Orthodb: the Hierarchical Catalog of Eukaryotic Orthologs in 2011 Robert M
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Prospects & Overviews Orthology Prediction Methods: a Quality Assessment Using Curated Protein Families
Prospects & Overviews Methods, Models & Techniques Orthology prediction methods: A quality assessment using curated protein families Kalliopi Trachana1), Tomas A. Larsson1)2), Sean Powell1), Wei-Hua Chen1), Tobias Doerks1), Jean Muller3)4) and Peer Bork1)5)Ã The increasing number of sequenced genomes has Introduction prompted the development of several automated orthology prediction methods. Tests to evaluate the accuracy of pre- The analysis of fully sequenced genomes offers valuable insights into the function and evolution of biological systems dictions and to explore biases caused by biological and [1]. The annotation of newly sequenced genomes, comparative technical factors are therefore required. We used 70 man- and functional genomics, and phylogenomics depend on ually curated families to analyze the performance of five reliable descriptions of the evolutionary relationships of public methods in Metazoa. We analyzed the strengths protein families. All the members within a protein family and weaknesses of the methods and quantified the impact are homologous and can be further separated into orthologs, of biological and technical challenges. From the latter part which are genes derived through speciation from a single ancestral sequence, and paralogs, which are genes resulting of the analysis, genome annotation emerged as the largest from duplication events before and after speciation (out- and single influencer, affecting up to 30% of the performance. in-paralogy, respectively) [2, 3]. The large number of fully Generally, most methods did well in assigning orthologous sequenced genomes and the fundamental role of orthology group but they failed to assign the exact number of genes in modern biology have led to the development of a plethora of forhalfofthegroups.Thepublicly available benchmark set methods (e.g. -
Complementary Symbiont Contributions to Plant Decomposition in a Fungus-Farming Termite
Complementary symbiont contributions to plant decomposition in a fungus-farming termite Michael Poulsena,1,2, Haofu Hub,1, Cai Lib,c, Zhensheng Chenb, Luohao Xub, Saria Otania, Sanne Nygaarda, Tania Nobred,3, Sylvia Klaubaufe, Philipp M. Schindlerf, Frank Hauserg, Hailin Panb, Zhikai Yangb, Anton S. M. Sonnenbergh, Z. Wilhelm de Beeri, Yong Zhangb, Michael J. Wingfieldi, Cornelis J. P. Grimmelikhuijzeng, Ronald P. de Vriese, Judith Korbf,4, Duur K. Aanend, Jun Wangb,j, Jacobus J. Boomsmaa, and Guojie Zhanga,b,2 aCentre for Social Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark; bChina National Genebank, BGI-Shenzen, Shenzhen 518083, China; cCentre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, DK-1350 Copenhagen, Denmark; dLaboratory of Genetics, Wageningen University, 6708 PB, Wageningen, The Netherlands; eFungal Biodiversity Centre, Centraalbureau voor Schimmelcultures, Royal Netherlands Academy of Arts and Sciences, NL-3584 CT, Utrecht, The Netherlands; fBehavioral Biology, Fachbereich Biology/Chemistry, University of Osnabrück, D-49076 Osnabrück, Germany; gCenter for Functional and Comparative Insect Genomics, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark; hDepartment of Plant Breeding, Wageningen University and Research Centre, NL-6708 PB, Wageningen, The Netherlands; iDepartment of Microbiology, Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria SA-0083, South Africa; and jDepartment of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark Edited by Ian T. Baldwin, Max Planck Institute for Chemical Ecology, Jena, Germany, and approved August 15, 2014 (received for review October 24, 2013) Termites normally rely on gut symbionts to decompose organic levels-of-selection conflicts that need to be regulated (12). -
Peregrine and Saker Falcon Genome Sequences Provide Insights Into Evolution of a Predatory Lifestyle
LETTERS OPEN Peregrine and saker falcon genome sequences provide insights into evolution of a predatory lifestyle Xiangjiang Zhan1,7, Shengkai Pan2,7, Junyi Wang2,7, Andrew Dixon3, Jing He2, Margit G Muller4, Peixiang Ni2, Li Hu2, Yuan Liu2, Haolong Hou2, Yuanping Chen2, Jinquan Xia2, Qiong Luo2, Pengwei Xu2, Ying Chen2, Shengguang Liao2, Changchang Cao2, Shukun Gao2, Zhaobao Wang2, Zhen Yue2, Guoqing Li2, Ye Yin2, Nick C Fox3, Jun Wang5,6 & Michael W Bruford1 As top predators, falcons possess unique morphological, 16,263 genes were predicted for F. peregrines, and 16,204 were pre- physiological and behavioral adaptations that allow them to be dicted for F. cherrug (Supplementary Table 6 and Supplementary successful hunters: for example, the peregrine is renowned as Note). Approximately 92% of these genes were functionally annotated the world’s fastest animal. To examine the evolutionary basis of using homology-based methods (Supplementary Table 7). predatory adaptations, we sequenced the genomes of both the Comparative genome analysis was carried out to assess evolution peregrine (Falco peregrinus) and saker falcon (Falco cherrug), and innovation within falcons using related genomes with comparable All rights reserved. and we present parallel, genome-wide evidence for evolutionary assembly quality (Supplementary Table 8). Orthologous genes were innovation and selection for a predatory lifestyle. The genomes, identified in the chicken, zebra finch, turkey, peregrine and saker assembled using Illumina deep sequencing with greater than using the program TreeFam3. For the five genome-enabled avian spe- 100-fold coverage, are both approximately 1.2 Gb in length, cies, a maximum-likelihood phylogeny using 861,014 4-fold degen- with transcriptome-assisted prediction of approximately 16,200 erate sites from 6,267 single-copy orthologs confirmed that chicken America, Inc. -
Supplemental Material for Aggregated Dendrograms for Visual Comparison Between Many Phylogenetic Trees
Supplemental Material for Aggregated Dendrograms for Visual Comparison Between Many Phylogenetic Trees Zipeng Liu, Shing Hei Zhan, and Tamara Munzner December 3, 2018 Contents S1 View coordination details2 S2 Cluster AD gradient coloring4 S3 Algorithm details5 S3.1 AD layout function parameters..........................5 S3.2 Front-end caching for rendering ADs.......................6 S4 Expert Interview Study8 S4.1 Participants....................................8 S4.2 Interview Questions................................8 S5 Full Screenshots of Usage Scenario 1: 1KP pilot study 10 S5.1 Sister group of land plants............................ 10 S5.2 Early diversification of land plants........................ 15 S6 Usage scenario 2: TreeFam 17 S6.1 PROTOSTOMIA and DEUTEROSTOMIA ..................... 17 S6.2 ECDYSOZOA and LOPHOTROZOA ........................ 22 S7 Screenshots of Information Density Comparison 24 S8 Case Studies 27 S8.1 Full Screenshots of Case Study 1......................... 27 S8.2 Full Screenshots of Case Study 2......................... 28 S8.3 Case Study 3................................... 28 S9 Screenshots of Dataset Upload 31 1 S1 View coordination details Table S1 documents the view coordination discussed in Section 6.6 of the main paper, showing which aspect of the data is visually encoded across all five levels of detail for each of the eight views. The table has six columns since we break out branches from leaves for clarity; both of these are the lowest level of detail. We duplicate Figure 7 and put it here for a quick reference showing all of these views, as Figure S1. 2 View Level of detail (LoD) Tree collection Subset of trees Individual Subtree Branch & Leaf node tree its attributes Reference color text label & whole view line & tooltip Dendrogram background black dot Tree Distribution row segment in row Cluster AD whole view one cluster line or Individual AD one AD block collapsed Pairwise butterfly color text label & consensus tree line & tooltip comparison layout background black dot t-SNE Tree Similarity dot scatterplot Ref. -
Strategic Plan 2011-2016
Strategic Plan 2011-2016 Wellcome Trust Sanger Institute Strategic Plan 2011-2016 Mission The Wellcome Trust Sanger Institute uses genome sequences to advance understanding of the biology of humans and pathogens in order to improve human health. -i- Wellcome Trust Sanger Institute Strategic Plan 2011-2016 - ii - Wellcome Trust Sanger Institute Strategic Plan 2011-2016 CONTENTS Foreword ....................................................................................................................................1 Overview .....................................................................................................................................2 1. History and philosophy ............................................................................................................ 5 2. Organisation of the science ..................................................................................................... 5 3. Developments in the scientific portfolio ................................................................................... 7 4. Summary of the Scientific Programmes 2011 – 2016 .............................................................. 8 4.1 Cancer Genetics and Genomics ................................................................................ 8 4.2 Human Genetics ...................................................................................................... 10 4.3 Pathogen Variation .................................................................................................. 13 4.4 Malaria -
Omamer: Tree-Driven and Alignment-Free Protein Assignment to Subfamilies Outperforms Closest Sequence Approaches
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.30.068296; this version posted August 6, 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 4.0 International license. OMAmer: tree-driven and alignment-free protein assignment to subfamilies outperforms closest sequence approaches 4,5,* Victor Rossier1, 2,3, Alex Warwick Vesztrocy1, 2, 3, Marc Robinson-Rechavi and Christophe Dessimoz1, 2, 3, 5,6,* 1Department of Computational Biology, University of Lausanne, Switzerland; 2Center for Integrative Genomics, University of Lausanne, Switzerland; 3SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; 4Department of Ecology and Evolution, University of Lausanne, Switzerland; 5Department of Genetics, Evolution, and Environment, University College London, UK; 6Department of Computer Science, University College London, UK. *Corresponding authors: [email protected] & [email protected] Abstract Assigning new sequences to known protein Families and subFamilies is a prerequisite For many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking For the closest sequence in a reFerence database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subFamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inFerence is computationally expensive. -
A Hands-On Introduction to Querying Evolutionary
F1000Research 2019, 8:1822 Last updated: 22 JUL 2020 METHOD ARTICLE A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL [version 1; peer review: 1 approved, 2 approved with reservations] Ana Claudia Sima1-3, Christophe Dessimoz 2-6, Kurt Stockinger1, Monique Zahn-Zabal 2,3, Tarcisio Mendes de Farias 2-4,7 1ZHAW Zurich University of Applied Sciences, Winterthur, Zurich, Switzerland 2Department of Computational Biology, University of Lausanne, Lausanne, Vaud, Switzerland 3SIB Swiss Institute of Bioinformatics, Lausanne, Vaud, Switzerland 4Center for Integrative Genomics, University of Lausanne, Lausanne, Vaud, Switzerland 5Department of Computer Science, University College London, London, UK 6Department of Genetics, Evolution, and Environment, University College London, London, UK 7Department of Ecology and Evolution, University of Lausanne, Lausanne, Vaud, Switzerland First published: 29 Oct 2019, 8:1822 Open Peer Review v1 https://doi.org/10.12688/f1000research.21027.1 Latest published: 22 Jul 2020, 8:1822 https://doi.org/10.12688/f1000research.21027.2 Reviewer Status Abstract Invited Reviewers The increasing use of Semantic Web technologies in the life sciences, in 1 2 3 particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative version 2 analyses, combining information from multiple sources. However, analyzing (revision) evolutionary data in RDF is not trivial, due to the steep learning curve 22 Jul 2020 required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple version 1 sources that publish orthology information in RDF, namely: The 29 Oct 2019 report report report Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). -
Treefam: 2008 Update Jue Ruan1, Heng Li2, Zhongzhong Chen1, Avril Coghlan2, Lachlan James M
Published online 1 December 2007 Nucleic Acids Research, 2008, Vol. 36, Database issue D735–D740 doi:10.1093/nar/gkm1005 TreeFam: 2008 Update Jue Ruan1, Heng Li2, Zhongzhong Chen1, Avril Coghlan2, Lachlan James M. Coin3, Yiran Guo1, Jean-Karim He´ riche´ 2, Yafeng Hu1, Karsten Kristiansen4, Ruiqiang Li1,4, Tao Liu1, Alan Moses2, Junjie Qin1, Søren Vang5, Albert J. Vilella6, Abel Ureta-Vidal6, Lars Bolund1,7, Jun Wang1,4,7 and Richard Durbin2,* 1Beijing Institute of Genomics of the Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China, 2Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, 3Department of Epidemiology & Public Health, Imperial College, St Mary’s Campus, Norfolk Place, London W2 1PG, UK, 4Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, 5Research Unit for Molecular Medicine, Aarhus University Hospital and Faculty of Health Sciences, University of Aarhus, DK-8200 Aarhus N, Denmark, 6EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK and 7Institute of Human Genetics, University of Aarhus, DK-8000 Aarhus C, Denmark Received September 14, 2007; Revised October 21, 2007; Accepted October 23, 2007 ABSTRACT In his original definition of orthology, Fitch defined orthologues in terms of a phylogenetic tree of a gene TreeFam (http://www.treefam.org) was developed family (1). It has now been well established that analysis of to provide curated phylogenetic trees for all animal phylogenetic trees is a very accurate way to determine gene families, as well as orthologue and paralogue orthology (2,3), which led us to develop the TreeFam assignments. Release 4.0 of TreeFam contains database and accompanying website in 2005 (4). -
Kusakidb V1.0: a Novel Approach for Validation and Completeness of Protein
bioRxiv preprint doi: https://doi.org/10.1101/2020.11.09.373753; this version posted November 10, 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-NC 4.0 International license. 1 KusakiDB v1.0: a novel approach for validation and completeness of protein 2 orthologous groups 3 4 Andrea Ghelfi (AG)*1, Yasukazu Nakamura (YN) 2, Sachiko Isobe (SI) 1 5 1 Laboratory of Plant Genetics and Genomics, Department of Frontier Research and 6 Development, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan; 2 Genome 7 Informatics Laboratory, National Institute of Genetics, Research Organization of Information 8 and Systems, Mishima, Japan. 9 10 Summary: 11 Plants have quite a low coverage in the major protein databases despite their roughly 350,000 12 species. Moreover, the agricultural sector is one of the main categories in bioeconomy. In 13 order to manipulate and/or engineer plant-based products, it is important to understand the 14 essential fabric of an organism, its proteins. Therefore, we created KusakiDB, which is a 15 database of orthologous proteins, in plants, that correlates three major databases, OrthoDB, 16 UniProt and RefSeq. KusakiDB has an orthologs assessment and management tools in order 17 to compare orthologous groups, which can provide insights not only under an evolutionary 18 point of view but also evaluate structural gene prediction quality and completeness among 19 plant species. KusakiDB could be a new approach to reduce error propagation of functional 20 annotation in plant species. -
Regulatory Evolution in Proteins by Turnover and Lineage-Specific Changes of Cyclin-Dependent Kinase Consensus Sites
Regulatory evolution in proteins by turnover and lineage-specific changes of cyclin-dependent kinase consensus sites Alan M. Moses*†, Muluye E. Liku‡, Joachim J. Li§, and Richard Durbin* *Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1HH, United Kingdom; and Departments of ‡Biochemistry and §Microbiology and Immunology, University of California, San Francisco, CA 94143 Edited by Philip P. Green, University of Washington School of Medicine, Seattle, WA, and approved September 25, 2007 (received for review February 6, 2007) Evolutionary change in gene regulation is a key mechanism un- after the initiation of DNA replication, to ensure that a single derlying the genetic component of organismal diversity. Here, we round of DNA replication occurs in each eukaryotic cell cycle, study evolution of regulation at the posttranslational level by a subset of the DNA replication machinery (the pre-RC) is examining the evolution of cyclin-dependent kinase (CDK) consen- directly inhibited by cyclin-dependent kinase (CDK) (17, 18). sus phosphorylation sites in the protein subunits of the pre- Here, we examine the evolution of regulation of the pre-RC replicative complex (RC). The pre-RC, an assembly of proteins by CDKs. Several features of this system make it attractive for formed during an early stage of DNA replication, is believed to be evolutionary analysis. First, the pre-RC proteins are found in regulated by CDKs throughout the animals and fungi. Interest- single copy in many animals and fungi (17), so it is relatively easy ingly, although orthologous pre-RC components often contain to identify their orthologs in most species. Also, human CDKs clusters of CDK consensus sites, the positions and numbers of sites have been shown to rescue yeast CDK mutations (19, 20), do not seem conserved. -
Phylogenetics – a Primer
Phylogenetics – a primer [email protected] What this primer can and can’t do Alice: "Would you tell me, please, which way I ought to go from here?" Cat: "That depends a good deal on where you want to get to,” Alice: "I don't much care where –” Cat: "Then it doesn't matter which way you go” Alice: "– so long as I get somewhere,” Cat: "Oh, you're sure to do that, if you only walk long enough." How do you get to where you want to be? What this primer can and can’t do “No wise fish would go anywhere without a porpoise.” Bioinformatics is not a good subject for passive learning. Learn some basic scripting. Go solve your own problems. If you get stuck (badly): Google, Biostars, SeqAnswers, StackOverflow If you get stuck (really badly): ask somebody What’s happening? 1. Reading 1.5 What’s the point? 2. Writing ? A tree evolutionarysimilarity A tree is a representation of relationships Testing the “Oasis hypothesis (OH)” OH: “Our music is totally different from Blur!” 1. Go to Youtube and copy links of Oasis song, Blur songs, and some others. • She’s electric (Oasis) • Country House (Blur) • Sunny afternoon (The Kinks) • My favourite things (Julie Andrews) • Who let the dogs out? (Baha Men) 2. Convert to .mp3 (http://www.youtube-mp3.org/) 3. Convert to .wav (http://media.io) 4. Cut out random 10s fragment (Audacity) Testing the “Oasis hypothesis (OH)” • Use R packages “tuneR” and “seewave” for amplitude modulation analysis • A recipe can be found here: http://www.vesnam.com/Rblog/sortmymusic/ • You end up with a distance (similarity) -
A Curated Ortholog Database for Yeasts and Fungi Spanning 600 Million Years of Evolution
bioRxiv preprint doi: https://doi.org/10.1101/237974; this version posted October 8, 2018. 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. AYbRAH: a curated ortholog database for yeasts and fungi spanning 600 million years of evolution Kevin Correia1, Shi M. Yu1, and Radhakrishnan Mahadevan1,2,* 1Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada, ON 2Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada Corresponding author: Radhakrishnan Mahadevan∗ Email address: [email protected] ABSTRACT Budding yeasts inhabit a range of environments by exploiting various metabolic traits. The genetic bases for these traits are mostly unknown, preventing their addition or removal in a chassis organism for metabolic engineering. To help understand the molecular evolution of these traits in yeasts, we created Analyzing Yeasts by Reconstructing Ancestry of Homologs (AYbRAH), an open-source database of predicted and manually curated ortholog groups for 33 diverse fungi and yeasts in Dikarya, spanning 600 million years of evolution. OrthoMCL and OrthoDB were used to cluster protein sequence into ortholog and homolog groups, respectively; MAFFT and PhyML were used to reconstruct the phylogeny of all homolog groups. Ortholog assignments for enzymes and small metabolite transporters were compared to their phylogenetic reconstruction, and curated to resolve any discrepancies. Information on homolog and ortholog groups can be viewed in the AYbRAH web portal (https://kcorreia.github. io/aybrah/) to review ortholog groups, predictions for mitochondrial localization and transmembrane domains, literature references, and phylogenetic reconstructions.