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Protein Function Prediction

Overview of attention for book
Cover of 'Protein Function Prediction'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Using PFP and ESG Protein Function Prediction Web Servers
  3. Altmetric Badge
    Chapter 2 GHOSTX: A Fast Sequence Homology Search Tool for Functional Annotation of Metagenomic Data
  4. Altmetric Badge
    Chapter 3 From Gene Annotation to Function Prediction for Metagenomics
  5. Altmetric Badge
    Chapter 4 An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS
  6. Altmetric Badge
    Chapter 5 MPFit: Computational Tool for Predicting Moonlighting Proteins
  7. Altmetric Badge
    Chapter 6 Predicting Secretory Proteins with SignalP
  8. Altmetric Badge
    Chapter 7 The ProFunc Function Prediction Server
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    Chapter 8 G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures
  10. Altmetric Badge
    Chapter 9 Local Alignment of Ligand Binding Sites in Proteins for Polypharmacology and Drug Repositioning
  11. Altmetric Badge
    Chapter 10 WATsite2.0 with PyMOL Plugin: Hydration Site Prediction and Visualization
  12. Altmetric Badge
    Chapter 11 Enzyme Annotation and Metabolic Reconstruction Using KEGG
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    Chapter 12 Ortholog Identification and Comparative Analysis of Microbial Genomes Using MBGD and RECOG
  14. Altmetric Badge
    Chapter 13 Exploring Protein Function Using the Saccharomyces Genome Database
  15. Altmetric Badge
    Chapter 14 Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server
  16. Altmetric Badge
    Chapter 15 The FANTOM5 Computation Ecosystem: Genomic Information Hub for Promoters and Active Enhancers
  17. Altmetric Badge
    Chapter 16 Multi-Algorithm Particle Simulations with Spatiocyte
Attention for Chapter 11: Enzyme Annotation and Metabolic Reconstruction Using KEGG
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Chapter title
Enzyme Annotation and Metabolic Reconstruction Using KEGG
Chapter number 11
Book title
Protein Function Prediction
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-7015-5_11
Pubmed ID
Book ISBNs
978-1-4939-7013-1, 978-1-4939-7015-5
Authors

Minoru Kanehisa

Editors

Daisuke Kihara

Abstract

KEGG is an integrated database resource for linking sequences to biological functions from molecular to higher levels. Knowledge on molecular functions is stored in the KO (KEGG Orthology) database, while cellular- and organism-level functions are represented in the PATHWAY and MODULE databases. Genes in the complete genomes, which are stored in the GENES database, are given KO identifiers by the internal annotation procedure, enabling reconstruction of KEGG pathways and modules for interpretation of higher-level functions. This is possible because all the KEGG pathways and modules are represented as networks of KO nodes. Here we present knowledge-based prediction methods for functional characterization of amino acid sequences using the KEGG resource. Specifically we show how the tools available at the KEGG website including BlastKOALA and KEGG Mapper can be utilized for enzyme annotation and metabolic reconstruction.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 33%
Student > Master 7 17%
Researcher 6 14%
Student > Bachelor 4 10%
Student > Doctoral Student 2 5%
Other 4 10%
Unknown 5 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 40%
Agricultural and Biological Sciences 11 26%
Nursing and Health Professions 1 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Environmental Science 1 2%
Other 3 7%
Unknown 8 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 May 2017.
All research outputs
#10,650,938
of 12,010,397 outputs
Outputs from Methods in molecular biology
#5,589
of 8,286 outputs
Outputs of similar age
#223,796
of 266,429 outputs
Outputs of similar age from Methods in molecular biology
#17
of 36 outputs
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