Article

A protocol for generating a high-quality genome-scale metabolic reconstruction.

Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Nature Protocol (impact factor: 8.36). 01/2010; 5(1):93-121. DOI:10.1038/nprot.2009.203 pp.93-121
Source: PubMed

ABSTRACT Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

0 0
 · 
0 Bookmarks
 · 
70 Views
  • Source
    Article: Database resources of the National Center for Biotechnology Information.
    David L Wheeler, Tanya Barrett, Dennis A Benson, Stephen H Bryant, Kathi Canese, Vyacheslav Chetvernin, Deanna M Church, Michael DiCuccio, Ron Edgar, Scott Federhen, [......], Gregory D Schuler, Edwin Sequeira, Steven T Sherry, Karl Sirotkin, Alexandre Souvorov, Grigory Starchenko, Roman L Tatusov, Tatiana A Tatusova, Lukas Wagner, Eugene Yaschenko
    [show abstract] [hide abstract]
    ABSTRACT: In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link(BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace and Assembly Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Viral Genotyping Tools, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.
    Nucleic Acids Research 02/2007; 35(Database issue):D5-12. · 8.03 Impact Factor
  • Article: Mycobacterial cell wall: structure and role in natural resistance to antibiotics.
    V Jarlier, H Nikaido
    [show abstract] [hide abstract]
    ABSTRACT: Mycobacteria show a high degree of intrinsic resistance to most antibiotics and chemotherapeutic agents. The low permeability of the mycobacterial cell wall, with its unusual structure, is now known to be a major factor in this resistance. Thus hydrophilic agents cross the cell wall slowly because the mycobacterial porin is inefficient in allowing the permeation of solutes and exists in low concentration. Lipophilic agents are presumably slowed down by the lipid bilayer which is of unusually low fluidity and abnormal thickness. Nevertheless, the cell wall barrier alone cannot produce significant levels of drug resistance, which requires synergistic contribution from a second factor, such as the enzymatic inactivation of drugs.
    FEMS Microbiology Letters 11/1994; 123(1-2):11-8. · 2.04 Impact Factor
  • Source
    Article: Predicting subcellular localization of proteins using machine-learned classifiers.
    Z Lu, D Szafron, R Greiner, P Lu, D S Wishart, B Poulin, J Anvik, C Macdonell, R Eisner
    [show abstract] [hide abstract]
    ABSTRACT: MOTIVATION: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. RESULTS: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.
    Bioinformatics 04/2004; 20(4):547-56. · 5.47 Impact Factor

Show more

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

30 organisms
 
abstract pertinent information
 
biochemical transformations
 
Bottom-up metabolic network reconstructions
 
common denominator
 
computational biological studies
 
genome-scale metabolic reconstructions
 
helpful manual
 
high-quality genome-scale metabolic reconstruction
 
knowledge bases
 
last 10 years
 
mathematical format facilitates
 
metabolic engineering
 
network content
 
Network reconstructions
 
phenotypic characteristics
 
predictive potential
 
specific target organisms
 
step necessary
 
systems biology
 

Ines Thiele