Large-Scale Multiple Sequence Alignment and Phylogeny Estimation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Selecting Molecular Markers for a Specific Phylogenetic Problem
MOJ Proteomics & Bioinformatics Review Article Open Access Selecting molecular markers for a specific phylogenetic problem Abstract Volume 6 Issue 3 - 2017 In a molecular phylogenetic analysis, different markers may yield contradictory Claudia AM Russo, Bárbara Aguiar, Alexandre topologies for the same diversity group. Therefore, it is important to select suitable markers for a reliable topological estimate. Issues such as length and rate of evolution P Selvatti Department of Genetics, Federal University of Rio de Janeiro, will play a role in the suitability of a particular molecular marker to unfold the Brazil phylogenetic relationships for a given set of taxa. In this review, we provide guidelines that will be useful to newcomers to the field of molecular phylogenetics weighing the Correspondence: Claudia AM Russo, Molecular Biodiversity suitability of molecular markers for a given phylogenetic problem. Laboratory, Department of Genetics, Institute of Biology, Block A, CCS, Federal University of Rio de Janeiro, Fundão Island, Keywords: phylogenetic trees, guideline, suitable genes, phylogenetics Rio de Janeiro, RJ, 21941-590, Brazil, Tel 21 991042148, Fax 21 39386397, Email [email protected] Received: March 14, 2017 | Published: November 03, 2017 Introduction concept that occupies a central position in evolutionary biology.15 Homology is a qualitative term, defined by equivalence of parts due Over the last three decades, the scientific field of molecular to inherited common origin.16–18 biology has experienced remarkable advancements -
An Automated Pipeline for Retrieving Orthologous DNA Sequences from Genbank in R
life Technical Note phylotaR: An Automated Pipeline for Retrieving Orthologous DNA Sequences from GenBank in R Dominic J. Bennett 1,2,* ID , Hannes Hettling 3, Daniele Silvestro 1,2, Alexander Zizka 1,2, Christine D. Bacon 1,2, Søren Faurby 1,2, Rutger A. Vos 3 ID and Alexandre Antonelli 1,2,4,5 ID 1 Gothenburg Global Biodiversity Centre, Box 461, SE-405 30 Gothenburg, Sweden; [email protected] (D.S.); [email protected] (A.Z.); [email protected] (C.D.B.); [email protected] (S.F.); [email protected] (A.A.) 2 Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE-405 30 Gothenburg, Sweden 3 Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA Leiden, The Netherlands; [email protected] (H.H.); [email protected] (R.A.V.) 4 Gothenburg Botanical Garden, Carl Skottsbergsgata 22A, SE-413 19 Gothenburg, Sweden 5 Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford St., Cambridge, MA 02138 USA * Correspondence: [email protected] Received: 28 March 2018; Accepted: 1 June 2018; Published: 5 June 2018 Abstract: The exceptional increase in molecular DNA sequence data in open repositories is mirrored by an ever-growing interest among evolutionary biologists to harvest and use those data for phylogenetic inference. Many quality issues, however, are known and the sheer amount and complexity of data available can pose considerable barriers to their usefulness. A key issue in this domain is the high frequency of sequence mislabeling encountered when searching for suitable sequences for phylogenetic analysis. -
"Phylogenetic Analysis of Protein Sequence Data Using The
Phylogenetic Analysis of Protein Sequence UNIT 19.11 Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program Antonis Rokas1 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee ABSTRACT Phylogenetic analysis is the study of evolutionary relationships among molecules, phenotypes, and organisms. In the context of protein sequence data, phylogenetic analysis is one of the cornerstones of comparative sequence analysis and has many applications in the study of protein evolution and function. This unit provides a brief review of the principles of phylogenetic analysis and describes several different standard phylogenetic analyses of protein sequence data using the RAXML (Randomized Axelerated Maximum Likelihood) Program. Curr. Protoc. Mol. Biol. 96:19.11.1-19.11.14. C 2011 by John Wiley & Sons, Inc. Keywords: molecular evolution r bootstrap r multiple sequence alignment r amino acid substitution matrix r evolutionary relationship r systematics INTRODUCTION the baboon-colobus monkey lineage almost Phylogenetic analysis is a standard and es- 25 million years ago, whereas baboons and sential tool in any molecular biologist’s bioin- colobus monkeys diverged less than 15 mil- formatics toolkit that, in the context of pro- lion years ago (Sterner et al., 2006). Clearly, tein sequence analysis, enables us to study degree of sequence similarity does not equate the evolutionary history and change of pro- with degree of evolutionary relationship. teins and their function. Such analysis is es- A typical phylogenetic analysis of protein sential to understanding major evolutionary sequence data involves five distinct steps: (a) questions, such as the origins and history of data collection, (b) inference of homology, (c) macromolecules, developmental mechanisms, sequence alignment, (d) alignment trimming, phenotypes, and life itself. -
New Syllabus
M.Sc. BIOINFORMATICS REGULATIONS AND SYLLABI (Effective from 2019-2020) Centre for Bioinformatics SCHOOL OF LIFE SCIENCES PONDICHERRY UNIVERSITY PUDUCHERRY 1 Pondicherry University School of Life Sciences Centre for Bioinformatics Master of Science in Bioinformatics The M.Sc Bioinformatics course started since 2007 under UGC Innovative program Program Objectives The main objective of the program is to train the students to learn an innovative and evolving field of bioinformatics with a multi-disciplinary approach. Hands-on sessions will be provided to train the students in both computer and experimental labs. Program Outcomes On completion of this program, students will be able to: Gain understanding of the principles and concepts of both biology along with computer science To use and describe bioinformatics data, information resource and also to use the software effectively from large databases To know how bioinformatics methods can be used to relate sequence to structure and function To develop problem-solving skills, new algorithms and analysis methods are learned to address a range of biological questions. 2 Eligibility for M.Sc. Bioinformatics Students from any of the below listed Bachelor degrees with minimum 55% of marks are eligible. Bachelor’s degree in any relevant area of Physics / Chemistry / Computer Science / Life Science with a minimum of 55% of marks 3 PONDICHERRY UNIVERSITY SCHOOL OF LIFE SCIENCES CENTRE FOR BIOINFORMATICS LIST OF HARD-CORE COURSES FOR M.Sc. BIOINFORMATICS (Academic Year 2019-2020 onwards) Course -
Errors in Multiple Sequence Alignment and Phylogenetic Reconstruction
Multiple Sequence Alignment Errors and Phylogenetic Reconstruction THESIS SUBMITTED FOR THE DEGREE “DOCTOR OF PHILOSOPHY” BY Giddy Landan SUBMITTED TO THE SENATE OF TEL-AVIV UNIVERSITY August 2005 This work was carried out under the supervision of Professor Dan Graur Acknowledgments I would like to thank Dan for more than a decade of guidance in the fields of molecular evolution and esoteric arts. This study would not have come to fruition without the help, encouragement and moral support of Tal Dagan and Ron Ophir. To them, my deepest gratitude. Time flies like an arrow Fruit flies like a banana - Groucho Marx Table of Contents Abstract ..........................................................................................................................1 Chapter 1: Introduction................................................................................................5 Sequence evolution...................................................................................................6 Alignment Reconstruction........................................................................................7 Errors in reconstructed MSAs ................................................................................10 Motivation and aims...............................................................................................13 Chapter 2: Methods.....................................................................................................17 Symbols and Acronyms..........................................................................................17 -
Molecular Phylogenetics Suggests a New Classification and Uncovers Convergent Evolution of Lithistid Demosponges
RESEARCH ARTICLE Deceptive Desmas: Molecular Phylogenetics Suggests a New Classification and Uncovers Convergent Evolution of Lithistid Demosponges Astrid Schuster1,2, Dirk Erpenbeck1,3, Andrzej Pisera4, John Hooper5,6, Monika Bryce5,7, Jane Fromont7, Gert Wo¨ rheide1,2,3* 1. Department of Earth- & Environmental Sciences, Palaeontology and Geobiology, Ludwig-Maximilians- Universita¨tMu¨nchen, Richard-Wagner Str. 10, 80333 Munich, Germany, 2. SNSB – Bavarian State Collections OPEN ACCESS of Palaeontology and Geology, Richard-Wagner Str. 10, 80333 Munich, Germany, 3. GeoBio-CenterLMU, Ludwig-Maximilians-Universita¨t Mu¨nchen, Richard-Wagner Str. 10, 80333 Munich, Germany, 4. Institute of Citation: Schuster A, Erpenbeck D, Pisera A, Paleobiology, Polish Academy of Sciences, ul. Twarda 51/55, 00-818 Warszawa, Poland, 5. Queensland Hooper J, Bryce M, et al. (2015) Deceptive Museum, PO Box 3300, South Brisbane, QLD 4101, Australia, 6. Eskitis Institute for Drug Discovery, Griffith Desmas: Molecular Phylogenetics Suggests a New Classification and Uncovers Convergent Evolution University, Nathan, QLD 4111, Australia, 7. Department of Aquatic Zoology, Western Australian Museum, of Lithistid Demosponges. PLoS ONE 10(1): Locked Bag 49, Welshpool DC, Western Australia, 6986, Australia e116038. doi:10.1371/journal.pone.0116038 *[email protected] Editor: Mikhail V. Matz, University of Texas, United States of America Received: July 3, 2014 Accepted: November 30, 2014 Abstract Published: January 7, 2015 Reconciling the fossil record with molecular phylogenies to enhance the Copyright: ß 2015 Schuster et al. This is an understanding of animal evolution is a challenging task, especially for taxa with a open-access article distributed under the terms of the Creative Commons Attribution License, which mostly poor fossil record, such as sponges (Porifera). -
Molecular Homology and Multiple-Sequence Alignment: an Analysis of Concepts and Practice
CSIRO PUBLISHING Australian Systematic Botany, 2015, 28, 46–62 LAS Johnson Review http://dx.doi.org/10.1071/SB15001 Molecular homology and multiple-sequence alignment: an analysis of concepts and practice David A. Morrison A,D, Matthew J. Morgan B and Scot A. Kelchner C ASystematic Biology, Uppsala University, Norbyvägen 18D, Uppsala 75236, Sweden. BCSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia. CDepartment of Biology, Utah State University, 5305 Old Main Hill, Logan, UT 84322-5305, USA. DCorresponding author. Email: [email protected] Abstract. Sequence alignment is just as much a part of phylogenetics as is tree building, although it is often viewed solely as a necessary tool to construct trees. However, alignment for the purpose of phylogenetic inference is primarily about homology, as it is the procedure that expresses homology relationships among the characters, rather than the historical relationships of the taxa. Molecular homology is rather vaguely defined and understood, despite its importance in the molecular age. Indeed, homology has rarely been evaluated with respect to nucleotide sequence alignments, in spite of the fact that nucleotides are the only data that directly represent genotype. All other molecular data represent phenotype, just as do morphology and anatomy. Thus, efforts to improve sequence alignment for phylogenetic purposes should involve a more refined use of the homology concept at a molecular level. To this end, we present examples of molecular-data levels at which homology might be considered, and arrange them in a hierarchy. The concept that we propose has many levels, which link directly to the developmental and morphological components of homology. -
The Tree Alignment Problem Andres´ Varon´ and Ward C Wheeler*
Varon´ and Wheeler BMC Bioinformatics 2012, 13:293 http://www.biomedcentral.com/1471-2105/13/293 RESEARCH ARTICLE Open Access The tree alignment problem Andres´ Varon´ and Ward C Wheeler* Abstract Background: The inference of homologies among DNA sequences, that is, positions in multiple genomes that share a common evolutionary origin, is a crucial, yet difficult task facing biologists. Its computational counterpart is known as the multiple sequence alignment problem. There are various criteria and methods available to perform multiple sequence alignments, and among these, the minimization of the overall cost of the alignment on a phylogenetic tree is known in combinatorial optimization as the Tree Alignment Problem. This problem typically occurs as a subproblem of the Generalized Tree Alignment Problem, which looks for the tree with the lowest alignment cost among all possible trees. This is equivalent to the Maximum Parsimony problem when the input sequences are not aligned, that is, when phylogeny and alignments are simultaneously inferred. Results: For large data sets, a popular heuristic is Direct Optimization (DO). DO provides a good tradeoff between speed, scalability, and competitive scores, and is implemented in the computer program POY. All other (competitive) algorithms have greater time complexities compared to DO. Here, we introduce and present experiments a new algorithm Affine-DO to accommodate the indel (alignment gap) models commonly used in phylogenetic analysis of molecular sequence data. Affine-DO has the same time complexity as DO, but is correctly suited for the affine gap edit distance. We demonstrate its performance with more than 330,000 experimental tests. These experiments show that the solutions of Affine-DO are close to the lower bound inferred from a linear programming solution. -
Incorporating Molecular Data Into Your Phylogenetic Tree Activity Adapted from Bishop Museum ECHO Project Taxonomy Module and ENSI/SENSI Making Cladograms Lesson
Incorporating Molecular Data into your Phylogenetic Tree Activity adapted from Bishop Museum ECHO Project Taxonomy Module and ENSI/SENSI Making Cladograms Lesson In addition to morphological data, molecular data, such as DNA sequences, can also tell us about the evolutionary relationships between organisms. For example, in the DNA sequence data below, we can see that the E. coli sequence is very different from the sequences of humans, yeast and corn (this sequence data comes from one slowly evolving gene, called the 16S rRNA gene). This makes sense considering that bacteria are in their own domain, and so their DNA sequences are very different than the other organisms. Your job is to incorporate the DNA sequence data in the following table into your morphological phylogeny from last class. You can consider the tunicate to be the most ancestral species. Examine the sequences, and determine how many DNA bases each organism has in common with the tunicate. It may help to highlight common sequences in different colors. Organism Genotype # of Bases in Common Tunicate (Ancestor) GTAAGCCGTTTAGCGTTAACGTCCGTAGCTAAGGTCCGTAGC 42 Yellowfin 33 tuna GTAAAATTTTTAGCGTTAATTCATGTAGCTAAGGTCCGTAGC Coqui frog GTAAAATTAAAAGCGTTAATTCATGTAGCTAAGGTCCGGCGC 28 Green sea 24 turtle GTATAATTAAAAGCGTTAATTCATGTAGCTTCCGTCCGGCGC Wallaby 18 GTTTAATTAAAAGCGTTCCTTCATGTAGCTTCCACGCGGCGC Hoary bat 16 GTTTAATTAAAAGATTTCCTTCATGTAGCTTCCACGCGGCGC Human 15 GTTTAATTAAAAGATTTCCTTCATGTGGCTTCCACGCGGCGC Material developed for the University of Hawaii-Manoa GK-12 program (NSF grant #05385500). Website: www.hawaii.edu/gk-12/evolution. Duplication for educational purposes only. Enter the number of bases each organism has in common with the tunicate into the phylogeny below: Tunicate Tuna Frog Turtle Wallaby Bat Human ____ bases ____ bases ____ bases ____ bases ____ bases ____ bases Does the molecular data agree with the morphological data? What would you do if the molecular and morphological data do not agree? Material developed for the University of Hawaii-Manoa GK-12 program (NSF grant #05385500). -
Sequence Alignment in Molecular Biology
JOURNAL OF COMPUTATIONAL BIOLOGY Volume 5, Number 2,1998 Mary Ann Liebert, Inc. Pp. 173-196 Sequence Alignment in Molecular Biology ALBERTO APOSTÓLICO1 and RAFFAELE GIANCARLO2 ABSTRACT Molecular biology is becoming a computationally intense realm of contemporary science and faces some of the current grand scientific challenges. In its context, tools that identify, store, compare and analyze effectively large and growing numbers of bio-sequences are found of increasingly crucial importance. Biosequences are routinely compared or aligned, in a variety of ways, to infer common ancestry, to detect functional equivalence, or simply while search- ing for similar entries in a database. A considerable body of knowledge has accumulated on sequence alignment during the past few decades. Without pretending to be exhaustive, this paper attempts a survey of some criteria of wide use in sequence alignment and comparison problems, and of the corresponding solutions. The paper is based on presentations and lit- erature at the on held at in November given Workshop Sequence Alignment Princeton, N.J., 1994, as part of the DIMACS Special Year on Mathematical Support for Molecular Biology. Key words: computational molecular biology, design and analysis of algorithms, sequence align- ment, sequence data banks, edit distances, dynamic programming, philogeny, evolutionary tree. 1. INTRODUCTION taxonomy is based ON THE assumption that conspicuous morphological and functional Classicalsimilarities in species denote close common ancestry. Likewise, modern molecular taxonomy pursues phylogeny and classification of living species based on the conformation and structure of their respective ge- netic codes. It is assumed that DNA code presides over the reproduction, development and susteinance of living organisms, part of it (RNA) being employed directly in various biological functions, part serving as a template or blueprint for proteins. -
Molecular Phylogenetics: Principles and Practice
REVIEWS STUDY DESIGNS Molecular phylogenetics: principles and practice Ziheng Yang1,2 and Bruce Rannala1,3 Abstract | Phylogenies are important for addressing various biological questions such as relationships among species or genes, the origin and spread of viral infection and the demographic changes and migration patterns of species. The advancement of sequencing technologies has taken phylogenetic analysis to a new height. Phylogenies have permeated nearly every branch of biology, and the plethora of phylogenetic methods and software packages that are now available may seem daunting to an experimental biologist. Here, we review the major methods of phylogenetic analysis, including parsimony, distance, likelihood and Bayesian methods. We discuss their strengths and weaknesses and provide guidance for their use. statistical Systematics Before the advent of DNA sequencing technologies, phylogenetics, creating the emerging field of 2,18,19 The inference of phylogenetic phylogenetic trees were used almost exclusively to phylogeography. In species tree methods , the gene relationships among species describe relationships among species in systematics and trees at individual loci may not be of direct interest and and the use of such information taxonomy. Today, phylogenies are used in almost every may be in conflict with the species tree. By averaging to classify species. branch of biology. Besides representing the relation- over the unobserved gene trees under the multi-species 20 Taxonomy ships among species on the tree of life, phylogenies -
Algorithmic Complexity in Computational Biology: Basics, Challenges and Limitations
Algorithmic complexity in computational biology: basics, challenges and limitations Davide Cirillo#,1,*, Miguel Ponce-de-Leon#,1, Alfonso Valencia1,2 1 Barcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, Spain 2 ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain. # Contributed equally * corresponding author: [email protected] (Tel: +34 934137971) Key points: ● Computational biologists and bioinformaticians are challenged with a range of complex algorithmic problems. ● The significance and implications of the complexity of the algorithms commonly used in computational biology is not always well understood by users and developers. ● A better understanding of complexity in computational biology algorithms can help in the implementation of efficient solutions, as well as in the design of the concomitant high-performance computing and heuristic requirements. Keywords: Computational biology, Theory of computation, Complexity, High-performance computing, Heuristics Description of the author(s): Davide Cirillo is a postdoctoral researcher at the Computational biology Group within the Life Sciences Department at Barcelona Supercomputing Center (BSC). Davide Cirillo received the MSc degree in Pharmaceutical Biotechnology from University of Rome ‘La Sapienza’, Italy, in 2011, and the PhD degree in biomedicine from Universitat Pompeu Fabra (UPF) and Center for Genomic Regulation (CRG) in Barcelona, Spain, in 2016. His current research interests include Machine Learning, Artificial Intelligence, and Precision medicine. Miguel Ponce de León is a postdoctoral researcher at the Computational biology Group within the Life Sciences Department at Barcelona Supercomputing Center (BSC). Miguel Ponce de León received the MSc degree in bioinformatics from University UdelaR of Uruguay, in 2011, and the PhD degree in Biochemistry and biomedicine from University Complutense de, Madrid (UCM), Spain, in 2017.