Comparative Genomics for Reliable Protein-Function Prediction from Genomic Data
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Chapter 14: Functional Genomics Learning Objectives
Chapter 14: Functional Genomics Learning objectives Upon reading this chapter, you should be able to: ■ define functional genomics; ■ describe the key features of eight model organisms; ■ explain techniques of forward and reverse genetics; ■ discuss the relation between the central dogma and functional genomics; and ■ describe proteomics-based approaches to functional genomics. Outline : Functional genomics Introduction Relation between genotype and phenotype Eight model organisms E. coli; yeast; Arabidopsis; C. elegans; Drosophila; zebrafish; mouse; human Functional genomics using reverse and forward genetics Reverse genetics: mouse knockouts; yeast; gene trapping; insertional mutatgenesis; gene silencing Forward genetics: chemical mutagenesis Functional genomics and the central dogma Approaches to function; Functional genomics and DNA; …and RNA; …and protein Proteomic approaches to functional genomics CASP; protein-protein interactions; protein networks Perspective Albert Blakeslee (1874–1954) studied the effect of altered chromosome numbers on the phenotype of the jimson-weed Datura stramonium, a flowering plant. Introduction: Functional genomics Functional genomics is the genome-wide study of the function of DNA (including both genes and non-genic regions), as well as RNA and proteins encoded by DNA. The term “functional genomics” may apply to • the genome, transcriptome, or proteome • the use of high-throughput screens • the perturbation of gene function • the complex relationship of genotype and phenotype Functional genomics approaches to high throughput analyses Relationship between genotype and phenotype The genotype of an individual consists of the DNA that comprises the organism. The phenotype is the outward manifestation in terms of properties such as size, shape, movement, and physiology. We can consider the phenotype of a cell (e.g., a precursor cell may develop into a brain cell or liver cell) or the phenotype of an organism (e.g., a person may have a disease phenotype such as sickle‐cell anemia). -
Masterpath: Network Analysis of Functional Genomics Screening Data
MasterPATH: network analysis of functional genomics screening data Natalia Rubanova, Guillaume Pinna, Jeremie Kropp, Anna Campalans, Juan Pablo Radicella, Anna Polesskaya, Annick Harel-Bellan, Nadya Morozova To cite this version: Natalia Rubanova, Guillaume Pinna, Jeremie Kropp, Anna Campalans, Juan Pablo Radicella, et al.. MasterPATH: network analysis of functional genomics screening data. BMC Genomics, BioMed Central, 2020, 21 (1), pp.632. 10.1186/s12864-020-07047-2. hal-02944249 HAL Id: hal-02944249 https://hal.archives-ouvertes.fr/hal-02944249 Submitted on 26 Nov 2020 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. Rubanova et al. BMC Genomics (2020) 21:632 https://doi.org/10.1186/s12864-020-07047-2 METHODOLOGY ARTICLE Open Access MasterPATH: network analysis of functional genomics screening data Natalia Rubanova1,2,3* , Guillaume Pinna4, Jeremie Kropp1, Anna Campalans5,6,7, Juan Pablo Radicella5,6,7, Anna Polesskaya8, Annick Harel-Bellan1 and Nadya Morozova1,4 Abstract Background: Functional genomics employs several experimental approaches to investigate gene functions. High- throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). -
The Economic Impact and Functional Applications of Human Genetics and Genomics
The Economic Impact and Functional Applications of Human Genetics and Genomics Commissioned by the American Society of Human Genetics Produced by TEConomy Partners, LLC. Report Authors: Simon Tripp and Martin Grueber May 2021 TEConomy Partners, LLC (TEConomy) endeavors at all times to produce work of the highest quality, consistent with our contract commitments. However, because of the research and/or experimental nature of this work, the client undertakes the sole responsibility for the consequence of any use or misuse of, or inability to use, any information or result obtained from TEConomy, and TEConomy, its partners, or employees have no legal liability for the accuracy, adequacy, or efficacy thereof. Acknowledgements ASHG and the project authors wish to thank the following organizations for their generous support of this study. Invitae Corporation, San Francisco, CA Regeneron Pharmaceuticals, Inc., Tarrytown, NY The project authors express their sincere appreciation to the following indi- viduals who provided their advice and input to this project. ASHG Government and Public Advocacy Committee Lynn B. Jorde, PhD ASHG Government and Public Advocacy Committee (GPAC) Chair, President (2011) Professor and Chair of Human Genetics George and Dolores Eccles Institute of Human Genetics University of Utah School of Medicine Katrina Goddard, PhD ASHG GPAC Incoming Chair, Board of Directors (2018-2020) Distinguished Investigator, Associate Director, Science Programs Kaiser Permanente Northwest Melinda Aldrich, PhD, MPH Associate Professor, Department of Medicine, Division of Genetic Medicine Vanderbilt University Medical Center Wendy Chung, MD, PhD Professor of Pediatrics in Medicine and Director, Clinical Cancer Genetics Columbia University Mira Irons, MD Chief Health and Science Officer American Medical Association Peng Jin, PhD Professor and Chair, Department of Human Genetics Emory University Allison McCague, PhD Science Policy Analyst, Policy and Program Analysis Branch National Human Genome Research Institute Rebecca Meyer-Schuman, MS Human Genetics Ph.D. -
Human Functional Genomics Project Begins Unraveling Links Between
Human Functional Genomics Project Begins Unraveling Links Between Genes, Immune Response Jul 28, 2016 | Elizabeth Newbern Premium NEW YORK (GenomeWeb) – Earlier this month, researchers involved in the Human Functional Genomics Project (HFGP) published data on the relationship between genetics and human cytokine response in Nature Medicine. The data was derived from a first cohort, the 200 Functional Genomics project, and the researchers plan to publish more data from this cohort in Nature Medicine in August. In addition, the HFGP just finished collecting patient data from a second cohort made up of 500 healthy individuals from Western Europe and aptly named the 500 Functional Genomics project, late last year. The HFGP researchers also recently finished collecting data from their LifeLines-deep cohort, made up of 1,600 healthy individuals from Western Europe. The researchers are assessing and analyzing the 500 FG and LifeLines-deep data now and hope to publish preliminary results at the end of this year, Mihai Netea, member of the HFGP's scientific advisory board and professor of the Radboud Center for Infectious Diseases (RCI) in the Netherlands, told GenomeWeb. They also finished recruitment and started data collection for their 300 Functional Genomics project, which is a cohort of 300 obese individuals, he added. The human immune system is composed of a complex network of organs, tissues, cells, and molecules and has evolved to both protect the host against infections and to play roles in immune surveillance. However, it has previously been unclear what role genetics plays in influencing cytokine responses in health and disease. The HFGP researchers aim to find out more about the role of genetics in immune response by characterizing and better understanding variation in human response through the use of various omics analyses — genomic, transcriptomic, metabolomic, and microbiomic — and in-depth functional phenotyping. -
Model-Based Integration of Genomics and Metabolomics Reveals SNP Functionality in Mycobacterium Tuberculosis
Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis Ove Øyåsa,b,1, Sonia Borrellc,d,1, Andrej Traunerc,d,1, Michael Zimmermanne, Julia Feldmannc,d, Thomas Liphardta,b, Sebastien Gagneuxc,d, Jörg Stellinga,b, Uwe Sauere, and Mattia Zampierie,2 aDepartment of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; bSIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; cDepartment of Medical Parasitoloy and Infection Biology, Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; dUniversity of Basel, 4058 Basel, Switzerland; and eInstitute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland Edited by Ralph R. Isberg, Tufts University School of Medicine, Boston, MA, and approved March 2, 2020 (received for review September 12, 2019) Human tuberculosis is caused by members of the Mycobacterium infection of macrophages (29–32). Beyond analyses of individual tuberculosis complex (MTBC) that vary in virulence and transmis- laboratory strains, however, no systematic characterization and sibility. While genome-wide association studies have uncovered comparative analysis of intrinsic metabolic differences across several mutations conferring drug resistance, much less is known human-adapted MTBC clinical strains has been performed. about the factors underlying other bacterial phenotypes. Variation If the metabolic and other phenotypic diversity between in the outcome of tuberculosis infection and diseases has been MTBC strains contributes to and modulates pathogenicity, an attributed primarily to patient and environmental factors, but obvious question is: Which elements of the limited genetic di- recent evidence indicates an additional role for the genetic diver- versity in the MTBC are responsible for phenotypic strain di- sity among MTBC clinical strains. -
High-Throughput Automated Microfluidic Sample Preparation for Accurate Microbial Genomics
High-throughput automated microfluidic sample preparation for accurate microbial genomics The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Kim, Soohong et al. “High-Throughput Automated Microfluidic Sample Preparation for Accurate Microbial Genomics.” Nature Communications 8 (2017): 13919. © 2017 Macmillan Publishers Limited As Published http://dx.doi.org/10.1038/ncomms13919 Publisher Nature Publishing Group Version Final published version Citable link http://hdl.handle.net/1721.1/108270 Terms of Use Creative Commons Attribution 4.0 International License Detailed Terms http://creativecommons.org/licenses/by/4.0/ ARTICLE Received 14 Oct 2016 | Accepted 11 Nov 2016 | Published 27 Jan 2017 DOI: 10.1038/ncomms13919 OPEN High-throughput automated microfluidic sample preparation for accurate microbial genomics Soohong Kim1,2, Joachim De Jonghe1,3, Anthony B. Kulesa1,2, David Feldman1,4, Tommi Vatanen1,5, Roby P. Bhattacharyya1,6, Brittany Berdy7, James Gomez1, Jill Nolan1, Slava Epstein7 & Paul C. Blainey1,2 Low-cost shotgun DNA sequencing is transforming the microbial sciences. Sequencing instruments are so effective that sample preparation is now the key limiting factor. Here, we introduce a microfluidic sample preparation platform that integrates the key steps in cells to sequence library sample preparation for up to 96 samples and reduces DNA input requirements 100-fold while maintaining or improving data quality. The general-purpose microarchitecture we demonstrate supports workflows with arbitrary numbers of reaction and clean-up or capture steps. By reducing the sample quantity requirements, we enabled low-input (B10,000 cells) whole-genome shotgun (WGS) sequencing of Mycobacterium tuberculosis and soil micro-colonies with superior results. -
IBM Functional Genomics Platform, a Cloud-Based Platform for Studying Microbial Life at Scale
IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , VOL. 16, NO. 7, DECEMBER 2019 1 IBM Functional Genomics Platform, A Cloud-Based Platform for Studying Microbial Life at Scale Edward E. Seabolt*, Gowri Nayar*, Harsha Krishnareddy, Akshay Agarwal, Kristen L. Beck, Ignacio Terrizzano, Eser Kandogan, Mark Kunitomi, Mary Roth, Vandana Mukherjee, and James H. Kaufman Abstract—The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. With the increasing availability of genomic data, traditional bioinformatic tools require substantial computational time and the creation of ever-larger indices each time a researcher seeks to gain insight from the data. To address these challenges, we pre-computed important relationships between biological entities spanning the Central Dogma of Molecular Biology and captured this information in a relational database. The database can be queried across hundreds of millions of entities and returns results in a fraction of the time required by traditional methods. In this paper, we describe IBM Functional Genomics Platform (formerly known as OMXWare), a comprehensive database relating genotype to phenotype for bacterial life. Continually updated, IBM Functional Genomics Platform today contains data derived from 200,000 curated, self-consistently assembled genomes. The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology, KEGG, MetaCyc, and Reactome. IBM Functional Genomics Platform maps all of the many-to-many connections between each biological entity including the originating genome, gene, protein, and protein domain. Various microbial studies, from infectious disease to environmental health, can benefit from the rich data and connections. -
6.047/6.878 Lecture 4: Comparative Genomics I: Genome Annotation Using Evolutionary Signatures
6.047/6.878 Lecture 4: Comparative Genomics I: Genome Annotation Using Evolutionary Signatures Mark Smith (Partially adapted from notes by: Angela Yen, Christopher Robde, Timo Somervuo and Saba Gul) 9/18/12 1 Contents 1 Introduction 4 1.1 Motivation and Challenge......................................4 1.2 Importance of many closely{related genomes...........................4 2 Conservation of genomic sequences5 2.1 Functional elements in Drosophila .................................5 2.2 Rates and patterns of selection...................................5 3 Excess Constraint 6 3.1 Causes of Excess Constraint.....................................7 3.2 Modeling Excess Constraint.....................................8 3.3 Excess Constraint in the Human Genome.............................9 3.4 Examples of Excess Constraint................................... 10 4 Diversity of evolutionary signatures: An Overview of Selection Patterns 10 4.1 Selective Pressures On Different Functional Elements....................... 11 5 Protein{Coding Signatures 12 5.1 Reading{Frame Conservation (RFC)................................ 13 5.2 Codon{Substitution Frequencies (CSFs).............................. 14 5.3 Classification of Drosophila Genome Sequences.......................... 16 5.4 Leaky Stop Codons.......................................... 17 6 microRNA (miRNA) genes 19 6.1 Computational Challenge...................................... 20 6.2 Unusual miRNA Genes........................................ 21 7 Regulatory Motifs 22 7.1 Computationally Detecting -
The Distribution and Evolution of Arabidopsis Thaliana Cis Natural Antisense Transcripts Johnathan Bouchard, Carlos Oliver and Paul M Harrison*
Bouchard et al. BMC Genomics (2015)16:444 DOI 10.1186/s12864-015-1587-0 RESEARCH ARTICLE Open Access The distribution and evolution of Arabidopsis thaliana cis natural antisense transcripts Johnathan Bouchard, Carlos Oliver and Paul M Harrison* Abstract Background: Natural antisense transcripts (NATs) are regulatory RNAs that contain sequence complementary to other RNAs, these other RNAs usually being messenger RNAs. In eukaryotic genomes, cis-NATs overlap the gene they complement. Results: Here, our goal is to analyze the distribution and evolutionary conservation of cis-NATs for a variety of available data sets for Arabidopsis thaliana, to gain insights into cis-NAT functional mechanisms and their significance. Cis-NATs derived from traditional sequencing are largely validated by other data sets, although different cis-NAT data sets have different prevalent cis-NAT topologies with respect to overlapping protein-coding genes. A. thaliana cis-NATs have substantial conservation (28-35% in the three substantive data sets analyzed) of expression in A. lyrata. We examined evolutionary sequence conservation at cis-NAT loci in Arabidopsis thaliana across nine sequenced Brassicaceae species (picked for optimal discernment of purifying selection), focussing on the parts of their sequences not overlapping protein- coding transcripts (dubbed ‘NOLPs’). We found significant NOLP sequence conservation for 28-34% NATs across different cis-NAT sets. This NAT NOLP sequence conservation versus A. lyrata is generally significantly correlated with conservation of expression. We discover a significant enrichment of transcription factor binding sites (as evidenced by CHIP-seq data) in NOLPs compared to randomly sampled near-gene NOLP-like DNA , that is linked to significant sequence conservation. -
Transcriptional Interferences in Cis Natural Antisense Transcripts of Humans and Mice
Copyright Ó 2007 by the Genetics Society of America DOI: 10.1534/genetics.106.069484 Transcriptional Interferences in cis Natural Antisense Transcripts of Humans and Mice Naoki Osato, Yoshiyuki Suzuki, Kazuho Ikeo and Takashi Gojobori1 Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Research Organization of Information and Systems, Mishima 411-8540, Japan Manuscript received December 9, 2006 Accepted for publication March 21, 2007 ABSTRACT For a significant fraction of mRNAs, their expression is regulated by other RNAs, including cis natural antisense transcripts (cis -NATs) that are complementary mRNAs transcribed from opposite strands of DNA at the same genomic locus. The regulatory mechanism of mRNA expression by cis -NATs is unknown, although a few possible explanations have been proposed. To understand this regulatory mechanism, we conducted a large-scale analysis of the currently available data and examined how the overlapping arrange- ments of cis -NATs affect their expression level. Here, we show that for both human and mouse the expression level of cis -NATs decreases as the length of the overlapping region increases. In particular, the proportions of the highly expressed cis -NATs in all cis -NATs examined were 36 and 47% for human and mouse, respectively, when the overlapping region was ,200 bp. However, both proportions decreased to virtually zero when the overlapping regions were .2000 bp in length. Moreover, the distribution of the expression level of cis -NATs changes according to different types of the overlapping pattern of cis -NATs in the genome. These results are consistent with the transcriptional collision model for the regulatory mechanism of gene expression by cis -NATs. -
Application of Comparative Genomics for Detection of Genomic Features and Transcriptional Regulatory Elements Hong Lu Iowa State University
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2011 Application of comparative genomics for detection of genomic features and transcriptional regulatory elements Hong Lu Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Cell and Developmental Biology Commons, and the Genetics and Genomics Commons Recommended Citation Lu, Hong, "Application of comparative genomics for detection of genomic features and transcriptional regulatory elements" (2011). Graduate Theses and Dissertations. 12151. https://lib.dr.iastate.edu/etd/12151 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Application of comparative genomics for detection of genomic features and transcriptional regulatory elements by Hong Lu A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Bioinformatics and Computational Biology Program of Study Committee: Volker Brendel, Co-major Professor Roger Wise, Co-major Professor Julie Dickerson Nick Lauter Adam Bogdanove Iowa State University Ames, Iowa 2011 Copyright © Hong Lu, 2011. All rights reserved. ii TABLE OF CONTENTS CHAPTER 1. INTRODUCTION -
After the Draft Sequence, What Next for the Human Genome Mapping Project Resource Centre?
Comparative and Functional Genomics Comp Funct Genom 2001; 2: 176–179. DOI: 10.1002/cfg.83 Interview: Duncan Campbell After the draft sequence, what next for the Human Genome Mapping Project Resource Centre? Dr R. Duncan Campbell is the Director of the Medical Research Council (MRC) funded UK Human Genome Mapping Project Resource Centre (HGMP-RC), on the Wellcome Trust Genome Campus at Hinxton, near Cambridge, UK. The Centre provides both biological and bioinformatics resources to the UK genomics community, and also has research groups supporting these activities and investigating separate projects. CFG: The HGMP-RC is broadly arranged into three tools for DNA and protein sequence analysis, Divisions: Bioinformatics, Biology Services and which will expand the functions offered by the Research. Let’s tackle these one at a time. The Genetics Computer Group (GCG) package. We are draft sequence release will no doubt have encouraged currently redeveloping our website in order to make many researchers to get on-line and use bio- it bigger, better and more interactive for our users, informatics tools to mine the dataset, can you tell and our hope is that we will have a new website me about the projects that the Bioinformatics team which will go live in the next few months. The site are working on to facilitate such investigations? will include access and ordering for the existing services and information and support for new RDC: With the huge amount of draft sequence now services as they come on-line. available, we have seen increased use of the bioinformatics tools already on offer from the CFG: The Biology Services Division supplies a HGMP-RC, such as NIX and PIX.