Joint Genome Institute PROGRESS REPORT 2006 JGI’S Mission
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Computer Applications Making Rapid Advances in High Throughput Microbial Proteomics (HTMP) Balakrishna Anandkumar1,§, Steve W
Send Orders for Reprints to [email protected] Combinatorial Chemistry & High Throughput Screening, 2014, 17, 173-182 173 Computer Applications Making Rapid Advances in High Throughput Microbial Proteomics (HTMP) Balakrishna Anandkumar1,§, Steve W. Haga2,§ and Hui-Fen Wu*,3,4,5,6 1Department of Biochemistry and Biotechnology, Sourashtra College, Madurai 625004, India 2Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804, Taiwan 3Department of Chemistry, National Sun Yat Sen University, Kaohsiung, 804, Taiwan 4School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, 800, Taiwan 5Center for Nanoscience and Nanotechnology, National Sun Yat-Sen University, Kaohsiung, 804, Taiwan 6Doctoral Degree Program in Marine Biotechnology, National Sun Yat-sen University, Kaohsiung, 804, Taiwan Abstract: The last few decades have seen the rise of widely-available proteomics tools. From new data acquisition devices, such as MALDI-MS and 2DE to new database searching softwares, these new products have paved the way for high throughput microbial proteomics (HTMP). These tools are enabling researchers to gain new insights into microbial metabolism, and are opening up new areas of study, such as protein-protein interactions (interactomics) discovery. Computer software is a key part of these emerging fields. This current review considers: 1) software tools for identifying the proteome, such as MASCOT or PDQuest, 2) online databases of proteomes, such as SWISS-PROT, Proteome Web, or the Proteomics Facility of the Pathogen Functional Genomics Resource Center, and 3) software tools for applying proteomic data, such as PSI-BLAST or VESPA. These tools allow for research in network biology, protein identification, functional annotation, target identification/validation, protein expression, protein structural analysis, metabolic pathway engineering and drug discovery. -
Alejandra C. Ortiz 425D Mann Hall 2501 Stinson Dr
Alejandra C. Ortiz 425D Mann Hall 2501 Stinson Dr. Raleigh, NC 27695 Phone: (919) 515-8392 E-Mail: [email protected] Appointments Assistant Professor, Department of Civil, Construction, and Environmental Engineering, North Carolina State University. 2017-Present National Center for Earth Surface Dynamics 2 Synthesis Postdoctoral Fellow, Department of Geological Sciences at Indiana University. 2015 – 2016. Adviser: Dr. Doug Edmonds Education Ph.D. MIT-WHOI Joint Program in Oceanography and Applied Ocean Science & Engineering. 2010 – 2015. Marine Geology & Geophysics Department. Investigating the Evolution and Formation of Coastlines and the Response to Sea-Level Rise. Dr. Andrew D. Ashton. M.S. MIT. 2010 - 2012. Civil and Environmental Engineering Department. Investigation of the Effect of a Circular Patch of Vegetation on Turbulence Generation and Sediment Deposition Using Four Case Studies. Dr. Heidi M. Nepf. B.A. Wellesley College. 2006 – 2010. Geosciences & Classical Civilizations. Honors in Geosciences & cum laude. Sigma Psi. Senior Thesis in Geosciences: Investigating the Effect of Wave Energy on Coastal Morphology and Beach Sedimentology Using Real and Modeled Wave Data. Dr. Britt Argow. Research Interests • Coastal Geomorphology • Numerical Modeling • Coastal Response to Climate Change • Fluvial Ecogeomorphology • Coastal Sedimentology Academic Experience Teaching • Teaching Assistant. MIT – 12.717. Coastal Geomorphology. Planned class field trip, 2015 planed and graded homework assignments. Graduate Students. • Teaching Assistant. MIT – 1.69. Transport Processes in the Environment. Planned and 2013 prepped 1 lab and 2 lectures. Undergraduates. • Teaching Assistant. MIT – 12.747. Modeling, Data Analysis, and Numerical Techniques 2012 for Geochemistry. MatLab Programming. Graduate Students. • Teaching Assistant. Wellesley College – CS 112. Computation for the Sciences. MatLab 2009-2010 Programming. -
Genome 559 Intro to Statistical and Computational Genomics
Genome 559! Intro to Statistical and Computational Genomics! Lecture 16a: Computational Gene Prediction Larry Ruzzo Today: Finding protein-coding genes coding sequence statistics prokaryotes mammals More on classes More practice Codons & The Genetic Code Ala : Alanine Second Base Arg : Arginine U C A G Asn : Asparagine Phe Ser Tyr Cys U Asp : Aspartic acid Phe Ser Tyr Cys C Cys : Cysteine U Leu Ser Stop Stop A Gln : Glutamine Leu Ser Stop Trp G Glu : Glutamic acid Leu Pro His Arg U Gly : Glycine Leu Pro His Arg C His : Histidine C Leu Pro Gln Arg A Ile : Isoleucine Leu Pro Gln Arg G Leu : Leucine Ile Thr Asn Ser U Lys : Lysine Ile Thr Asn Ser C Met : Methionine First Base A Third Base Ile Thr Lys Arg A Phe : Phenylalanine Met/Start Thr Lys Arg G Pro : Proline Val Ala Asp Gly U Ser : Serine Val Ala Asp Gly C Thr : Threonine G Val Ala Glu Gly A Trp : Tryptophane Val Ala Glu Gly G Tyr : Tyrosine Val : Valine Idea #1: Find Long ORF’s Reading frame: which of the 3 possible sequences of triples does the ribosome read? Open Reading Frame: No stop codons In random DNA average ORF = 64/3 = 21 triplets 300bp ORF once per 36kbp per strand But average protein ~ 1000bp So, coding DNA is not random–stops are rare Scanning for ORFs 1 2 3 U U A A U G U G U C A U U G A U U A A G! A A U U A C A C A G U A A C U A A U A C! 4 5 6 Idea #2: Codon Frequency,… Even between stops, coding DNA is not random In random DNA, Leu : Ala : Tryp = 6 : 4 : 1 But in real protein, ratios ~ 6.9 : 6.5 : 1 Even more: synonym usage is biased (in a species dependant way) Examples known with 90% AT 3rd base Why? E.g. -
CURRICULUM VITAE George M. Weinstock, Ph.D
CURRICULUM VITAE George M. Weinstock, Ph.D. DATE September 26, 2014 BIRTHDATE February 6, 1949 CITIZENSHIP USA ADDRESS The Jackson Laboratory for Genomic Medicine 10 Discovery Drive Farmington, CT 06032 [email protected] phone: 860-837-2420 PRESENT POSITION Associate Director for Microbial Genomics Professor Jackson Laboratory for Genomic Medicine UNDERGRADUATE 1966-1967 Washington University EDUCATION 1967-1970 University of Michigan 1970 B.S. (with distinction) Biophysics, Univ. Mich. GRADUATE 1970-1977 PHS Predoctoral Trainee, Dept. Biology, EDUCATION Mass. Institute of Technology, Cambridge, MA 1977 Ph.D., Advisor: David Botstein Thesis title: Genetic and physical studies of bacteriophage P22 genomes containing translocatable drug resistance elements. POSTDOCTORAL 1977-1980 Postdoctoral Fellow, Department of Biochemistry TRAINING Stanford University Medical School, Stanford, CA. Advisor: Dr. I. Robert Lehman. ACADEMIC POSITIONS/EMPLOYMENT/EXPERIENCE 1980-1981 Staff Scientist, Molec. Gen. Section, NCI-Frederick Cancer Research Facility, Frederick, MD 1981-1983 Staff Scientist, Laboratory of Genetics and Recombinant DNA, NCI-Frederick Cancer Research Facility, Frederick, MD 1981-1984 Adjunct Associate Professor, Department of Biological Sciences, University of Maryland, Baltimore County, Catonsville, MD 1983-1984 Senior Scientist and Head, DNA Metabolism Section, Lab. Genetics and Recombinant DNA, NCI-Frederick Cancer Research Facility, Frederick, MD 1984-1990 Associate Professor with tenure (1985) Department of Biochemistry -
The U.S. Department of Energy's Ten-Year-Plans for the Office Of
U.S. DEPARTMENT OF ENERGY The U.S. Department of Energy’s Ten-Year-Plans for the Office of Science National Laboratories FY 2019 FY 2019 Annual Laboratory Plans for the Office of Science National Laboratories i Table of Contents Introduction ................................................................................................................................................................1 Ames Laboratory ........................................................................................................................................................3 Lab-at-a-Glance ......................................................................................................................................................3 Mission and Overview ............................................................................................................................................3 Core Capabilities .....................................................................................................................................................4 Science Strategy for the Future ..............................................................................................................................8 Infrastructure .........................................................................................................................................................8 Argonne National Laboratory ................................................................................................................................. -
Here She Received a NASA Earth Systems Science Fellowship (1996- 1999) and Completed Her Ph.D
TRAIT-BASED APPROACHES TO OCEAN LIFE traitspace.com Keynotes Brian Enquist Lionel Guidi Image: Erik Selander Alexandra Worden Neil Banas CHICHELEY HALL, BUCKINGHAMSHIRE, UK 18th to 21st August, 2019 FOURTH WORKSHOP ON TRAIT-BASED APPROACHES TO OCEAN LIFE Table of Contents Schedule ...................................................................................................................................... 2 Keynote Speakers ........................................................................................................................ 5 Abstracts ..................................................................................................................................... 7 1 Schedule Sunday 13:00-14:00 Lunch 18th August 14:00-14:10 Welcome & Introduction Session 1: Traits, environments, ecology and evolution 14:10-15:10 Keynote: Brian Enquist The past, present, and future of trait- based ecology: Toward a more predictive framework 15:10-15:30 Davi Castro Tavares Traits shared by marine megafauna and their relationships with ecosystem functions and services 15:30-15:50 Stephanie Dutkiewicz Biogeochemical and ecological redundancy in phytoplankton communities 15:50-16:10 Tea/Coffee 16:10-16:30 Stephanie Green A traits-based framework to account for the influence of predator-prey interactions on species distribution under global change 16:30-16:50 David Talmy Trade-offs modify ecosystem biomass structure along trophic gradients 16:50-17:10 Aleksandra Does initial diversity influence Lewandowska phytoplankton response -
Ijphs Jan Feb 07.Pmd
www.ijpsonline.com Review Article Pharmacogenomics and Computational Genomics: An Appraisal P. K. TRIPATHI*, SHALINI TRIPATHI AND N. K. JAIN1 Rajarshi Rananjay Sinh College of Pharmacy, Amethi-227 405, 1Department of Pharmaceutical Sciences, Dr. H. S. Gour Vishwavidyalaya, Sagar-470 003, India. Pharmacogenomics deals with the interactions of individual genetic constitution with drug therapy. It is very likely that pharmacogenetic tests will make up a significant proportion of total molecular biology testing in future. Therefore, this article emphasizes the applications of pharmacogenomics, and computational genome analysis in drug therapy. Patients sometimes experience adverse drug reactions for both efficacy and safety of a given drug regimen and (ADR) leading to deterioration of their underlying this is the central topic of pharmacogenomics. Drug condition in clinical practice. Physiology of individual development and pretreatment genetic analysis of patients patient can vary, so the response of an individual to drug will be two major practical aspects in pharmacogenomics. therapy may also be highly variable. This is a major Highly specialized drug research laboratories will achieve clinical problem, since this inter-individual variability is the first goal, while the second goal has to be achieved until now only partly predictable. Besides these medical by clinical .com).laboratories 2,3. problems, cost-benefit calculations for a given pharmacological therapy will be affected significantly. Drug development: This is exemplified by a recent US study, which estimates There are two pharmacogenomic approaches. First, for that over 100,000 patients die every year from ADRs1. most therapies a correct diagnosis is mandatory for a satisfactory therapeutic result. This is more relevant There are many factors such as age, sex, nutritional.medknow because many diseases may be caused by different status, kidney and liver function, concomitant diseases and genetic defects or be significantly affected by the genetic medications, and the disease that affects drug responses. -
For Immediate Release. Oct. 8Th, 2010 Microbesonline.Org Enhanced for Metagenomics and Metabolism
For Immediate Release. Oct. 8th, 2010 MicrobesOnline.org Enhanced for Metagenomics and Metabolism & The Arkin lab (genomics.lbl.gov) computational biology and bioinformatics team, as part of the Joint BioEnergy Institute (JBEI, www.jbei.org) and the Virtual Institute for Microbial Stress and Survival (VIMSS, vimss.lbl.gov), has added significant new functionality to the MicrobesOnline.org web resource for comparative and functional analysis of microbial genomes. This work, led by Dylan Chivian with John Bates, Keith Keller, Morgan Price, Paramvir Dehal, under the guidance of Adam Arkin, offers the scientific community new capabilities for metagenomic and metabolic analyses. metaMicrobesOnline Figure 1. metaMicrobesOnline permits the user to select metagenomes and isolates they want to study and then perform easy searches for keywords or gene families (e.g. “amylase” or “PFAM00128”). Scanning electron microscopy image of microbial compost community courtesy Bernhard Knierim and Manfred Auer. The new meta.MicrobesOnline.org web resource, which currently contains over 1600 genomes from bacterial, archaeal, and microeukaryotic isolates, offers combined phylogenetic gene tree analysis of millions of genes from over 150 ecological and organismal metagenomes. These trees are built using our FastTree [1] program, which offers rapid highly accurate tree building, even for very large trees. Such combined analysis is superior to BLAST-based homology approaches in that trees offers the ability to place genes from environmental samples into an evolutionary context and permits more precise functional grouping within a gene family, yielding information about the key genes for a given environment. Additionally, comparison with isolate genomes gives researchers clues for which additional genes to look for to complete the components of systems, or may possess phylogenetic markers to aid in assigning the species for environmental sequence fragments, permitting the determination of which community members are responsible for which roles. -
Second Annual DOE Joint Genome Institute User Meeting
Second Annual DOE Joint Genome Institute User Meeting Sponsored By U.S. Department of Energy Office of Science March 28–30, 2007 Marriott Hotel Walnut Creek, California Contents Speaker Presentations Abstracts alphabetical by speaker....................................................................................... 1 Poster Presentations Posters alphabetical by first author. *Presenting author. .................................................. 11 Attendees Current as of March 9, 2007 ............................................................................................. 69 Author Index ................................................................................................................... 77 iii iv Speaker Presentations Abstracts alphabetical by speaker. The JGI Aspergillus niger Genome Project Scott E. Baker ([email protected]) Fungal Biotechnology Team, Chemical and Biological Process Development Group, Pacific Northwest National Laboratory, Richland, WA Aspergillus niger is an economically important filamentous ascomycete fungus that is used in industry for its prodigious production of citric acid and a number of enzymes. The DOE Joint Genome Institute has sequenced the genome of A. niger ATCC 1015, a wildtype strain and the source of the first patented microbial fermentation process for citric acid production. Preliminary annotation indicates the presence of over 250 glycosyl hydrolases. These enzymes are crucial for the degradation of lignocellulosic biomass into simple sugars and other chemical building blocks. -
DOE Human Genome Program Contractor-Grantee Workshop VIII
Human Genome Prozram U.S. Department of Energy Office of Biological and Environmental Research SC-72 GTN Germantown, MD 20874-1290 301/903-6488, Fax: 3011903-8521 E-mail: [email protected] A limited number of print copies are available. Contact: Sheryl Martin Human Genome Management Information System Oak Ridge National Laboratory 1060 Commerce Park, MS 6480 Oak Ridge, TN 37830 865/576-6669, Fax: 865/574-9888 E-mail: [email protected] An electronic version of this document will be available on February 27, 2000, at the Human Genome Project Infonnation Web site under Publications (http:llwww.ornl.gov/hgmis). Abstracts for this publication were submitted via the web. DOE/SC-0002 DOE Human Genome Program Contractor-Grantee Workshop VIII February 27-March 2, 2000 Santa Fe, New Mexico Date Published: February 2000 Prepared for the U.S. Department ofEnergy Office of Science Office of Biological and Environmental Research Washington, DC 20874-1290 Prepa.-ed by Human Genome Management Information System Oak Ridge National Labomtory Oak Ridge, 1N 37830 Managed by LOCKHEED MARTIN ENERGY RESEARCH CORP. for the U.S. DEPAR1MENT OF ENERGY UNDER CONI'RACT DE-AC05-960R22464 Contents1 Introduction to Contractor-Grantee Workshop VIII . 1 Sequencing . 3 1. Sequence Analysis of HUman Chromosome 19 Anne Olsen, Paul Predki, Ken Frankel, Laurie Gordon, Astrid Terry, Matt Nolan, Mark Wagner, Amy Brower, Andrea Aerts, Marne! Bondoc, Kristen Kadner, Manesh Shah, Richard Mural, Miriam Land, Denise Schmoyer, Sergey Petrov, Doug Hyatt, Morey Parang, Jay Snoddy, Ed Uberbacher, and the JGI Production Sequencing Team . 3 2. Draft Sequencing Procedures for Chromosome 16 Sequencing Mark 0. -
Computational Genomics and Genetics of Developmental Disorders
Computational genomics and genetics of developmental disorders Hongjian Qi Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2018 © 2018 Hongjian Qi All Rights Reserved ABSTRACT Computational genomics and genetics of developmental disorders Hongjian Qi Computational genomics is at the intersection of computational applied physics, math, statistics, computer science and biology. With the advances in sequencing technology, large amounts of comprehensive genomic data are generated every year. However, the nature of genomic data is messy, complex and unstructured; it becomes extremely challenging to explore, analyze and understand the data based on traditional methods. The needs to develop new quantitative methods to analyze large-scale genomics datasets are urgent. By collecting, processing and organizing clean genomics datasets and using these datasets to extract insights and relevant information, we are able to develop novel methods and strategies to address specific genetics questions using the tools of applied mathematics, statistics, and human genetics. This thesis describes genetic and bioinformatics studies focused on utilizing and developing state-of-the-art computational methods and strategies in order to identify and interpret de novo mutations that are likely causing developmental disorders. We performed whole exome sequencing as well as whole genome sequencing on congenital diaphragmatic hernia parents-child trios and identified a new candidate risk gene MYRF. Additionally, we found male and female patients carry a different burden of likely-gene-disrupting mutations, and isolated and complex patients carry different gene expression levels in early development of diaphragm tissues for likely-gene-disrupting mutations. -
Comparing Genomes: Databases and Computational Tools for Comparative Analysis of Prokaryotic Genomes DOI: 10.3395/Reciis.V1i2.Sup.105En
[www.reciis.cict.fiocruz.br] ISSN 1981-6286 SUPPLEMENT – BIOINFORMATICS AND HEALTH Review Articles Comparing genomes: databases and computational tools for comparative analysis of prokaryotic genomes DOI: 10.3395/reciis.v1i2.Sup.105en Marcos Antonio Basílio Catanho de Miranda Laboratório de Genômica Laboratório de Genômica Funcional e Bioinformática Funcional e Bioinformática do Instituto Oswaldo Cruz do Instituto Oswaldo Cruz da Fundação Oswaldo da Fundação Oswaldo Cruz, Cruz, Rio de Janeiro, Brazil Rio de Janeiro, Brazil [email protected] [email protected] Wim Degrave Laboratório de Genômica Funcional e Bioinformática do Instituto Oswaldo Cruz da Fundação Oswaldo Cruz, Rio de Janeiro, Brazil [email protected] Abstract Since the 1990’s, the complete genetic code of more than 600 living organisms has been deciphered, such as bacte- ria, yeasts, protozoan parasites, invertebrates and vertebrates, including Homo sapiens, and plants. More than 2,000 other genome projects representing medical, commercial, environmental and industrial interests, or comprising mo- del organisms, important for the development of the scientific research, are currently in progress. The achievement of complete genome sequences of numerous species combined with the tremendous progress in computation that occurred in the last few decades allowed the use of new holistic approaches in the study of genome structure, orga- nization and evolution, as well as in the field of gene prediction and functional classification. Numerous public or proprietary databases and computational tools have been created attempting to optimize the access to this information through the web. In this review, we present the main resources available through the web for comparative analysis of prokaryotic genomes.