User Manual for Splitstree4 V4.17.1
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Ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods
Package ‘ade4’ September 16, 2021 Version 1.7-18 Title Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences Author Stéphane Dray <[email protected]>, Anne-Béatrice Du- four <[email protected]>, and Jean Thioulouse <[email protected]>, with con- tributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, Daniel Bor- card, Pierre Legendre, Stéphanie Bougeard and Aurélie Siberchicot. Based on ear- lier work by Daniel Chessel. Maintainer Aurélie Siberchicot <[email protected]> Depends R (>= 2.10) Imports graphics, grDevices, methods, stats, utils, MASS, pixmap, sp Suggests ade4TkGUI, adegraphics, adephylo, ape, CircStats, deldir, lattice, spdep, splancs, waveslim, progress, foreach, parallel, doParallel, iterators Description Tools for multivariate data analysis. Several methods are provided for the analy- sis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analy- sis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analy- sis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is de- scribed in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>. License GPL (>= 2) URL http://pbil.univ-lyon1.fr/ADE-4/ BugReports https://github.com/sdray/ade4/issues Encoding UTF-8 NeedsCompilation yes Repository CRAN Date/Publication 2021-09-16 11:30:02 UTC R topics documented: ade4-package . .8 1 2 R topics documented: abouheif.eg . .8 acacia . .9 add.scatter . 10 aminoacyl . 13 amova............................................ 14 apis108 . 15 apqe............................................. 16 aravo............................................. 17 ardeche . 18 area.plot . 19 arrival............................................ 21 as.taxo . 22 atlas . -
BIOINFORMATICS APPLICATIONS NOTE Doi:10.1093/Bioinformatics/Btl478
Vol. 22 no. 22 2006, pages 2823–2824 BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btl478 Phylogenetics Clearcut: a fast implementation of relaxed neighbor joining Luke ShenemanÃ, Jason Evans and James A. Foster Department of Biological Sciences, University of Idaho, Moscow, ID, USA Received on June 8, 2006; revised on September 5, 2006; accepted on September 6, 2006 Advance Access publication September 18, 2006 Associate Editor: Keith A Crandall ABSTRACT tree is constructed. At each step, traditional NJ searches the entire Summary: Clearcut is an open source implementation for the relaxed distance matrix and identifies and joins the pair of nodes with the neighbor joining (RNJ) algorithm. While traditional neighbor joining (NJ) global minimum transformed distance. In contrast, RNJ opportunis- remains a popular method for distance-based phylogenetic tree recon- tically joins any two neighboring nodes immediately after it is struction, it suffers from a O(N3) time complexity, where N represents determined that the nodes are closer to each other than any other the number of taxa in the input. Due to this steep asymptotic time node in the distance matrix. It is not required that the candidate complexity, NJ cannot reasonably handle very large datasets. In nodes be the closest of all nodes remaining in the matrix. In this contrast, RNJ realizes a typical-case time complexity on the order of sense, our algorithm relaxes the requirement of exhaustively search- N2logN without any significant qualitative difference in output. RNJ is ing the distance matrix at each step to find the closest two nodes particularly useful when inferring a very large tree or a large number of to join. -
"Introduction to Inferring Evolutionary Relationships". In: Current
Introduction to Inferring Evolutionary UNIT 6.1 Relationships Much of bioinformatics is essentially comparative biology. Inferences based on compari- sons between entities such as motifs, sequences, genomes, and molecular structures are made. Given that living organisms and their constituent components have an evolutionary history, phylogeny properly lies at the heart of comparative biology (Harvey and Pagel, 1991). Increasingly, researchers in bioinformatics are realizing that phylogeny-based comparisons can yield important insights that can be missed using other techniques (Eisen, 1998; Rehmsmeier and Vingron, 2001). For example, a knowledge of phylogeny is vital in determining whether a set of sequences are orthologous or paralogous (Page and Charleston, 1997; Yuan et al., 1998; Storm and Sonnhammer, 2001; Zmasek and Eddy, 2001), which in turn has implications for predicting the function of a novel sequence (Eisen, 1998). Indeed, it is increasingly common for protein family databases to include gene phylogenies in addition to alignments. Examples include HOVERGEN (http://pbil.univ-lyon1.fr/databases/hovergen.html; Duret et al., 1994), SYSTERS (http://systers.molgen.mpg.de; Krause et al., 2000), and COPSE (http://copse.molgen. mpg.de). Phylogenetic analysis at the level of whole genomes poses new analytical challenges (Kim and Salisbury, 2001; Korbel et al., 2002; Wang et al., 2002), but promises new insights into genomic evolution. In some cases, the role of phylogeny might not be either obvious or explicit, so that many people may well have built phylogenetic trees without necessarily realizing it. The popular multiple sequence alignment program Clustal (UNIT 2.3) builds a phylogeny (the “guide tree”) every time it aligns sequences. -
Neighbor Joining, Fastme, and Distance-Based Methods
Getting a Tree Fast: Neighbor Joining, UNIT 6.3 FastME, and Distance-Based Methods Distance methods, and especially Neighbor Joining (NJ; Saitou and Nei, 1987), are popular methods for reconstructing phylogenies from alignments of DNA or protein sequences (UNIT 2.3). They are fast, allowing hundreds and even thousands of taxa to be dealt with by ordinary computers. The speed of these methods greatly simplifies the use of the bootstrap procedure (Page and Holmes, 1998; Graur and Li, 2000), which assesses the confidence level of inferred clades. They provide a simple way to incorporate knowledge of the evolution of the sequences being studied, depending on how the distance matrix is estimated. Numerous simulation studies have demonstrated their topological accuracy, and, unlike parsimony methods, they are not hampered by inconsistency (or “Felsenstein zone”; Swofford et al., 1996). The popularity of NJ, among the numerous existing distance-based methods, is explained by its speed and by the fact that its topological accuracy remains relatively close to that of recent approaches—i.e., FITCH (Felsenstein, 1997), BIONJ (Gascuel, 1997a), WEIGHBOR (Bruno et al., 2000), and FastME (Desper and Gascuel, 2002, 2004). However, several simulation studies (e.g., Vinh and Von Haeseler, 2005) showed that, with a high number of taxa, NJ is outperformed by FastME, both in terms of computing time and topological accuracy. Therefore, this latter program should be considered preferable for large-scale studies. NJ and other current distance methods do not assume a molecular clock (Page and Holmes, 1998), as opposed to the Unweighted Pair Group Method Using Arithmetic averages (UPGMA; Sokal and Michener, 1958), which is precluded for most phylogenetic studies. -
Tree Editing & Visualization
Tree Editing & Visualization Lisa Pokorny & Marina Marcet-Houben (with help from Miguel Ángel Naranjo Ortiz) Data Visualization VISUALIZATION SWEET SPOT “Strive to give your viewer the greatest number of useful ideas in the shortest time with the least ink in the smallest space” Tufte, E. The Visual Display of Quantitative Information (Graphic Press, Cheshire, Connecticut, USA, 2007). http://mkweb.bcgsc.ca/vizbi/2013/krzywinski-visual-design-principles-vizbi2013.pdf Data Visualization ● Satisfy your audience, not yourself ● Don’t merely display data, explain it ● Be aware of bias in evaluating effectiveness of visual forms ● Patterns are hard to see when variation is due to both data and formatting http://mkweb.bcgsc.ca/vizbi/2013/krzywinski-visual-design-principles-vizbi2013.pdf Data Visualization ● Know your message and stick to it (context musn’t dilute message). ● Choose effective encoding (to explore your data) and design (to communicate concepts). Data Visualization How do we get from data to visualization? http://mkweb.bcgsc.ca/vizbi/2013/krzywinski-visual-design-principles-vizbi2013.pdf Data Visualization How do we get from data to visualization? ● properties of the data / data type ○ Phylogenies (cladograms, phylograms, chronograms, cloudograms, etc.) ○ Networks (reticulograms, tanglegrams, etc.) ● properties of the image / visual encoding ○ What? Points, lines, labels… ○ Where? 2D, 3D(?) ○ How? Size, shape, texture, color, hue... ● the rules of mapping data to image ○ Principles of grouping ○ etc. Krygier & Wood. 2011. Making -
An R Interface for PHYLIP
Methods in Ecology and Evolution 2014, 5, 976–981 doi: 10.1111/2041-210X.12233 APPLICATION Rphylip: an R interface for PHYLIP Liam J. Revell1* and Scott A. Chamberlain2 1Department of Biology, University of Massachusetts Boston, Boston, MA 02125, USA; and 2Department of Biological Sciences, Simon Fraser University, Vancouver, BC V5A 1S6, Canada Summary 1. The phylogeny methods software package PHYLIP has long been among the most widely used packages for phylogeny inference and phylogenetic comparative biology. Numerous methods available in PHYLIP, including several new phylogenetic comparative analyses of considerable importance, are not implemented in any other software. 2. Over the past decade, the popularity of the R statistical computing environment for many different types of phylogenetic analyses has soared, particularly in phylogenetic comparative biology. There are now numerous packages and methods developed for the R environment. 3. In this article, we present Rphylip, a new R interface for the PHYLIP package. Functions of Rphylip interface seamlessly with all of the major analysis functions of the PHYLIP package. This new interface will enable the much easier use of PHYLIP programs in an integrated R workflow. 4. In this study, we describe our motivation for developing Rphylip and present an illustration of how functions in the Rphylip package can be used for phylogenetic analysis in R. Key-words: phylogeny, statistics, computational biology, evolution PHYLIP contains a wide diversity of methods. These meth- Introduction ods cover both phylogeny inference and evolutionary analysis Over the past several decades, phylogenetics has assumed a using phylogenies, including a range of analyses not imple- central role in evolutionary study (Felsenstein 1985b, 2004; mented (so far as we know) in any other software (e.g. -
Intro to PHYLIP
Integrative Biology 200A University of California, Berkeley "PRINCIPLES OF PHYLOGENETICS" Spring 2012 Lab 1: Introduction to PHYLIP What’s due at the end of lab, or next Tuesday in class: 1. Print out of Caminicules outfile and outtrees from pars 2. Print out of Azolla consensus output Setup 1. Have a laptop running either Windows or Mac. (Or you might get away with Linux, but I am not a Linux wizard.) 2. Download and install MESQUITE. (If this doesn’t work out of the box, you may have to download Java Virtual Machine as well, some older computers might not have it already.) 3. Download and install PHYLIP. Introduction Today we will be learning about some of the features of the PHYLIP (PHYLogeny Inference Package) software package. PHYLIP was developed by the famous evolutionary biologist Joe Felsenstein, works on most operating systems, and is available for free online. It is widely used, but slightly less popular than PAUP*. Like Mesquite, PHYLIP is an open source package, and you can make changes yourself if you program in C++. Methods available in the package include parsimony, distance matrix, and likelihood, as well as bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites, distance matrices, and discrete-state characters. What is PHYLIP? PHYLIP consists of about 30 programs that perform different algorithms on various types of data, and collectively are able to do most things you might want to do when it comes to inferring phylogenies. In PHYLIP, there is -
Romanesco Documentation Release 0.1.0
Romanesco Documentation Release 0.1.0 Kitware, Inc. January 27, 2016 Contents 1 What is Romanesco? 1 1.1 Installation................................................1 1.2 Configuration...............................................1 1.3 Types and formats............................................2 1.4 API documentation............................................5 1.5 Developer documentation........................................ 11 1.6 Plugins.................................................. 13 2 Indices and tables 17 Python Module Index 19 i ii CHAPTER 1 What is Romanesco? Romanesco is a python application for generic task execution. It can be run within a celery worker to provide a distributed batch job execution platform. The application can run tasks in a variety of languages and environments, including python, R, spark, and docker, all via a single python or celery broker interface. Tasks can be chained together into workflows, and these workflows can actually span multiple languages and environments seamlessly. Data flowing between tasks can be automatically converted into a format understandable in the target environment. For example, a python object from a python task can be automatically converted into an R object for an R task at the next stage of a pipeline. Romanesco defines a specification that prescribes a loose coupling between a task and its runtime inputs and outputs. That specification is described in the API documentation section. This specification is language-independent and instances of the spec are best represented by a hierarchical data format such as JSON or YAML, or an equivalent serializable type such as a dict in python. Romanesco is designed to be easily extended to new languages and environments, or to support new data types and formats, or modes of data transfer. -
A Software Tool for the Conversion of Sequence Alignments
embnet.news Volume 6 Nr. 1 Page 10 when you start SeqLab. On my Macintosh, TNTx/SeqLab Device: LASERWRITER is always stable when SeqLab is launched as the third Port or Queue: ps-filename program. Therefore, I usually run two other X-windows programs (xterm or emacs) before running SeqLab. If you If you have X-windows connection to Unix-host, you can cannot tolerate this you will have to switch to a "real" X- view postscript files by xv and also transfer to other formats. server. Also, when you use xv, it is better and faster to define DISPLAY to view files unciphered. GCGFigure setenv DISPLAY ip_number_of_mac:0.0 xv ps-filename GCGFigure is the best way (IMHO) to transfer graphics from GCG to Macintosh image programs. First, run GCG program with parameter -fig(ure)=anyfile.name. The output file (figure file) is a text file and can be transferred to the Macintosh by Fetch or via clipboard. If the scrollback buffer ForCon : a software tool for of Telnet (Edit-menu -> Preferences -> Terminals) or SSH the conversion of sequence (Edit-menu -> Connection properties -> Terminal-tab) is enough big, copy the file from screen to clipboard, paste alignments into text editor and save as a text file. Open the figure file into GCGFigure and save as a PICT file which all image programs can open. You can download GCGFigure from Jeroen Raes and Yves Van de Peer* - Department of ftp://alanine.gcg.com from directory Biochemistry, University of Antwerp (UIA), /pub/mac/. Universiteitsplein 1, B-2610 Antwerpen, Belgium *To whom correspondence should be addressed (E-mail: PostScript-files [email protected] ) key words : sequence alignments – phylogenetic analysis GCG programs can save output as a postscript file. -
Tutorial: Environment for Tree Exploration Release 3.0.0B7
Tutorial: Environment for Tree Exploration Release 3.0.0b7 Jaime Huerta-Cepas November 25, 2015 Contents 1 Changelog history3 1.1 What’s new in ETE 2.3...................................3 1.2 What’s new in ETE 2.2...................................5 1.3 What’s new in ETE 2.1...................................9 2 The ETE tutorial 13 2.1 Working With Tree Data Structures............................ 13 2.2 The Programmable Tree Drawing Engine......................... 43 2.3 Phylogenetic Trees..................................... 64 2.4 Clustering Trees...................................... 80 2.5 Phylogenetic XML standards............................... 85 2.6 Interactive web tree visualization............................. 93 2.7 Testing Evolutionary Hypothesis.............................. 95 2.8 Dealing with the NCBI Taxonomy database........................ 105 2.9 SCRIPTS: orthoXML................................... 108 3 ETE’s Reference Guide 115 3.1 Master Tree class...................................... 115 3.2 Treeview module...................................... 127 3.3 PhyloTree class....................................... 138 3.4 Clustering module..................................... 141 3.5 Nexml module....................................... 142 3.6 Phyloxml Module..................................... 158 3.7 Seqgroup class....................................... 161 3.8 WebTreeApplication object................................ 161 3.9 EvolTree class....................................... 162 3.10 NCBITaxa class..................................... -
Fine Tuning of Phylip on Intel Xeon Architecture
Prinkesh Sharma et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 2587-2592 FINE TUNING OF PHYLIP ON INTEL XEON ARCHITECTURE 1 2 3 Prinkesh Sharma , Kondorpa Kumar Borchetia , Shivam Saxena 1,2,3Dept. of Computer Science & Engineering, National Institute of Technology Silchar-788010, India Sumeet Singh Bhambrah4, Anneswa Ghosh5 andJoydeep Chakraborty6 4,5,6Dept. of Computer Science & Engineering, National Institute of Technology Silchar-788010, India Abstract -Computational Science is witnessing an exceptional growth over the years, but we are still lacking in efficient programming to effectively optimize these computations. In today's modern world, computations need to be done and results delivered in the least possible time. Porting, optimization, scaling and tuning of existing High Performance Computing (HPC) Applications on hybrid architectures is the norm for reaping the benefits of extreme scale computing. That being said, we must remember that the real gist in optimizing computations lies in properly tuning the core source code running on a single processor or a shared memory model within a node. This paper gauges the performance of PHYLIP application on Intel Xeon Processor. Keywords - High Performance Computing,Parallel Programming,Optimization,Phylogenetic I. INTRODUCTION A. About PHYLIP HPC systems are becoming challenging in terms PHYLIP is a comprehensive phylogenetic analysis of speedup and scalability. This ever increasing package created by Joseph Felsenstein at the University of complexity demands well-organized and flexible Washington. The PHYLIP package is one of the most numerical algorithms to achieve high performance comprehensive sets of tools freely available for use in computing. The size of compute intensive problems also phylogenetic studies [6]. -
Anti-Consensus: Detecting Trees That Have an Evolutionary Signal That Is Lost in Consensus
bioRxiv preprint doi: https://doi.org/10.1101/706416; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Anti-consensus: detecting trees that have an evolutionary signal that is lost in consensus Daniel H. Huson1 ∗, Benjamin Albrecht1, Sascha Patz1 and Mike Steel2 1 Institute for Bioinformatics and Medical Informatics, University of T¨ubingen,Germany 2 Biomathematics Research Centre, University of Canterbury, Christchurch, New Zealand *Corresponding author, [email protected] Abstract In phylogenetics, a set of gene trees is often summarized by a consensus tree, such as the majority consensus, which is based on the set of all splits that are present in more than 50% of the input trees. A \consensus network" is obtained by lowering the threshold and considering all splits that are contained in 10% of the trees, say, and then computing the corresponding splits network. By construction and in practice, a consensus network usually shows the majority tree, extended by a number of rectangles that represent local rearrangements around internal nodes of the consensus tree. This may lead to the false conclusion that the input trees do not differ in a significant way because \even a phylogenetic network" does not display any large discrepancies. To harness the full potential of a phylogenetic network, we introduce the new concept of an anti-consensus network that aims at representing the largest interesting discrepancies found in a set of gene trees.