Behavior-Dependent Cis Regulation Reveals Genes and Pathways Associated with Bower Building in Cichlid Fishes
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1 Exon Probe Sets and Bioinformatics Pipelines for All Levels of Fish Phylogenomics
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.18.949735; this version posted February 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Exon probe sets and bioinformatics pipelines for all levels of fish phylogenomics 2 3 Lily C. Hughes1,2,3,*, Guillermo Ortí1,3, Hadeel Saad1, Chenhong Li4, William T. White5, Carole 4 C. Baldwin3, Keith A. Crandall1,2, Dahiana Arcila3,6,7, and Ricardo Betancur-R.7 5 6 1 Department of Biological Sciences, George Washington University, Washington, D.C., U.S.A. 7 2 Computational Biology Institute, Milken Institute of Public Health, George Washington 8 University, Washington, D.C., U.S.A. 9 3 Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian 10 Institution, Washington, D.C., U.S.A. 11 4 College of Fisheries and Life Sciences, Shanghai Ocean University, Shanghai, China 12 5 CSIRO Australian National Fish Collection, National Research Collections of Australia, 13 Hobart, TAS, Australia 14 6 Sam Noble Oklahoma Museum of Natural History, Norman, O.K., U.S.A. 15 7 Department of Biology, University of Oklahoma, Norman, O.K., U.S.A. 16 17 *Corresponding author: Lily C. Hughes, [email protected]. 18 Current address: Department of Organismal Biology and Anatomy, University of Chicago, 19 Chicago, IL. 20 21 Keywords: Actinopterygii, Protein coding, Systematics, Phylogenetics, Evolution, Target 22 capture 23 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.18.949735; this version posted February 19, 2020. -
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. -
Automated Measurement of Long-Term Bower Behaviors in Lake Malawi
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.27.968511; this version posted February 28, 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. 1 1 Automated measurement of long-term bower behaviors in Lake Malawi cichlids using 2 depth sensing and action recognition 3 4 Zachary V Johnson1, Lijiang Long1,2, Junyu Li1, Manu Tej Sharma Arrojwala1, Vineeth Aljapur1, 5 Tyrone Lee1, Mark C Lowder1, Karen Gu1, Tucker J Lancaster1,2, Joseph I Stockert1, Jean M 6 Moorman3, Rachel L Lecesne4, Jeffrey T Streelman# 1,2,3, and Patrick T McGrath# 1,2,3,5 7 8 1School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA 9 2Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, 10 Atlanta, GA 30332, USA 11 3Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, 12 Atlanta, GA 30332, USA 13 4School of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA 14 5School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA 15 16 #Co-corresponding authors: [email protected] (P.T.M.), 17 [email protected] (J.T.S.) 18 19 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.27.968511; this version posted February 28, 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. -
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 -
Furf Handout 2016.Pdf
0 College of Science – Fall Undergraduate Research Fair 2016 Welcome! The purpose of this event is to provide science students with an opportunity to get many of their questions answered about undergraduate research. Not only about how to get more involved in research, but also how to get more out of the research experience itself. Throughout and beyond the College of Science there are many different ways in which students can get involved in research. Often it’s just a question of looking in the right places and being persistent in the hunt for the right opportunity. However, getting the right opportunity is also about getting as much information as possible from a diversity of sources. This could be as simple as a fellow student but there are many organizations, institutes, and centers on campus that are also more than willing to help a student find and support their research endeavors. Furthermore, there are many ways for students to get even more out of their research experience, through publishing and presenting their research to their peers. Through a combination of listening to speakers, poster presenters, and representatives from various institutions, students should be able to get some ideas about how best to get started looking for research opportunities. Also, students should be able to see how they can add value to their research experience by participating in other related activities. The sooner a student begins the search, the sooner they will be able to start participating in undergraduate research and getting the most -
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). -
Supplemental Methods
Supplemental Methods Collection and Preparation After extraction, the DNA samples were sheared with a 1.5 blunt end needles (Jensen Global, Santa Barbara, CA, USA) and run through pulse field gel electrophoresis, in order to separate large DNA molecules, for 16 hours. Samples were checked with a spectrophotometer (Synergy H1 Hybrid Reader, BioTek Instruments Inc., Winooski, VT) and Qubit® fluorometer for quantification of our genomic DNA. Illumina and Pacbio Hybrid Assembly, Genome Size Estimation, and Quality Assessment All computational and bioinformatics analyses were conducted on the High Performance Computing (HPC) Cluster located that University of California, Irvine. Sequence data generated from two lanes of Illumina HiSeq 2500 were concatenated and raw sequence reads were assembled through PLATANUS v1.2.1 (1), which accounts for heterozygous diploid sequence data. Parameters used for PLATANUS was -m 256 (memory) and -t 48 (threads) for this initial assembly. Afterwards, contigs assembled from PLATANUS and reads from 40 SMRT cells of PacBio sequencing were assembled with a hybrid assembler DBG2OLC v1.0 (2). We used the following parameters in DBG2OLC: k 17 KmerCovTh 2 MinOverlap 20 AdaptiveTh 0.01 LD1 0 and RemoveChimera 1 and ran pbdagcon with default parameters. Without Illumina sequence reads, we also conducted a PacBio reads only assembly with FALCON v0.3.0 (https://github.com/PacificBiosciences/FALCON) with default parameters in order to assemble PacBio reads into contiguous sequences. The parameters we used were input type = raw, length_cutoff = 4000, length_cutoff_pr = 8000, with different cluster settings 32, 16, 32, 8, 64, and 32 cores, concurrency setting jobs were 32, and the remaining were default parameters. -
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. -
Behavioral Evolution Contributes to Hindbrain Diversification Among Lake Malawi Cichlid Fish
www.nature.com/scientificreports OPEN Behavioral evolution contributes to hindbrain diversifcation among Lake Malawi cichlid fsh Ryan A. York1,2*, Allie Byrne1, Kawther Abdilleh3, Chinar Patil3, Todd Streelman3, Thomas E. Finger4,5 & Russell D. Fernald1,6 The evolutionary diversifcation of animal behavior is often associated with changes in the structure and function of nervous systems. Such evolutionary changes arise either through alterations of individual neural components (“mosaically”) or through scaling of the whole brain (“concertedly”). Here we show that the evolution of a courtship behavior in Malawi cichlid fsh is associated with rapid, extensive, and specifc diversifcation of orosensory, gustatory centers in the hindbrain. We fnd that hindbrain volume varies signifcantly between species that build pit (depression) compared to castle (mound) type bowers and that this trait is evolving rapidly among castle-building species. Molecular analyses of neural activity via immediate early gene expression indicate a functional role for hindbrain structures during bower building. Finally, comparisons of bower building species in neighboring Lake Tanganyika suggest parallel patterns of neural diversifcation to those in Lake Malawi. Our results suggest that mosaic brain evolution via alterations to individual brain structures is more extensive and predictable than previously appreciated. Animal behaviors vary widely, as do their neural phenotypes1. Evolutionary neuroscience identifes how the brain diversifes over time and space in response to selective pressures2. A key goal of evolutionary neuroscience has been to identify whether brain structures evolve independently (“mosaically”) or in tandem with each other as they refect key life history traits, especially behavior3–6. While a number of studies have linked variation in brain structure with other traits across evolutionary time2,7–9, it remains unclear whether or not this variation is predict- able.