↓ Skip to main content

Bioprospecting metagenomes: glycosyl hydrolases for converting biomass

Overview of attention for article published in Biotechnology for Biofuels, May 2009
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
1 tweeter
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
127 Dimensions

Readers on

mendeley
380 Mendeley
citeulike
3 CiteULike
connotea
1 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Bioprospecting metagenomes: glycosyl hydrolases for converting biomass
Published in
Biotechnology for Biofuels, May 2009
DOI 10.1186/1754-6834-2-10
Pubmed ID
Authors

Luen-Luen Li, Sean R McCorkle, Sebastien Monchy, Safiyh Taghavi, Daniel van der Lelie

Abstract

Throughout immeasurable time, microorganisms evolved and accumulated remarkable physiological and functional heterogeneity, and now constitute the major reserve for genetic diversity on earth. Using metagenomics, namely genetic material recovered directly from environmental samples, this biogenetic diversification can be accessed without the need to cultivate cells. Accordingly, microbial communities and their metagenomes, isolated from biotopes with high turnover rates of recalcitrant biomass, such as lignocellulosic plant cell walls, have become a major resource for bioprospecting; furthermore, this material is a major asset in the search for new biocatalytics (enzymes) for various industrial processes, including the production of biofuels from plant feedstocks. However, despite the contributions from metagenomics technologies consequent upon the discovery of novel enzymes, this relatively new enterprise requires major improvements. In this review, we compare function-based metagenome screening and sequence-based metagenome data mining, discussing the advantages and limitations of both methods. We also describe the unusual enzymes discovered via metagenomics approaches, and discuss the future prospects for metagenome technologies.

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 380 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 10 3%
United States 5 1%
United Kingdom 3 <1%
Sweden 3 <1%
Spain 2 <1%
France 2 <1%
Malaysia 2 <1%
Indonesia 2 <1%
Germany 2 <1%
Other 16 4%
Unknown 333 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 101 27%
Researcher 73 19%
Student > Master 49 13%
Student > Bachelor 35 9%
Student > Postgraduate 24 6%
Other 75 20%
Unknown 23 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 208 55%
Biochemistry, Genetics and Molecular Biology 59 16%
Environmental Science 27 7%
Chemistry 14 4%
Immunology and Microbiology 12 3%
Other 23 6%
Unknown 37 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 22 March 2020.
All research outputs
#4,645,041
of 16,717,595 outputs
Outputs from Biotechnology for Biofuels
#345
of 1,209 outputs
Outputs of similar age
#73,904
of 292,289 outputs
Outputs of similar age from Biotechnology for Biofuels
#9
of 36 outputs
Altmetric has tracked 16,717,595 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,209 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 70% 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 292,289 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 73% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.