DOCSLIB.ORG

DOE Joint Genome Institute Strategic Planning for the Genomic Sciences Report from the May 30–31, 2012, Workshop

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
DOE Joint Genome Institute Strategic Planning for the Genomic Sciences Report from the May 30–31, 2012, Workshop DOE Joint Genome Institute Strategic Planning for the Genomic Sciences Report from the May 30–31, 2012, Workshop Convened by U.S. Department of Energy Office of Science Office of Biological and Environmental Research Co-Chairs Jim Fredrickson Michael Laub Jan Leach Pacific Northwest National Laboratory Massachusetts Institute of Technology Colorado State University Breakout Group Chairs Richard Michelmore Kimmen Sjölander Tom Schmidt University of California, Davis University of California, Berkeley Michigan State University Organizer Daniel Drell [email protected], 301.903.4742 Web address for this document: genomicscience.energy.gov/userfacilities/jgi/futuredirections/ About the Cover Images on the cover represent a broad range of the complex scales encompassed by the science supported by the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy’s (DOE) Office of Science. These scales range from genes at the subcellular level to genomes of microbes and their communities, to the genomics of plant-microbe interactions and plants that could be feedstocks for bioenergy, to the scale of ecosystem and landscape function. The under- lying DNA strand represents the DNA sequence data that provide the foundation for further systems-level experimentation. The images culminate in a wired cell that represents the predictive understanding of biological systems sought by BER programs. DNA sequence data generated by the DOE Joint Genome Institute user facility are having a major impact toward the achievement of this goal. Image credits: Microscopic images of organic matter decomposers (copyright Corbis), fungal hyphae on a root surface (copyright Corbis), and cross-section of a switchgrass stem (DOE BioEnergy Science Center and National Renewable Energy Laboratory). Grassland habitat (U.S. Department of Agriculture Natural Resources Conservation Service). Spruce-peatland ecosystem (Oak Ridge National Laboratory). Aerial view of Arctic landscape (Oak Ridge National Laboratory). Cover devel- oped at Oak Ridge National Laboratory. Suggested Citation U.S. DOE. 2012. DOE Joint Genome Institute Strategic Planning for the Genomic Sciences: Report from the May 30–31, 2012, Work- shop, DOE/SC-0152, U.S. Department of Energy Office of Science. DOE/SC-0152 DOE Joint Genome Institute Strategic Planning for the Genomic Sciences Report from the May 30–31, 2012, Workshop Published September 2012 Convened by U.S. Department of Energy Office of Science Office of Biological and Environmental Research DOE JGI Strategic Planning for the Genomic Sciences: Report from the May 30–31, 2012, Workshop ii U.S. Department of Energy Office of Science • Office of Biological and Environmental Research DOE JGI Strategic Planning for the Genomic Sciences: Report from the May 30–31, 2012, Workshop Contents Director’s Letter .................................................................................................................................................................................................................................... v Executive Summary .......................................................................................................................................................................................................................... vii Sidebar 1: Major Themes and Needs Emerging from the Workshop ................................................................................................................................... vii Introduction and Background ........................................................................................................................................................................................................1 Sidebar 2: Decreasing the Lag Time Between Sequencing and Annotation ........................................................................................................................2 Sidebar 3: DOE Biological and Environmental Research Program Perspective ....................................................................................................................3 Sidebar 4: DOE Genomic Science Systems Biology Program ...................................................................................................................................................5 Grand Challenges ................................................................................................................................................................................................................................9 Next-Generation Enabling Capabilities ...................................................................................................................................................................................13 Sidebar 5: Functional Annotation: A Prerequisite for Predictive Biology ...........................................................................................................................15 Sidebar 6: Automating Science in a Robotic Laboratory .......................................................................................................................................................19 Summary ..............................................................................................................................................................................................................................................21 Appendices ...........................................................................................................................................................................................................................................23 Appendix 1: Grand Challenges ..........................................................................................................................................................................................................23 Appendix 2: Department of Energy Assets ......................................................................................................................................................................................29 DOE Joint Genome Institute .....................................................................................................................................................................................................29 DOE Environmental Molecular Sciences Laboratory .............................................................................................................................................................30 DOE Systems Biology Knowledgebase ....................................................................................................................................................................................31 DOE Synchrotron and Neutron Beam Facilities for Biology ..................................................................................................................................................32 Appendix 3: Workshop Agenda, Charge Questions, Participants .................................................................................................................................................33 Appendix 4: Bibliography ..................................................................................................................................................................................................................37 Appendix 5: Glossary ..........................................................................................................................................................................................................................39 Acronyms and Abbreviations ................................................................................................................................................................................Inside back cover U.S. Department of Energy Office of Science • Office of Biological and Environmental Research iii DOE JGI Strategic Planning for the Genomic Sciences: Report from the May 30–31, 2012, Workshop iv U.S. Department of Energy Office of Science • Office of Biological and Environmental Research DOE JGI Strategic Planning for the Genomic Sciences: Report from the May 30–31, 2012, Workshop Department of Energy Washington, DC 20585 September 20, 2012 In October 2011, the U. S. Department of Energy (DOE) Joint Genome Institute (JGI) issued a draft “10-Year Strategic Vision: Forging the Future of the DOE JGI.” This document provided a high-level overview of DOE JGI and its plans to evolve as a next-generation genomic science user facility. The intent was to draft a vision for DOE JGI that goes beyond just sequence generation and seeks new technologies and/or capabilities to enhance the interpretation and use of genomic data. The draft document took advantage of a recent assessment (Grand Challenges for Biological and Environmental Research: A Long-Term Vision DOE/SC-0135) of the major long-term scientific challenges in energy and the environment that are the core mission areas of the DOE Office of Biological and Environmental Research (BER) and outlines how DOE JGI must evolve to help meet these research challenges. In May 2012, BER hosted a separate workshop on “DOE JGI Strategic Planning for the Genomic Sciences” to solicit additional community input towards articulating a high-level DOE Office of Science vision for DOE JGI’s role in advancing BER mission science. The intention was not to explore how DOE JGI could evolve to be a
Load more

Recommended publications
  • Whole Genome Sequencing and Comparative Genomic Analysis Of
    Li et al. BMC Genomics (2020) 21:181 https://doi.org/10.1186/s12864-020-6593-1 RESEARCH ARTICLE Open Access Whole genome sequencing and comparative genomic analysis of oleaginous red yeast Sporobolomyces pararoseus NGR identifies candidate genes for biotechnological potential and ballistospores-shooting Chun-Ji Li1,2, Die Zhao3, Bing-Xue Li1* , Ning Zhang4, Jian-Yu Yan1 and Hong-Tao Zou1 Abstract Background: Sporobolomyces pararoseus is regarded as an oleaginous red yeast, which synthesizes numerous valuable compounds with wide industrial usages. This species hold biotechnological interests in biodiesel, food and cosmetics industries. Moreover, the ballistospores-shooting promotes the colonizing of S. pararoseus in most terrestrial and marine ecosystems. However, very little is known about the basic genomic features of S. pararoseus. To assess the biotechnological potential and ballistospores-shooting mechanism of S. pararoseus on genome-scale, the whole genome sequencing was performed by next-generation sequencing technology. Results: Here, we used Illumina Hiseq platform to firstly assemble S. pararoseus genome into 20.9 Mb containing 54 scaffolds and 5963 predicted genes with a N50 length of 2,038,020 bp and GC content of 47.59%. Genome completeness (BUSCO alignment: 95.4%) and RNA-seq analysis (expressed genes: 98.68%) indicated the high-quality features of the current genome. Through the annotation information of the genome, we screened many key genes involved in carotenoids, lipids, carbohydrate metabolism and signal transduction pathways. A phylogenetic assessment suggested that the evolutionary trajectory of the order Sporidiobolales species was evolved from genus Sporobolomyces to Rhodotorula through the mediator Rhodosporidiobolus. Compared to the lacking ballistospores Rhodotorula toruloides and Saccharomyces cerevisiae, we found genes enriched for spore germination and sugar metabolism.
    [Show full text]
  • GALA, a Database for Genomic Sequence Alignments and Annotations
    Resources GALA, a Database for Genomic Sequence Alignments and Annotations Belinda Giardine,1 Laura Elnitski,1,2 Cathy Riemer,1 Izabela Makalowska,4 Scott Schwartz,1 Webb Miller,1,3,4 and Ross C. Hardison2,4,5 Departments of 1Computer Science and Engineering, 2Biochemistry and Molecular Biology, 3Biology, and 4Huck Institute for Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA We have developed a relational database to contain whole genome sequence alignments between human and mouse with extensive annotations of the human sequence. Complex queries are supported on recorded features, both directlyand on proximityamong them. Searches can reveal a wide varietyof relationships, such as finding all genes expressed in a designated tissue that have a highlyconserved non coding sequence !Ј to the start site. Other examples are finding single nucleotide polymorphisms that occur in conserved noncoding regions upstream of genes and identifying CpG islands that overlap the !Ј ends of divergentlytranscribed genes. The database is available online at http://globin.cse.psu.edu/ and http://bio.cse.psu.edu/. The determination and annotation of complete genomic combined with sequence conservation can refine predictions DNA sequences provide the opportunity for unprecedented of functional sequences (Levy et al. 2001). One way to do this advances in our understanding of evolution, genetics, and is to record both extensive annotations and sequence align- physiology, but the amount and diversity of data pose daunt- ments in a database. ing challenges as well. Three excellent browsers provide access We have developed a database of genomic DNA se- to the sequence and annotations of the human genome, viz., quence alignments and annotations, called GALA, to search the human genome browser !"#$ at %&'& (Kent et al.
    [Show full text]
  • ANSWER KEY Sybsc. Life Sciences- SEM
    ANSWER KEY S.Y.B.Sc. Life Sciences- SEM III - Paper III Q.P.Code: 79543 Exam Date: 2nd November 2018 Marks : 100 Q. 1 Do as Directed: (20mks) .Q. 1. A) Define / Explain the following terms: (07) 1. TCP/IP- It is commonly known as TCP/IP because the foundational protocols in the suite are the Transmission Control Protocol (TCP) and the Internet Protocol (IP). It is a set of networking protocols that allows two or more computers to communicate. 2. WWW- The World Wide Web (WWW), also called the Web, is an information space where documents and other web resourcesare identified by Uniform Resource Locators (URLs), interlinked by hypertext links, and accessible via the Internet. Web pages are primarily text documents formatted and annotated with Hypertext Markup Language (HTML). In addition to formatted text, web pages may contain images, video, audio, and software components that are rendered in the user's web browser as coherent pages of multimedia content. 3. Proteomics- Proteomics is the large-scale study of proteomes. A proteome is a set of proteins produced in an organism, system, or biological context. 4. Human Genome Project-The Genome Project (HGP) was an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional standpoint. 5. Forward reading frame-An open reading frame starts with an atg (Met) in most species and ends with a stop codon (taa, tag or tga).
    [Show full text]
  • Annotating a Non-Model Plant Genome – a Study on the Narrow-Leafed Lupin
    BioTechnologia vol. 93(3) C pp. 318-332 C 2012 Journal of Biotechnology, Computational Biology and Bionanotechnology RESEARCH PAPER Annotating a non-model plant genome – a study on the narrow-leafed lupin ANDRZEJ ZIELEZIŃSKI 1, PIOTR POTARZYCKI 1, MICHAŁ KSIĄŻKIEWICZ 2, WOJCIECH M. KARŁOWSKI 1* 1 Laboratory of Computational Genomics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland 2 Institute of Plant Genetics, Polish Academy of Sciences, Poznan, Poland * Corresponding author: [email protected] Abstract We present here a highly portable and easy-to-use gene annotation system CEL (Computational Environment for annotation of Legume genomes) that can be used to annotate any type of genomic sequence -- from BAC ends to complete chromosomes. CEL’s core engine is modular and hierarchically organized with an open-source struc- ture, permitting maximum customization -- users can assemble an individualized annotation pipeline by selecting computational components that best suit their annotation needs. The tool is designed to speed up genomic ana- lyses and features an algorithm that substitutes for a biologist’s expertise at various steps of gene structure pre- diction. This allows more complete automation of the labor-intensive and time-consuming annotation process. The system collects and prioritizes multiple sources of de novo gene predictions and gene expression evidence according to the confidence value of underlying supporting evidence, as a result producing high-quality gene- model sets. The data produced by CEL pipeline is suitable for direct visualization in any genome browser tool that supports GFF annotation format (e.g. Apollo, Artemis, Genome Browser etc.). This provides an easy means to view and edit individual contigs and BACs using just mouse’s clicks and drag-and-drop features.
    [Show full text]
  • University of Copenhagen
    Detection of RNA structures in porcine EST data and related mammals Seemann, Ernst Stefan; Gilchrist, Michael J.; Hofacker, Ivo L.; Stadler, Peter F.; Gorodkin, Jan Published in: BMC Genomics DOI: 10.1186/1471-2164-8-316 Publication date: 2007 Document version Publisher's PDF, also known as Version of record Citation for published version (APA): Seemann, E. S., Gilchrist, M. J., Hofacker, I. L., Stadler, P. F., & Gorodkin, J. (2007). Detection of RNA structures in porcine EST data and related mammals. BMC Genomics, 8(316). https://doi.org/10.1186/1471- 2164-8-316 Download date: 29. sep.. 2021 BMC Genomics BioMed Central Research article Open Access Detection of RNA structures in porcine EST data and related mammals Stefan E Seemann1,2, Michael J Gilchrist3, Ivo L Hofacker4, Peter F Stadler2,4 and Jan Gorodkin*1 Address: 1Division of Genetics and Bioinformatics, IBHV, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg, Denmark, 2Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany, 3The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge, CB2 1QN, UK and 4Institute for Theoretical Chemistry and Structural Biology, University of Vienna, Austria Email: Stefan E Seemann - [email protected]; Michael J Gilchrist - [email protected]; Ivo L Hofacker - [email protected]; Peter F Stadler - [email protected]; Jan Gorodkin* - [email protected] * Corresponding author Published: 10 September 2007 Received: 29 May 2007 Accepted: 10 September 2007 BMC Genomics 2007, 8:316 doi:10.1186/1471-2164-8-316 This article is available from: http://www.biomedcentral.com/1471-2164/8/316 © 2007 Seemann et al; licensee BioMed Central Ltd.
    [Show full text]
  • Using High-Resolution Annotation of Insect Mitochondrial DNA to Decipher Tandem Repeats in the Control Region
    bioRxiv preprint doi: https://doi.org/10.1101/500330; this version posted December 19, 2018. 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. Using high-resolution annotation of insect mitochondrial DNA to decipher tandem repeats in the control region Haishuo Ji1$, Xiaofeng Xu2$, Xiufeng Jin2, Zhi Cheng2, Hong Yin23 Guangyuan Liu2, Qiang Zhao1, Ze Chen2, Wenjun Bu1*, Shan Gao1* 1. College of Life Sciences, Nankai University, Tianjin, Tianjin 300071, P.R.China. 2. State Key Laboratory of Veterinary Etiological Biology and Key Laboratory of Veterinary Parasitology of Gansu Province, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Science, Lanzhou, Gansu 730046, P.R.China. 3. Jiangsu Co-Innovation Center for Prevention and Control of Important Animal Infectious Disease and Zoonose, Yangzhou 225009, P.R.China. $ These authors contributed equally to this paper. * The corresponding authors. SG [email protected] WB [email protected] - 1 - bioRxiv preprint doi: https://doi.org/10.1101/500330; this version posted December 19, 2018. 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. Abstract In this study, we used a small RNA sequencing (sRNA-seq) based method to annotate the mitochondrial genome of the insect Erthesina fullo Thunberg at 1 bp resolution. Most of the new annotations were consistent with the previous annotations which were obtained using PacBio full-length transcripts. Two important findings are that animals transcribe both entire strands of mitochondrial genomes and the tandem repeat in the control region of the E.
    [Show full text]
  • A Genetrek Analysis of the Maize Genome
    A GeneTrek analysis of the maize genome Renyi Liu*, Cle´ mentine Vitte*, Jianxin Ma*, A. Assibi Mahama†, Thanda Dhliwayo†, Michael Lee†, and Jeffrey L. Bennetzen*‡ *Department of Genetics, University of Georgia, Athens, GA 30602; and †Department of Agronomy, Iowa State University, Ames, IA 50011 Contributed by Jeffrey L. Bennetzen, May 7, 2007 (sent for review March 19, 2007) Analysis of the sequences of 74 randomly selected BACs demon- BAC clones from maize led to the prediction of 42,000–56,000 gene strated that the maize nuclear genome contains Ϸ37,000 candidate models and at least 66% repetitive DNA (14). genes with homologues in other plant species. An additional BAC sequences provide additional context information to a Ϸ5,500 predicted genes are severely truncated and probably pseu- sample sequence analysis, thus allowing genome predictions that dogenes. The distribution of genes is uneven, with Ϸ30% of BACs greatly enrich the GeneTrek approach. Here we describe a proce- containing no genes. BAC gene density varies from 0 to 7.9 per 100 dure for accurately predicting plant genome structure and compo- kb, whereas most gene islands contain only one gene. The average sition with a relatively small data input and use this approach in number of genes per gene island is 1.7. Only 72% of these genes comprehensive sequence annotation of randomly selected BACs show collinearity with the rice genome. Particular LTR retrotrans- that contain DNA from maize inbred B73. The results indicate that poson families (e.g., Gyma) are enriched on gene-free BACs, most the maize genome contains many gene-free regions, many highly of which do not come from pericentromeres or other large het- truncated gene fragments, and a nonrandom distribution of repet- erochromatic regions.
    [Show full text]
  • Comprehensive Genomic Analysis of an Indigenous Pseudomonas Pseudoalcaligenes Degrading Phenolic Compounds
    www.nature.com/scientificreports OPEN Comprehensive genomic analysis of an indigenous Pseudomonas pseudoalcaligenes degrading Received: 11 January 2019 Accepted: 15 August 2019 phenolic compounds Published: xx xx xxxx Maryam Safari1,2, Bagher Yakhchali1 & Vahid Shariati.J3 Environmental contamination with aromatic compounds is a universal challenge. Aromatic-degrading microorganisms isolated from the same or similar polluted environments seem to be more suitable for bioremediation. Moreover, microorganisms adapted to contaminated environments are able to use toxic compounds as the sole sources of carbon and energy. An indigenous strain of Pseudomonas, isolated from the Mahshahr Petrochemical plant in the Khuzestan province, southwest of Iran, was studied genetically. It was characterized as a novel Gram-negative, aerobic, halotolerant, rod-shaped bacterium designated Pseudomonas YKJ, which was resistant to chloramphenicol and ampicillin. Genome of the strain was completely sequenced using Illumina technology to identify its genetic characteristics. MLST analysis revealed that the YKJ strain belongs to the genus Pseudomonas indicating the highest sequence similarity with Pseudomonas pseudoalcaligenes strain CECT 5344 (99% identity). Core- and pan-genome analysis indicated that P. pseudoalcaligenes contains 1,671 core and 3,935 unique genes for coding DNA sequences. The metabolic and degradation pathways for aromatic pollutants were investigated using the NCBI and KEGG databases. Genomic and experimental analyses showed that the YKJ strain is able to degrade certain aromatic compounds including bisphenol A, phenol, benzoate, styrene, xylene, benzene and chlorobenzene. Moreover, antibiotic resistance and chemotaxis properties of the YKJ strain were found to be controlled by two-component regulatory systems. Environmental pollution is considered an important threat to the ecosystem1, infuencing all organisms and human health extensively1,2.
    [Show full text]
  • Automated Paleontology of Repetitive DNA with Reanno- Tate
    Automated Paleontology of Repetitive DNA with REanno- tate Vini Pereira∗1 1Department of Life Sciences, Imperial College London, Silwood Park campus, Ascot, Berkshire SL5 7PY, UK Email: Vini Pereira∗- [email protected] Current address: Theoretical Systems Biology, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK; ∗Corresponding author Abstract Background: Dispersed repeats are a major component of eukaryotic genomes and drivers of genome evolution. Annotation of DNA sequences homologous to known repetitive elements has been mainly performed with the program RepeatMasker. Sequences annotated by RepeatMasker often correspond to fragments of repetitive elements resulting from the insertion of younger elements or other rearrangements. Although RepeatMasker annotation is indispensable for studying genome biology, this annotation does not contain much information on the common origin of fossil fragments that share an insertion event, especially where clusters of nested insertions of repetitive elements have occurred. Results: Here I present REannotate, a computational tool to process RepeatMasker annotation for automated i) defragmentation of dispersed repetitive elements, ii) resolution of the temporal order of insertions in clusters of nested elements, and iii) estimating the age of the elements, if they have long terminal repeats. I have re-annotated the repetitive content of human chromosomes, providing evidence for a recent expansion of satellite repeats on the Y chromosome and, from the retroviral age distribution, for a higher rate of evolution on the Y relative to autosomes. Conclusions: REannotate is ready to process existing annotation for automated evolutionary analysis of all types of complex repeats in any genome. The tool is freely available under the GPL at http://www.bioinformatics.org/reannotate.
    [Show full text]
  • AI in Healthcare, Animal Husbandry & Plant Genomics Hello!
    AI in Healthcare, Animal Husbandry & Plant Genomics Hello! Good Afternoon everybody. After about 50 years of largely being in technology labs, pilots and science fiction, artificial intelligence (AI) has taken center stage today. Barely a day goes by, without dozens of new magazine and newspaper articles, blog posts, TV stories, LinkedIn columns, and tweets about cognitive technologies. It shouldn’t be at all surprising. The impact of AI has been very upfront and highly personal these days. The technology is beginning to reshape the existing ecosystem. Initially conceived as a technology that could mimic human intelligence, AI has evolved in ways that far exceed its original conception. With incredible advances made in data collection, processing and compute power, systems are now deployed to assist in a variety of tasks, exhibit intelligence and enhance user experience. Over a period of time, AI capabilities have increased manifold leading to its utility in various facets of life. I will try to bring out, the significance of AI in Healthcare, animal husbandry & plant genomics and try to explain the nuances in the usage of AI in this session. Our vision should be that we in India should not be a passive participant to the technologies developed elsewhere, but positioned to actively influence AI development path. The global genomics industry, is worth $16.4 Billion USD as of 2018, and is expected to reach $41.2 Billion USD by 2025. The genomics industry, consists of genomic products and services. The genomic products, are expected to dominate the market, due to the rising number of research programs, undertaken by government and private organizations.
    [Show full text]
  • A Review of Various Methods Used in the Analysis of Functional Gene Expression Data
    International Journal of Information Technology Convergence and Services (IJITCS) Vol.7, No.2/3, June 2017 A REVIEW OF VARIOUS METHODS USED IN THE ANALYSIS OF FUNCTIONAL GENE EXPRESSION DATA Houda Fyad, Fatiha Barigou, Karim Bouamrane, Baghdad Atmani LIO Laboratory, Department of Computer Science, Faculty of Exact and Applied Sciences University of Oran 1 Ahmed Ben Bella BP 1524, 31000 El M'naouer Oran, Algeria ABSTRACT Sequencing projects arising from high-throughput technologies including those of sequencing DNA microarray allowed measuring simultaneously the expression levels of millions of genes of a biological sample as well as to annotate and to identify the role (function) of those genes. Consequently, to better manage and organize this significant amount of information, bioinformatics approaches have been developed. These approaches provide a representation and a more 'relevant' integration of data in order to test and validate the researchers’ hypothesis. In this context, this article describes and discusses some techniques used for the functional analysis of gene expression data. KEYWORDS Microarray, Genome Annotation, Functional Analysis, Genes Expression Data, Data Mining, Clustering, Classification, Gene Ontology. 1. INTRODUCTION This paper is an extension of the previous work [1]. In this paper, we add the description of the bioinformatics approaches that have been developed to improve the quality of annotation. Also, we discuss some hybrid methods that performing well in analysis of functional gene expression data. The successful developments of high-throughput sequencing technology including those of sequencing DNA microarray generated a large volume of genomic data. This technology allows measuring the simultaneous expression of a large number of genes, or even all the genes contained in the genome under many and varied conditions.
    [Show full text]
  • Identification and DNA Annotation of a Plasmid Isolated From
    www.nature.com/scientificreports OPEN Identifcation and DNA annotation of a plasmid isolated from Chromobacterium violaceum Received: 14 December 2017 Daniel C. Lima1,2, Lena K. Nyberg3, Fredrik Westerlund3 & Silvia R. Batistuzzo de Medeiros2 Accepted: 12 March 2018 Chromobacterium violaceum is a ß-proteobacterium found widely worldwide with important Published: xx xx xxxx biotechnological properties and is associated to lethal sepsis in immune-depressed individuals. In this work, we report the discover, complete sequence and annotation of a plasmid detected in C. violaceum that has been unnoticed until now. We used DNA single-molecule analysis to confrm that the episome found was a circular molecule and then proceeded with NGS sequencing. After DNA annotation, we found that this extra-chromosomal DNA is probably a defective bacteriophage of approximately 44 kilobases, with 39 ORFs comprising, mostly hypothetical proteins. We also found DNA sequences that ensure proper plasmid replication and partitioning as well as a toxin addiction system. This report sheds light on the biology of this important species, helping us to understand the mechanisms by which C. violaceum endures to several harsh conditions. This discovery could also be a frst step in the development of a DNA manipulation tool in this bacterium. Chromobacterium violaceum is a Gram-negative facultative anaerobe bacillus belonging to the Neisseriaceae fam- ily1. Tis free-living ß-proteobacterium reside mainly around tropical and sub-tropical regions. Te study of C. violaceum started in the 1970s, focusing on its potential in pharmacology and industry for the production of antibiotics, anti-tumoral substances, biopolymers and others organic compounds (reviewed in refs2–4).
    [Show full text]