Molecular Phylogenetics Next Two Lectures
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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. -
Comparative Methods for Reconstructing Ancient Genome Organization Yoann Anselmetti, Nina Luhmann, Sèverine Bérard, Eric Tannier, Cedric Chauve
Comparative Methods for Reconstructing Ancient Genome Organization Yoann Anselmetti, Nina Luhmann, Sèverine Bérard, Eric Tannier, Cedric Chauve To cite this version: Yoann Anselmetti, Nina Luhmann, Sèverine Bérard, Eric Tannier, Cedric Chauve. Comparative Methods for Reconstructing Ancient Genome Organization. Comparative Genomics: Methods and Protocols, pp.343 - 362, 2017, 10.1007/978-1-4939-7463-4_13. hal-03192460 HAL Id: hal-03192460 https://hal.archives-ouvertes.fr/hal-03192460 Submitted on 8 Apr 2021 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. Chapter 13 Comparative Methods for Reconstructing Ancient Genome Organization Yoann Anselmetti, Nina Luhmann, Se`verine Be´rard, Eric Tannier, and Cedric Chauve Abstract Comparative genomics considers the detection of similarities and differences between extant genomes, and, based on more or less formalized hypotheses regarding the involved evolutionary processes, inferring ancestral states explaining the similarities and an evolutionary history explaining the differences. In this chapter, we focus on the reconstruction of the organization of ancient genomes into chromosomes. We review different methodological approaches and software, applied to a wide range of datasets from different kingdoms of life and at different evolutionary depths. We discuss relations with genome assembly, and potential approaches to validate computational predictions on ancient genomes that are almost always only accessible through these predictions. -
"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 -
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. -
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). -
Reconstruction of Ancestral RNA Sequences Under Multiple Structural Constraints Olivier Tremblay-Savard1,2, Vladimir Reinharz1 and Jérôme Waldispühl1*
The Author(s) BMC Genomics 2016, 17(Suppl 10):862 DOI 10.1186/s12864-016-3105-4 RESEARCH Open Access Reconstruction of ancestral RNA sequences under multiple structural constraints Olivier Tremblay-Savard1,2, Vladimir Reinharz1 and Jérôme Waldispühl1* From 14th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop Montreal, Canada. 11-14 October 2016 Abstract Background: Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors. Methods: In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families. Results: We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database. Conclusions: Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement. Keywords: RNA, Secondary structure, Ancestor reconstruction, Evolution, Phylogeny, Algorithm Background For a long time, most of the attention has been With the development of sequencing technologies given to the reconstruction of ancient protein and DNA emerged the need to elucidate the relationship between sequences, while RNA molecules remained relatively sequences from various organisms. -
Were Ancestral Proteins Less Specific?
bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120261; this version posted May 30, 2020. 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-ND 4.0 International license. Were ancestral proteins less specific? 1 1,2,3 1,2* Lucas C. Wheeler and Michael J. Harms 2 1. Institute of Molecular Biology, University of Oregon, Eugene OR 97403 3 2. Department of Chemistry and Biochemistry, University of Oregon, Eugene OR 97403 4 3. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder 5 CO 80309 6 7 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120261; this version posted May 30, 2020. 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-ND 4.0 International license. Abstract 8 Some have hypothesized that ancestral proteins were, on average, less specific than their 9 descendants. If true, this would provide a universal axis along which to organize protein 10 evolution and suggests that reconstructed ancestral proteins may be uniquely powerful tools 11 for protein engineering. Ancestral sequence reconstruction studies are one line of evidence 12 used to support this hypothesis. Previously, we performed such a study, investigating the 13 evolution of peptide binding specificity for the paralogs S100A5 and S100A6. -
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 -
Molecular Phylogenetics: State-Of-The- Art Methods for Looking Into the Past
262 Review TRENDS in Genetics Vol.17 No.5 May 2001 Molecular phylogenetics: state-of-the- art methods for looking into the past Simon Whelan, Pietro Liò and Nick Goldman As the amount of molecular sequence data in the public domain grows, so occurrence at each sequence site is mathematically does the range of biological topics that it influences through evolutionary modelled by a MARKOV PROCESS. Typically, the same considerations. In recent years, a number of developments have enabled Markov process model is applied to each sequence molecular phylogenetic methodology to keep pace. Likelihood-based inferential site. Different models are distinguished by their techniques, although controversial in the past, lie at the heart of these new assumptions or PARAMETERIZATIONS regarding the methods and are producing the promised advances in the understanding of average rates of occurrence of all the possible sequence evolution. They allow both a wide variety of phylogenetic inferences replacements3. It is now well established that from sequence data and robust statistical assessment of all results. It cannot more complex models, often describing replacement remain acceptable to use outdated data analysis techniques when superior rates in terms of a variety of biological phenomena, alternatives exist. Here, we discuss the most important and exciting methods generally give a statistically better fit to observed currently available to the molecular phylogeneticist. patterns of sequence evolution4–9, giving more accurate and robust estimates both of phylogeny10–12 Only through MOLECULAR PHYLOGENETIC (see Glossary) and the statistical confidence in the phylogeny13. The studies can we understand fully the rapidly development of more accurate models of sequence accumulating genomic sequence data and evolution has been a particularly active and fertile information regarding proteins’ structure and area of research in recent years, in the expectation function.