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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
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    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 2: GHOSTX: A Fast Sequence Homology Search Tool for Functional Annotation of Metagenomic Data
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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8 X users

Citations

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26 Dimensions

Readers on

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16 Mendeley
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Chapter title
GHOSTX: A Fast Sequence Homology Search Tool for Functional Annotation of Metagenomic Data
Chapter number 2
Book title
Protein Function Prediction
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-7015-5_2
Pubmed ID
28451968
Book ISBNs
978-1-4939-7013-1, 978-1-4939-7015-5, 978-1-4939-7013-1, 978-1-4939-7015-5
Authors

Shuji Suzuki, Takashi Ishida, Masahito Ohue, Masanori Kakuta, Yutaka Akiyama

Editors

Daisuke Kihara

Abstract

Metagenomic analysis based on whole genome shotgun sequencing data requires fast protein sequence homology searches for predicting the function of proteins coded on metagenome short reads. However, huge amounts of sequence data cause even general homology search analyses using BLASTX to become difficult in terms of computational cost. GHOSTX is a sequence homology search tool specifically developed for functional annotation of metagenome sequences. The tool is more than 160 times faster than BLASTX and has sufficient search sensitivity for metagenomic analysis. Using this tool, user can perform functional annotation of metagenomic data within a short time and infer metabolic pathways within an environment.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 13%
Student > Bachelor 2 13%
Professor 2 13%
Student > Ph. D. Student 2 13%
Professor > Associate Professor 2 13%
Other 3 19%
Unknown 3 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 31%
Biochemistry, Genetics and Molecular Biology 4 25%
Computer Science 3 19%
Chemistry 1 6%
Unknown 3 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 February 2018.
All research outputs
#6,461,330
of 23,498,099 outputs
Outputs from Methods in molecular biology
#1,945
of 13,368 outputs
Outputs of similar age
#101,004
of 311,670 outputs
Outputs of similar age from Methods in molecular biology
#34
of 271 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 13,368 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,670 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 271 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.

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