutzoni F. 2007. WASABI: An automated sequence processing system for multi-gene
Relationships of Floras (& Faunas)
Relationships of Floras (& Faunas) Knowledge of earth and organism histories now permit closer examination of relationships of disjunct floras and faunas. Southern Hemisphere temperate Southern Hemisphere
Visualization of Phylogenetic Trees and Metadata
Visualization of Phylogenetic Trees and Metadata November 27, 2015 Sample to Insight CLC bio, a QIAGEN Company Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone: +45 70 22 32 44 www.clcbio.com [email protected]
An experimental study comparing linguistic phylogenetic reconstruction methods *
An experimental study comparing linguistic phylogenetic reconstruction methods * François Barbançon, a Steven N. Evans, b Luay Nakhleh c, Don Ringe, d and Tandy Warnow, e, a Palantir Technologies, 100
Tree Integrated Pest Management. Dan Nortman Virginia Cooperative Extension, York County
Tree Integrated Pest Management Dan Nortman Virginia Cooperative Extension, York County IPM Refresher Definition: The use of a combination of appropriate pest control tactics to reduce pest population
Data Partitions and Complex Models in Bayesian Analysis: The Phylogeny of Gymnophthalmid Lizards
Syst. Biol. 53(3):448 469, 2004 Copyright c Society of Systematic Biologists ISSN: 1063-5157 print / 1076-836X online DOI: 10.1080/10635150490445797 Data Partitions and Complex Models in Bayesian Analysis:
Matter and Energy in Ecosystems
Matter and Energy in Ecosystems The interactions that take place among biotic and abiotic factors lead to transfers of energy and matter. Every species has a particular role, or niche, in an ecosystem.
Endophytes of perennial ryegrass and tall fescue
FEBRUARY 2007 PRIMEFACT 535 (REPLACES AGFACT P2.3.9) Endophytes of perennial ryegrass and tall fescue Harry Kemp Former District Agronomist Dr Chris Bourke Principal Research Scientist (Poisonous Plants),
NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
A comparison of methods for estimating the transition:transversion ratio from DNA sequences
Molecular Phylogenetics and Evolution 32 (2004) 495 503 MOLECULAR PHYLOGENETICS AND EVOLUTION www.elsevier.com/locate/ympev A comparison of methods for estimating the transition:transversion ratio from
Ecology Symbiotic Relationships
Ecology Symbiotic Relationships Overview of the Co-evolution and Relationships Exhibited Among Community Members What does Symbiosis mean? How do we define Symbiosis? Symbiosis in the broadest sense is
Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Name: Class: Date: Chapter 17 Practice Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The correct order for the levels of Linnaeus's classification system,
PROC. CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE 2006 1. E-mail: [email protected]
BIOINFTool: Bioinformatics and sequence data analysis in molecular biology using Matlab Mai S. Mabrouk 1, Marwa Hamdy 2, Marwa Mamdouh 2, Marwa Aboelfotoh 2,Yasser M. Kadah 2 1 Biomedical Engineering Department,
by Erik Lehnhoff, Walt Woolbaugh, and Lisa Rew
Designing the Perfect Plant Activities to Investigate Plant Ecology Plant ecology is an important subject that often receives little attention in middle school, as more time during science classes is devoted
Deterministic computer simulations were performed to evaluate the effect of maternallytransmitted
Supporting Information 3. Host-parasite simulations Deterministic computer simulations were performed to evaluate the effect of maternallytransmitted parasites on the evolution of sex. Briefly, the simulations
Name Class Date. binomial nomenclature. MAIN IDEA: Linnaeus developed the scientific naming system still used today.
Section 1: The Linnaean System of Classification 17.1 Reading Guide KEY CONCEPT Organisms can be classified based on physical similarities. VOCABULARY taxonomy taxon binomial nomenclature genus MAIN IDEA:
Algorithms in Computational Biology (236522) spring 2007 Lecture #1
Algorithms in Computational Biology (236522) spring 2007 Lecture #1 Lecturer: Shlomo Moran, Taub 639, tel 4363 Office hours: Tuesday 11:00-12:00/by appointment TA: Ilan Gronau, Taub 700, tel 4894 Office
Data for phylogenetic analysis
Data for phylogenetic analysis The data that are used to estimate the phylogeny of a set of tips are the characteristics of those tips. Therefore the success of phylogenetic inference depends in large
BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS
BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-110 012 [email protected] Genomics A genome is an organism s
BIOL 3200 Spring 2015 DNA Subway and RNA-Seq Data Analysis
BIOL 3200 Spring 2015 DNA Subway and RNA-Seq Data Analysis By the end of this lab students should be able to: Describe the uses for each line of the DNA subway program (Red/Yellow/Blue/Green) Describe
DNA Barcoding in Plants: Biodiversity Identification and Discovery
DNA Barcoding in Plants: Biodiversity Identification and Discovery University of Sao Paulo December 2009 W. John Kress Department of Botany National Museum of Natural History Smithsonian Institution New
Protein Sequence Analysis - Overview -
Protein Sequence Analysis - Overview - UDEL Workshop Raja Mazumder Research Associate Professor, Department of Biochemistry and Molecular Biology Georgetown University Medical Center Topics Why do protein
Borges, J. L. 1998. On exactitude in science. P. 325, In, Jorge Luis Borges, Collected Fictions (Trans. Hurley, H.) Penguin Books.
... In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those
Pairwise Sequence Alignment
Pairwise Sequence Alignment [email protected] SS 2013 Outline Pairwise sequence alignment global - Needleman Wunsch Gotoh algorithm local - Smith Waterman algorithm BLAST - heuristics What
Crawling and Detecting Community Structure in Online Social Networks using Local Information
Crawling and Detecting Community Structure in Online Social Networks using Local Information TU Delft - Network Architectures and Services (NAS) 1/12 Outline In order to find communities in a graph one
Fungi and plants practice
Name: Period: Date: Fungi and plants practice Multiple Choice Identify the choice that best completes the statement or answers the question. Indicate your answer choice with an UPPER CASE letter in the
Heredity - Patterns of Inheritance
Heredity - Patterns of Inheritance Genes and Alleles A. Genes 1. A sequence of nucleotides that codes for a special functional product a. Transfer RNA b. Enzyme c. Structural protein d. Pigments 2. Genes
AP Biology Essential Knowledge Student Diagnostic
AP Biology Essential Knowledge Student Diagnostic Background The Essential Knowledge statements provided in the AP Biology Curriculum Framework are scientific claims describing phenomenon occurring in
Scaling the gene duplication problem towards the Tree of Life: Accelerating the rspr heuristic search
Scaling the gene duplication problem towards the Tree of Life: Accelerating the rspr heuristic search André Wehe 1 and J. Gordon Burleigh 2 1 Department of Computer Science, Iowa State University, Ames,
KEY CONCEPT Organisms can be classified based on physical similarities. binomial nomenclature
Section 17.1: The Linnaean System of Classification Unit 9 Study Guide KEY CONCEPT Organisms can be classified based on physical similarities. VOCABULARY taxonomy taxon binomial nomenclature genus MAIN
Routing in packet-switching networks
Routing in packet-switching networks Circuit switching vs. Packet switching Most of WANs based on circuit or packet switching Circuit switching designed for voice Resources dedicated to a particular call
Data Mining Clustering (2) Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining
Data Mining Clustering (2) Toon Calders Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Outline Partitional Clustering Distance-based K-means, K-medoids,
Original article: COMPARISON OF FIVE CALLIGONUM SPECIES IN TARIM BASIN BASED ON MORPHOLOGICAL AND MOLECULAR DATA
Original article: COMPARISON OF FIVE CALLIGONUM SPECIES IN TARIM BASIN BASED ON MORPHOLOGICAL AND MOLECULAR DATA Maryamgul Abdurahman 1,2,3, Gulnur Sabirhazi* 1,3, Bin Liu 1,3, Linke Yin 1,3, Borong Pan
Bayesian coalescent inference of population size history
Bayesian coalescent inference of population size history Alexei Drummond University of Auckland Workshop on Population and Speciation Genomics, 2016 1st February 2016 1 / 39 BEAST tutorials Population
Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/2004 Hierarchical
PHYLOGENY AND EVOLUTION OF NEWCASTLE DISEASE VIRUS GENOTYPES
Eötvös Lóránd University Biology Doctorate School Classical and molecular genetics program Project leader: Dr. László Orosz, corresponding member of HAS PHYLOGENY AND EVOLUTION OF NEWCASTLE DISEASE VIRUS
Molecular identification of the turf grass rapid blight pathogen
Mycologia, 97(1), 2005, pp. 160 166. 2005 by The Mycological Society of America, Lawrence, KS 66044-8897 Molecular identification of the turf grass rapid blight pathogen K.D. Craven 1,2 Center for Integrated
Bio-Informatics Lectures. A Short Introduction
Bio-Informatics Lectures A Short Introduction The History of Bioinformatics Sanger Sequencing PCR in presence of fluorescent, chain-terminating dideoxynucleotides Massively Parallel Sequencing Massively
PLANT EVOLUTION DISPLAY Handout
PLANT EVOLUTION DISPLAY Handout Name: TA and Section time Welcome to UCSC Greenhouses. This sheet explains a few botanical facts about plant reproduction that will help you through the display and handout.
Cytospora Canker. A Hard Nut to Crack. My current ongoing projects 1/23/2013. 30% of Cherry trees
Cytospora Canker: A Hard Nut to Crack Ramesh Pokharel My research and extension program is aimed at > Solving practical fruit production problems > Increased producer profitability > Strengthening the
An Introduction to Phylogenetics
An Introduction to Phylogenetics Bret Larget [email protected] Departments of Botany and of Statistics University of Wisconsin Madison February 4, 2008 1 / 70 Phylogenetics and Darwin A phylogeny is
Turfgrass Selection for the Home Landscape
PUBLICATION 8035 Turfgrass Selection for the Home Landscape UNIVERSITY OF CALIFORNIA Agriculture and Natural Resources http://anrcatalog.ucdavis.edu M. ALI HARIVANDI, UC Cooperative Extension Farm Advisor,
CSE 326: Data Structures B-Trees and B+ Trees
Announcements (4//08) CSE 26: Data Structures B-Trees and B+ Trees Brian Curless Spring 2008 Midterm on Friday Special office hour: 4:-5: Thursday in Jaech Gallery (6 th floor of CSE building) This is
Clustering. Data Mining. Abraham Otero. Data Mining. Agenda
Clustering 1/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference 2/46 1 Introduction It seems logical that in a new situation we should act in a similar way as in
Biological Sciences Initiative. Human Genome
Biological Sciences Initiative HHMI Human Genome Introduction In 2000, researchers from around the world published a draft sequence of the entire genome. 20 labs from 6 countries worked on the sequence.
Using multiple models: Bagging, Boosting, Ensembles, Forests
Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or
Phylogenetic relationships among Staphylococcus species and refinement of cluster groups based on multilocus data
Lamers et al. BMC Evolutionary Biology 2012, 12:171 RESEARCH ARTICLE Open Access Phylogenetic relationships among Staphylococcus species and refinement of cluster groups based on multilocus data Ryan P
In-Network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection
In-Network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection Michele Albano Jie Gao Instituto de telecomunicacoes, Aveiro, Portugal Stony Brook University, Stony Brook, USA Data
Bird and bat droppings
Bird and bat droppings Introduction While the hazards of bird and bat droppings are generally exaggerated, there is some risk of disease wherever there are large populations of roosting birds or bats.
PHYLOGENETIC ANALYSIS
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D. Baxevanis, B.F. Francis Ouellette Copyright 2001 John Wiley & Sons, Inc. ISBNs: 0-471-38390-2 (Hardback);
Speciation in fig pollinators and parasites
Molecular Ecology (2002) 11, 1573 1578 Blackwell Science, Ltd SHORT COMMUNICATION Speciation in fig pollinators and parasites GEORGE D. WEIBLEN* and GUY L. BUSH *Department of Plant Biology, University
Question Bank Five Kingdom Classification
Question Bank Five Kingdom Classification 1. Who proposed Five Kingdom Classification? Give the bases of classification. Ans. Whittaker in 1969 proposed five kingdom classification based on :- (i) Cell
Practice Questions 1: Evolution
Practice Questions 1: Evolution 1. Which concept is best illustrated in the flowchart below? A. natural selection B. genetic manipulation C. dynamic equilibrium D. material cycles 2. The diagram below
Activity IT S ALL RELATIVES The Role of DNA Evidence in Forensic Investigations
Activity IT S ALL RELATIVES The Role of DNA Evidence in Forensic Investigations SCENARIO You have responded, as a result of a call from the police to the Coroner s Office, to the scene of the death of
DNA Sequence Alignment Analysis
Analysis of DNA sequence data p. 1 Analysis of DNA sequence data using MEGA and DNAsp. Analysis of two genes from the X and Y chromosomes of plant species from the genus Silene The first two computer classes
The Central Dogma of Molecular Biology
Vierstraete Andy (version 1.01) 1/02/2000 -Page 1 - The Central Dogma of Molecular Biology Figure 1 : The Central Dogma of molecular biology. DNA contains the complete genetic information that defines
Geography 4203 / 5203. GIS Modeling. Class (Block) 9: Variogram & Kriging
Geography 4203 / 5203 GIS Modeling Class (Block) 9: Variogram & Kriging Some Updates Today class + one proposal presentation Feb 22 Proposal Presentations Feb 25 Readings discussion (Interpolation) Last
The enigmatic monotypic crab plover Dromas ardeola is closely related to pratincoles and coursers (Aves, Charadriiformes, Glareolidae)
Short Communication Genetics and Molecular Biology, 33, 3, 583-586 (2010) Copyright 2010, Sociedade Brasileira de Genética. Printed in Brazil www.sbg.org.br The enigmatic monotypic crab plover Dromas ardeola
Vascular Plants Bryophytes. Seedless Plants
plant reproduction The Plants Vascular Plants Bryophytes Liverworts, Hornworts, Mosses lack roots and specialized tissues grow in moist, shady areas All have sieve cells and tracheids Seedless Plants Ferns
Final Project Report
CPSC545 by Introduction to Data Mining Prof. Martin Schultz & Prof. Mark Gerstein Student Name: Yu Kor Hugo Lam Student ID : 904907866 Due Date : May 7, 2007 Introduction Final Project Report Pseudogenes
Common Core Unit Summary Grades 6 to 8
Common Core Unit Summary Grades 6 to 8 Grade 8: Unit 1: Congruence and Similarity- 8G1-8G5 rotations reflections and translations,( RRT=congruence) understand congruence of 2 d figures after RRT Dilations
Virginia s Turfgrass Industry
Virginia s Turfgrass Industry Published August 2006 Compiled By National Agricultural Statistics Service Virginia Field Office USDA/NASS Virginia Field Office 02 Governors St. LL20 Richmond, VA. 2329-3676
Social Media Mining. Network Measures
Klout Measures and Metrics 22 Why Do We Need Measures? Who are the central figures (influential individuals) in the network? What interaction patterns are common in friends? Who are the like-minded users
What mathematical optimization can, and cannot, do for biologists. Steven Kelk Department of Knowledge Engineering (DKE) Maastricht University, NL
What mathematical optimization can, and cannot, do for biologists Steven Kelk Department of Knowledge Engineering (DKE) Maastricht University, NL Introduction There is no shortage of literature about the
OPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION
OPTIMAL DESIGN OF DISTRIBUTED SENSOR NETWORKS FOR FIELD RECONSTRUCTION Sérgio Pequito, Stephen Kruzick, Soummya Kar, José M. F. Moura, A. Pedro Aguiar Department of Electrical and Computer Engineering
Time series experiments
Time series experiments Time series experiments Why is this a separate lecture: The price of microarrays are decreasing more time series experiments are coming Often a more complex experimental design
Biology 300 Homework assignment #1 Solutions. Assignment:
Biology 300 Homework assignment #1 Solutions Assignment: Chapter 1, Problems 6, 15 Chapter 2, Problems 6, 8, 9, 12 Chapter 3, Problems 4, 6, 15 Chapter 4, Problem 16 Answers in bold. Chapter 1 6. Identify
Avoiding Tree & Utility Conflicts
Avoiding Tree & Utility Conflicts Determining where to plant a tree is a decision that should not be taken lightly. Many factors should be considered prior to planting. When planning what type of tree
A short guide to phylogeny reconstruction
A short guide to phylogeny reconstruction E. Michu Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic ABSTRACT This review is a short introduction to phylogenetic
BIOL 1030 TOPIC 5 LECTURE NOTES TOPIC 5: SEEDLESS VASCULAR PLANTS (CH. 29)
TOPIC 5: SEEDLESS VASCULAR PLANTS (CH. 29) I. Vascular Plants (overview) plants with xylem and phloem 7 or 9 living phyla, depending on who you talk to able to dominate most terrestrial habitats because
CLUSTER ANALYSIS FOR SEGMENTATION
CLUSTER ANALYSIS FOR SEGMENTATION Introduction We all understand that consumers are not all alike. This provides a challenge for the development and marketing of profitable products and services. Not every
PLANT DIVERSITY. EVOLUTION OF LAND PLANTS KINGDOM: Plantae
PLANT DIVERSITY 1 EVOLUTION OF LAND PLANTS KINGDOM: Plantae Spores Leaf Ancestral green algae Flagellated sperm for reproduction Plenty of water Nutrients and CO 2 diffuse into tissues Holdfast Flagellated
Taxonomy and Classification
Taxonomy and Classification Taxonomy = the science of naming and describing species Wisdom begins with calling things by their right names -Chinese Proverb museums contain ~ 2 Billion specimens worldwide
Common Core State Standards for Mathematics Accelerated 7th Grade
A Correlation of 2013 To the to the Introduction This document demonstrates how Mathematics Accelerated Grade 7, 2013, meets the. Correlation references are to the pages within the Student Edition. Meeting
2.3 Identify rrna sequences in DNA
2.3 Identify rrna sequences in DNA For identifying rrna sequences in DNA we will use rnammer, a program that implements an algorithm designed to find rrna sequences in DNA [5]. The program was made by
COC131 Data Mining - Clustering
COC131 Data Mining - Clustering Martin D. Sykora [email protected] Tutorial 05, Friday 20th March 2009 1. Fire up Weka (Waikako Environment for Knowledge Analysis) software, launch the explorer window
Host specificity and the probability of discovering species of helminth parasites
Host specificity and the probability of discovering species of helminth parasites 79 R. POULIN * and D. MOUILLOT Department of Zoology, University of Otago, P.O. Box 6, Dunedin, New Zealand UMR CNRS-UMII
A Step-by-Step Tutorial: Divergence Time Estimation with Approximate Likelihood Calculation Using MCMCTREE in PAML
9 June 2011 A Step-by-Step Tutorial: Divergence Time Estimation with Approximate Likelihood Calculation Using MCMCTREE in PAML by Jun Inoue, Mario dos Reis, and Ziheng Yang In this tutorial we will analyze
Inference of Large Phylogenetic Trees on Parallel Architectures. Michael Ott
Inference of Large Phylogenetic Trees on Parallel Architectures Michael Ott TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur Inference of
Cells are tiny building blocks that make up all living things. Cells are so small that you need a microscope to see them.
FC01 CELLS s are tiny building blocks that make up all living things. s are so small that you need a microscope to see them. ANIMAL CELL PLANT CELL This is the control centre of the cell. It contains chromosomes
How To Run Statistical Tests in Excel
How To Run Statistical Tests in Excel Microsoft Excel is your best tool for storing and manipulating data, calculating basic descriptive statistics such as means and standard deviations, and conducting
Introduction to Plants
Introduction to Plants Unity and Diversity of Life Q: What are the five main groups of plants, and how have four of these groups adapted to life on land? 22.1 What are of plants? WHAT I KNOW SAMPLE ANSWER:
Vector storage and access; algorithms in GIS. This is lecture 6
Vector storage and access; algorithms in GIS This is lecture 6 Vector data storage and access Vectors are built from points, line and areas. (x,y) Surface: (x,y,z) Vector data access Access to vector
Sequence Analysis 15: lecture 5. Substitution matrices Multiple sequence alignment
Sequence Analysis 15: lecture 5 Substitution matrices Multiple sequence alignment A teacher's dilemma To understand... Multiple sequence alignment Substitution matrices Phylogenetic trees You first need
