Phylogenetic Profiling of the Arabidopsis Thaliana Proteome
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Maternally Inherited Pirnas Direct Transient Heterochromatin Formation
RESEARCH ARTICLE Maternally inherited piRNAs direct transient heterochromatin formation at active transposons during early Drosophila embryogenesis Martin H Fabry, Federica A Falconio, Fadwa Joud, Emily K Lythgoe, Benjamin Czech*, Gregory J Hannon* CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom Abstract The PIWI-interacting RNA (piRNA) pathway controls transposon expression in animal germ cells, thereby ensuring genome stability over generations. In Drosophila, piRNAs are intergenerationally inherited through the maternal lineage, and this has demonstrated importance in the specification of piRNA source loci and in silencing of I- and P-elements in the germ cells of daughters. Maternally inherited Piwi protein enters somatic nuclei in early embryos prior to zygotic genome activation and persists therein for roughly half of the time required to complete embryonic development. To investigate the role of the piRNA pathway in the embryonic soma, we created a conditionally unstable Piwi protein. This enabled maternally deposited Piwi to be cleared from newly laid embryos within 30 min and well ahead of the activation of zygotic transcription. Examination of RNA and protein profiles over time, and correlation with patterns of H3K9me3 deposition, suggests a role for maternally deposited Piwi in attenuating zygotic transposon expression in somatic cells of the developing embryo. In particular, robust deposition of piRNAs targeting roo, an element whose expression is mainly restricted to embryonic development, results in the deposition of transient heterochromatic marks at active roo insertions. We hypothesize that *For correspondence: roo, an extremely successful mobile element, may have adopted a lifestyle of expression in the [email protected] embryonic soma to evade silencing in germ cells. -
"Phylogenetic Profiling"
Introductory Review Phylogenetic p rofiling Matteo Pellegrini, Todd O. Yeates and David Eisenberg Howard Hughes Medical Institute, University of California at Los Angeles, Los Angeles, CA, USA Sorel T. Fitz-Gibbon IGPP Center for Astrobiology, University of California at Los Angeles, Los Angeles, CA, USA 1. Introduction Biology has been profoundly changed by the development of techniques to se- quence DNA. The advent of rapid sequencing in conjunction with the capability to assemble sequence fragments into complete genome sequences enables researchers to read and analyze entire genomes of organisms. Parallel progress has been made in algorithms to study the evolutionary history of proteins. The techniques rely on the ability to measure the similarity of protein sequences in order to determine the likelihood that different proteins are descended from a common ancestor. It is therefore possible to reconstruct families of proteins that share a common ancestor. Combining these two capabilities, we can now not only determine which proteins are coded within an organism’s genome but we can also discover the evolutionary relationships between the proteins of multiple organisms. Phylogenetic profiling is the study of which protein types are found in which organisms. In order to perform phylogenetic profiling, one must first establish a classification of proteins into families. An example of such a classification scheme across a broad range of fully sequenced organisms is the Clusters of Orthologous Groups (Tatusov, 1997), where an attempt is made to group together proteins that perform a similar function. Next, each organism is described in terms of which protein families are coded or not coded in its genome. -
Pellegrini M. Using Phylogenetic Profiles To
Chapter 9 Using Phylogenetic Profiles to Predict Functional Relationships Matteo Pellegrini Abstract Phylogenetic profiling involves the comparison of phylogenetic data across gene families. It is possible to construct phylogenetic trees, or related data structures, for specific gene families using a wide variety of tools and approaches. Phylogenetic profiling involves the comparison of this data to determine which families have correlated or coupled evolution. The underlying assumption is that in certain cases these couplings may allow us to infer that the two families are functionally related: that is their function in the cell is coupled. Although this technique can be applied to noncoding genes, it is more commonly used to assess the function of protein coding genes. Examples of proteins that are functionally related include subunits of protein complexes, or enzymes that perform consecutive steps along biochemical pathways. We hypothesize the deletion of one of the families from a genome would then indirectly affect the function of the other. Dozens of different implementations of the phylogenetic profiling technique have been developed over the past decade. These range from the first simple approaches that describe phylogenetic profiles as binary vectors to the most complex ones that attempt to model to the coevolution of protein families on a phylogenetic tree. We discuss a set of these implementations and present the software and databases that are available to perform phylogenetic profiling. Key words: Phylogenetic profiles, Coevolution, Functional associations, Comparative genomics, Coevolving proteins 1. Introduction The remarkable improvements in sequencing technology that have occurred over the past few decades have made the sequenc- ing of genomes an ever more routine task. -
The Biogrid Interaction Database
D470–D478 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 26 November 2014 doi: 10.1093/nar/gku1204 The BioGRID interaction database: 2015 update Andrew Chatr-aryamontri1, Bobby-Joe Breitkreutz2, Rose Oughtred3, Lorrie Boucher2, Sven Heinicke3, Daici Chen1, Chris Stark2, Ashton Breitkreutz2, Nadine Kolas2, Lara O’Donnell2, Teresa Reguly2, Julie Nixon4, Lindsay Ramage4, Andrew Winter4, Adnane Sellam5, Christie Chang3, Jodi Hirschman3, Chandra Theesfeld3, Jennifer Rust3, Michael S. Livstone3, Kara Dolinski3 and Mike Tyers1,2,4,* 1Institute for Research in Immunology and Cancer, Universite´ de Montreal,´ Montreal,´ Quebec H3C 3J7, Canada, 2The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA, 4School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK and 5Centre Hospitalier de l’UniversiteLaval´ (CHUL), Quebec,´ Quebec´ G1V 4G2, Canada Received September 26, 2014; Revised November 4, 2014; Accepted November 5, 2014 ABSTRACT semi-automated text-mining approaches, and to en- hance curation quality control. The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein in- INTRODUCTION teractions curated from the primary biomedical lit- Massive increases in high-throughput DNA sequencing erature for all major model organism species and technologies (1) have enabled an unprecedented level of humans. As of September 2014, the BioGRID con- genome annotation for many hundreds of species (2–6), tains 749 912 interactions as drawn from 43 149 pub- which has led to tremendous progress in the understand- lications that represent 30 model organisms. -
Flybase: Updates to the Drosophila Melanogaster Knowledge Base Aoife Larkin 1,*, Steven J
Published online 21 November 2020 Nucleic Acids Research, 2021, Vol. 49, Database issue D899–D907 doi: 10.1093/nar/gkaa1026 FlyBase: updates to the Drosophila melanogaster knowledge base Aoife Larkin 1,*, Steven J. Marygold 1, Giulia Antonazzo1, Helen Attrill 1, Gilberto dos Santos2, Phani V. Garapati1, Joshua L. Goodman3,L.SianGramates 2, Gillian Millburn1, Victor B. Strelets3, Christopher J. Tabone2, Jim Thurmond3 and FlyBase Consortium† 1 Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge Downloaded from https://academic.oup.com/nar/article/49/D1/D899/5997437 by guest on 15 January 2021 CB2 3DY, UK, 2The Biological Laboratories, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA and 3Department of Biology, Indiana University, Bloomington, IN 47405, USA Received September 15, 2020; Revised October 13, 2020; Editorial Decision October 14, 2020; Accepted October 22, 2020 ABSTRACT In this paper, we highlight a variety of new features and improvements that have been integrated into FlyBase since FlyBase (flybase.org) is an essential online database our last review (3). We have introduced a number of new fea- for researchers using Drosophila melanogaster as a tures that allow users to more easily find and use the data in model organism, facilitating access to a diverse ar- which they are interested; these include customizable tables, ray of information that includes genetic, molecular, improved searching using expanded Experimental Tool Re- genomic and reagent resources. Here, we describe ports, more links to and from other resources, and bet- the introduction of several new features at FlyBase, ter provision of precomputed bulk files. We have upgraded including Pathway Reports, paralog information, dis- tools such as Fast-Track Your Paper and ID validator. -
Integrating Genomic Data to Predict Transcription Factor Binding
Integrating Genomic Data to Predict Transcription Factor Binding Dustin T. Holloway 1 Mark Kon 2 Charles DeLisi 3 [email protected] [email protected] [email protected] 1 Molecular Biology Cell Biology and Biochemistry, 3 Boston University, Boston, MA 02215, U.S.A. Bioinformatics and Systems Biology, 2 Boston University, Boston, MA 02215, U.S.A. Department of Mathematics and Statistics, Boston University, Boston, MA 02215 , U.S.A. Abstract Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor’s binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes. -
Scalable Phylogenetic Profiling Using Minhash Uncovers Likely Eukaryotic Sexual Reproduction Genes
bioRxiv preprint doi: https://doi.org/10.1101/852491; this version posted November 22, 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. 1 Scalable Phylogenetic Profiling using MinHash Uncovers Likely Eukaryotic Sexual Reproduction Genes 1,2,3,* 1,2,3 4,5 1,2,3,6,7,* David Moi , Laurent Kilchoer , Pablo S. Aguilar and Christophe Dessimoz 1 2 Department of Computational Biology, University of Lausanne, Switzerland; Center for Integrative Genomics, 3 4 University of Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Instituto de 5 Investigaciones Biotecnologicas (IIBIO), Universidad Nacional de San Martín Buenos Aires, Argentina; Instituto de 6 Fisiología, Biología Molecular y Neurociencias (IFIBYNE-CONICET), Department of Genetics, Evolution, and 7 Environment, University College London, UK; Department of Computer Science, University College London, UK. *Corresponding authors: [email protected] and [email protected] Abstract Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require quadratic time or worse in the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. -
Review Article Protein-Protein Interaction Detection: Methods and Analysis
Hindawi Publishing Corporation International Journal of Proteomics Volume 2014, Article ID 147648, 12 pages http://dx.doi.org/10.1155/2014/147648 Review Article Protein-Protein Interaction Detection: Methods and Analysis V. Srinivasa Rao,1 K. Srinivas,1 G. N. Sujini,2 and G. N. Sunand Kumar1 1 Department of CSE, VR Siddhartha Engineering College, Vijayawada 520007, India 2 Department of CSE, Mahatma Gandhi Institute of Technology, Hyderabad 500075, India Correspondence should be addressed to V. Srinivasa Rao; [email protected] Received 26 October 2013; Revised 5 December 2013; Accepted 20 December 2013; Published 17 February 2014 Academic Editor: Yaoqi Zhou Copyright © 2014 V. Srinivasa Rao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. -
Mapping Global and Local Co-Evolution Across 600 Species to Identify Novel Homologous Recombination Repair Genes
Downloaded from genome.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press Mapping global and local co-evolution across 600 species to identify novel homologous recombination repair genes Dana Sherill-Rofe1*, Dolev Rahat1,2*, Steven Findlay3,4,#, Anna Mellul1,#, Irene Guberman1, Maya Braun1, Idit Bloch1, Alon Lalezari1, Arash Samiei 3,4, Ruslan Sadreyev5,6,7, Michal 8 3,4,9,10 2,† 1,†, Goldberg , Alexandre Orthwein , Aviad Zick and Yuval Tabach 1Department of Developmental Biology and Cancer Research, Institute for Medical Research- Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel. 2Sharett Institute of Oncology, Hadassah Medical Center, Ein-Kerem, Jerusalem, Israel. 3Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Quebec, Canada. 4Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada. 5Department of Molecular Biology, Massachusetts General Hospital, Boston, MA USA. 6Department of Genetics, Harvard Medical School, Boston, MA USA. 7Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA. 8Department of Genetics, Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem, Israel. 9Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada. 10Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada. *These authors contributed equally to this work. # These authors contributed equally to this work. -
Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by Pickeringhutchins Ltd
European Bioinformatics Institute EMBL-EBI Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by PickeringHutchins Ltd www.pickeringhutchins.com EMBL member states: Austria, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom. Associate member state: Australia EMBL-EBI is a part of the European Molecular Biology Laboratory (EMBL) EMBL-EBI EMBL-EBI EMBL-EBI EMBL-European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton Cambridge CB10 1SD United Kingdom Tel. +44 (0)1223 494 444, Fax +44 (0)1223 494 468 www.ebi.ac.uk EMBL Heidelberg Meyerhofstraße 1 69117 Heidelberg Germany Tel. +49 (0)6221 3870, Fax +49 (0)6221 387 8306 www.embl.org [email protected] EMBL Grenoble 6, rue Jules Horowitz, BP181 38042 Grenoble, Cedex 9 France Tel. +33 (0)476 20 7269, Fax +33 (0)476 20 2199 EMBL Hamburg c/o DESY Notkestraße 85 22603 Hamburg Germany Tel. +49 (0)4089 902 110, Fax +49 (0)4089 902 149 EMBL Monterotondo Adriano Buzzati-Traverso Campus Via Ramarini, 32 00015 Monterotondo (Rome) Italy Tel. +39 (0)6900 91402, Fax +39 (0)6900 91406 © 2012 EMBL-European Bioinformatics Institute All texts written by EBI-EMBL Group and Team Leaders. This publication was produced by the EBI’s Outreach and Training Programme. Contents Introduction Foreword 2 Major Achievements 2011 4 Services Rolf Apweiler and Ewan Birney: Protein and nucleotide data 10 Guy Cochrane: The European Nucleotide Archive 14 Paul Flicek: -
The Drosophila Melanogaster Transcriptome by Paired-End RNA Sequencing
Downloaded from genome.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Resource The Drosophila melanogaster transcriptome by paired-end RNA sequencing Bryce Daines,1,2,6 Hui Wang,2,6 Liguo Wang,3,6 Yumei Li,1,2 Yi Han,2 David Emmert,4 William Gelbart,4 Xia Wang,1,2 Wei Li,3,5 Richard Gibbs,1,2,7 and Rui Chen1,2,7 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA; 2Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; 3Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA; 4Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA; 5Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA RNA-seq was used to generate an extensive map of the Drosophila melanogaster transcriptome by broad sampling of 10 developmental stages. In total, 142.2 million uniquely mapped 64–100-bp paired-end reads were generated on the Illumina GA II yielding 3563 sequencing coverage. More than 95% of FlyBase genes and 90% of splicing junctions were observed. Modifications to 30% of FlyBase gene models were made by extension of untranslated regions, inclusion of novel exons, and identification of novel splicing events. A total of 319 novel transcripts were identified, representing a 2% increase over the current annotation. Alternate splicing was observed in 31% of D. melanogaster genes, a 38% increase over previous estimations, but significantly less than that observed in higher organisms. Much of this splicing is subtle such as tandem alternate splice sites. -
Rnacentral: Progress in the Development of the Non-Coding RNA Sequence Database
RNAcentral: Progress in the Development of the Non-coding RNA Sequence Database Other potential titles: ● RNAcentral: New Developments in the Non-coding RNA Sequence Database ● RNAcentral: Three Years of ncRNA Data Integration Anton I. Petrov1, Blake A. Sweeney1, Boris Burkov1*, Simon Kay1, Rob Finn1, Alex Bateman1, and The RNAcentral Consortium European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD *To whom correspondence should be addressed: [email protected] BACKGROUND More than fifty major specialised resources exist in the area of non-coding RNA (ncRNA) research. RNAcentral (http://rnacentral.org) provides a unified interface that aggregates information from over twenty of them, including: - larger databases (e.g. RefSeq, ENA, Ensembl!) - databases with specific content type (e.g. Rfam, PDBe) - RNA type-specific databases (e.g. miRBase, GtRNAdb, snoPY) - organism-specific databases (e.g. HGNC, FlyBase, PomBase). RESULTS Launched in 2014 [2], RNAcentral currently contains over 10 million ncRNA sequences from more than 20 RNA databases and Is updated several times a year. Recent updates include ncRNA data from: - HGNC - Ensembl - FlyBase - Rfam (most of sequences in RNAcentral are annotated with Rfam families, while the rest are candidates for producing new Rfam families) There are three main ways of browsing the data through the RNAcentral website. - The text search makes it easy to explore all ncRNA sequences, compare data across different resources, and discover what is known about each ncRNA. - Using the sequence similarity search one can search data from multiple RNA databases starting from a sequence. - Finally, one can explore ncRNAs in select species by genomic location using an integrated genome browser.